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DIADEM 5.0 user manual (SATURN version)

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1. Select Algorithm Fixed Step Length Algorithm Convergence Initial Step Length os Maximum Iterations jis aai El o m Absolute Gap jo Relative Gap gt Jor HADES Select Algorithm Social Pressure Algorithm Convergence Initial Step Length oi Maximum Iterations jo Maximum Flow Change 0 001 Relative Gap Jor Sector File Definition Sector File CADIADEM sectors dat 0 Browse Run DIADEM Help Close This page defines settings related to the algorithms used to achieve convergence between supply and demand Depending on the model set up two levels of convergence may be relevant 6 10 1 Main demand supply loop Three algorithms are available from the drop down menu Fixed Step Length FSL this is usually the best performing algorithm achieving the required level of convergence in fewest iterations 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 66 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Method of Successive Averages this is the traditional slow but sure method Research to date shows that it will take a very long time to reach acceptable convergence levels Algorithm 1 this performs like FSL in the early iterations but can then use up a lot of run time without making further progress towards convergence Depending on the algorithm selected a number of parameters also need to be defined in the
2. A separate document is available from the DIADEM website that provides a screen by screen guide to what s new in the user interface for DIADEM 2 1 users Version 2 1 of DIADEM was the first general release in December 2005 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 100
3. DIADEM User Manual Version 5 0 SATURN Mott MacDonald Appendix C Initial tour proportions from NTS data The following tables show the proportion of trips going out and returning in each time period for home based trip purposes by mode The time periods following the standard definitions AM peak 0700 1000 Inter peak IP 1000 1600 PM peak 1600 1900 Off peak OP 1900 0700 Table C 1 Initial tour proportions for home based work D 13 56 3 9 0 10 4 02 21 47 0 00 2 46 0 07 0 04 0 52 1 32 Outbound Outbound Outbound time time time 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 97 56 84 20 93 45 18 7 03 3 01 3 05 58 27 4 59 1 15 1 01 63 55 5 64 3 39 3 30 3 66 15 99 7 09 2 19 1 25 1 21 11 74 Total 66 88 14 21 6 59 12 32 100 00 71 12 13 98 5 61 9 29 100 00 84 23 9 20 2 51 4 06 100 00 DIADEM User Manual Version 5 0 SATURN Table C 2 Initial tour proportions for home based employer s business Outbound Outbound Outbound time time time 14 34 14 89 0 00 0 59 29 82 0 00 16 51 0 00 0 00 0 67 0 51 Table C 3 Initial tour proportions for home based other 5 91 12 16 0 00 28 39 0 00 0 05 0 30 0 29 6 21 40 89 22 05 34 81 0 02 0 10 96 98 15 06 0 00 36 34 0 00
4. Singly and doubly constrained distributions options are available in DIADEM In the singly constrained models the trip ends are fixed for one end of the trip with no constraints on the other end The singly constrained options available in DIADEM are 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 12 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Origin constrained for OD models equivalent to a model of destination choice Destination constrained for OD models equivalent to a model of origin choice most likely to be used for trips travelling to home with the home end fixed Production constrained for PA models The doubly constrained model is available for OD and PA options The total trip ends to and from each zone are fixed In the absolute model there is the option to define mode specific constants for the mode choice model and size variables and K factors for the distribution model Size variables cannot be used for the doubly constrained distribution model The A parameter is defined for the response at the bottom of the hierarchy The parameter is defined for all higher level responses 4 2 HADES 4 2 1 Overview HADES stands for Heterogeneous Arrival and Departure times with Equilibrium Scheduling It is a model of micro arrival time choice intended to deal with short time shifts of the order of about 5 to 20 minutes As such it is distinct from the mac
5. DIADEM User Manual Version 5 0 SATURN February 2011 Reissued September 2012 Department for Transport epartment for Transport ATKINS DIADEM User Manual Version 5 0 SATURN February 2011 Reissued September 2012 Department for Transport From Thursday 13th September 2012 the responsibility for the maintenance support and sale of DIADEM on behalf of the Department for Transport has transferred from Mott MacDonald to Atkins Limited For all enquires please contact Atkins via email on DIADEM atkinsglobal com in the first instance or by telephone on 44 0 1372 756272 Atkins Limited Woodcote Grove Ashley Road Epsom KT18 5BW United Kingdom T 44 0 1372 726140 F 44 0 1372 740055 W www atkinsglobal com DIADEM User Manual Version 5 0 Mott MacDonald Issue and revision record Revision Date Originator Checker Approver Description A March 2010 A Gordon S Sirivadidurage C White First issue version 4 1 manual B May 2010 A Gordon S Sirivadidurage C White Second issue C August 2010 A Gordon S Sirivadidurage C White Third issue D October 2010 A Gordon S Sirivadidurage C White Fourth issue E January 2011 A Gordon C White C White Fifth issue version 5 0 manual F February 2011 A Gordon S Sirivadidurage C White Sixth issue This document is issued for the party which commissioned it We accept no responsibility for the consequences of this and for specific purposes connected with the above captioned document be
6. American Economic Review Papers and Proceedings 82 2 482 486 Small K A 1992b Urban Transportation Economics In Fundamentals of Pure and Applied Economics 51 Harwood Academic Publishers Vickrey W S 1969 Congestion theory and transport investment American Economic Review Papers and Proceedings 59 251 261 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 86 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Appendices Appendix A Description of algorithms 88 Appendix B Demand model functions 90 Appendix C Initial tour proportions from NTS data 97 Appendix D History of DIADEM changes 99 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1453538064 87 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Appendix A Description of algorithms A 1 Definitions and notation DIADEM deals with commodity flows Each combination of origin destination mode time period and demand segment constitutes a unique commodity The following notation is used X a vector of commodity flows with elements X C X a vector of commodity costs with elements C X obtained from the assignment of X V X the value of the objective function for flows X U x the search direction vector for flows X A auxiliary cost estimate vector with elements 2 DMM only UX the upper limit on the flow for commodity s DMM only UC the upper limit on the c
7. D bed C CONTRAM SATURN Segmentation Model Parameters Highway Trip Cost Data PT Trip Cost Data PA Model Data Absolute Model Data DIADEM Parameters HADES Data SATURN Settings Trip Data Distribution Constants Use Initial Guess Origin Production size variables for destination constrained Initial Guess Root Directory Browse Destination Attraction size variables for origin constrained Hw Initial Guess Browse K Factors PT Initial Guess Browse Use Trip Ends Mode Constants Origin Production Browse Browse Destination Attraction Browse Run DIADEM Help Close This page is used to define various file names containing data for the absolute demand model The data falls into two categories trip data and model constants mode constants and size variables Trip data may be defined in one of two ways as trip ends or as a full initial guess matrix 6 9 1 Initial guess Defining the initial guess requires full trip matrices by demand segment and mode These are specified ina similar way to the reference trip matrices used for the incremental model i e OD matrices by time period for OD based models and 24 hour PA matrices for PA based models The initial guess is used as the matrix to be assigned on the first iteration by DIADEM It is also used to calculate the trip end constraints to be applied during the model Definition of the
8. Earlier work on HADES involved a number of developments of the original EST Bates 2007 and Mott MacDonald 2005a 2005c One of these was to extend the concept of the PAT to a PAT window The traveller is assumed to be indifferent to arrival at any time within this window and only incurs scheduling costs if they arrive before or after this 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 13 DIADEM User Manual Version 5 0 SATURN Mott MacDonald EST can be illustrated with reference to the following diagram which shows the costs for someone with a PAT window between 0845 and 0900 The scheduling cost in generalised minutes associated with early or late arrival is shown by the solid blue line The travel duration is shown by the dotted red line and the total travel cost is shown by the dashed green line In this example the PAT window coincides with the peak in travel durations When the total cost including the scheduling cost is considered the optimum arrival time is actually at 0835 in other words the traveller is willing to accept some scheduling cost in return for reduced travel duration Figure 4 4 Costs associated with preferred arrival time between 0845 and 0900 80 Duration oe Schedule cost Total cost 70 60 50 40 Cost mins 30 20 Preferred arrival time m 08 00 08 15 08 30 08 45 09 00 09 15 09 30 Arrival Time
9. DS button to copy the settings from this demand segment to the next This can be useful when setting up the data for the first time as there will often be only small differences in the parameters for different demand segments 6 5 3 Parameters Model parameters must be defined for each of the selected responses the parameter boxes for responses not selected will be greyed out For the logit based models the spread or dispersion parameter A must be defined for the choice at the bottom of the hierarchy A must be negative 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 47 DIADEM User Manual Version 5 0 SATURN Mott MacDonald For choices above the bottom the scaling parameter 8 must be defined For each level this determines the sensitivity of the response relative to the level below 8 must be greater than zero and less than or equal to 1 It will be clear from which boxes are greyed out for each response whether 8 or A needs to be defined See Section 5 1 for an explanation of 8 and Illustrative parameter values can be found in Section 1 11 of WebTAG Unit 3 10 3 Variable Demand Modelling Key Processes or they can be calibrated locally if suitable data are available In general the appropriate value of A depends on the units used for generalised cost DIADEM always uses generalised minutes this is consistent with the illustrative parameters in WebTAG In accordance with WebTAG
10. advice when mode choice appears above distribution in the hierarchy it is possible to define separate distribution parameters for car and PT modes If mode choice is below distribution then only a single parameter can be defined The elasticity model uses the Tanner function see Appendix B 3 so two parameters are needed although often one of them will be zero The first parameter A corresponds to the exponential part of the function and the second parameter y to the power function Thus to implement a power elasticity function set A to zero and to implement an exponential elasticity function set y to zero Non zero parameters must be negative Note that A corresponds to the B parameter in the WebTAG notation and y corresponds to the A parameter Parameters relating to the use of the HADES model if selected are defined separately on the HADES Data page 6 5 4 Advanced distribution options Advanced Distribution x Cost Intra zonal Costs Calculate Intra Zonal Costs Iv p 0 5 Minimum Cost 5 Cancel Help Spatial Segmentation Use Spatial Segmentation D Distribution Parameters Browse There are two features available under the advanced distribution options calculating intra zonal costs and Spatial segmentation Assignment models have trouble producing costs for intra zonal trips and will typically return a value of zero Even when a non zero value is returned it may not be reliabl
11. al 2007 For home based trips a single tour consists of all the trios made between leaving home and returning For example home to work to shops to home is a single tour 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 29 DIADEM User Manual Version 5 0 SATURN Mott MacDonald DIADEM makes the simplifying assumption that all tours are so called simple tours i e they go from home to a single destination and then back home again Diversions from this can be modelled as non home based trips using OD based demand modelling The distinction between PA modelling and simple tours is not clear cut As far as DIADEM is concerned the key difference is that with tour modelling we explicitly consider and link together the time periods for outbound and return travel The 24 hour PA matrix is broken down by outbound and return travel times If there are for example four time periods AM and PM peaks inter peak and off peak then the PA matrix is broken down into 16 4 x 4 combinations of outbound and return times To break down a 24 hour PA matrix to this level of detail requires information on what proportion of trips in the 24 hour matrix go out and return in each combination of time periods This data is typically available from household travel diaries but usually not from roadside interviews A new method has been developed for DIADEM which makes use of national data combined with local data to min
12. and decrease at the end as would be expected if the whole of the peak period were modelled 7 2 7 3 Possible FIFO violation FIFO stands for First In First Out meaning that if for a particular OD pair and user class vehicle X departs before vehicle Y then it should also arrive before vehicle Y This message will be produced if this condition is not met For example the travel duration for a vehicle departing at 0800 might be 20 minutes implying arrival at 0820 but a later departure at 0805 might have a 10 minute travel duration implying an earlier arrival at 0815 This should never happen in a CONTRAM model In SATURN it can usually be fixed by using the PASSQ option on the DIADEM Segmentation page If this doesn t work then please contact DIADEM technical support for further advice 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 80 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 8 Hints and tips 8 1 Realism testing with DIADEM 8 1 1 Overview Realism testing is an important part of making sure the demand model is compliant with WebTAG It is essential when importing model parameters e g using the WebTAG illustrative parameters rather than calibrating them locally The relevant part of WebTAG is Section 1 6 of Unit 3 10 4 Variable Demand Modelling Convergence Realism and Sensitivity 8 1 2 Fuel cost elasticity The main requirement is to test the outturn elasticity o
13. are using SATURN version 10 6 17 or later you can make use of the warm start facility This means that the SATURN assignment can make use of the route and cost information from the previous DIADEM iteration reducing the time required to get a converged assignment To do this make the following modifications to your network dat files Set UPDATE to T under OPTION Set WSTART to T under OPTION Set SAVUFO to T under PARAM The last two options are likely to be most successful if you are using the origin based assignment OBA option but are sometimes worth trying in other cases When you run DIADEM you may see a SATURN dialogue box asking you for a UFS file to update from You can select your base year UFS file This will only happen on the first DIADEM iteration Subsequently the warm start and update will be based on the results of the previous DIADEM iteration 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 83 DIADEM User Manual Version 5 0 SATURN Mott MacDonald It should be noted that in some cases the extra time taken for SATURN to produce the UFO file might offset other time savings particularly for a high value of NITA_S so significant savings cannot be guaranteed For more information on warm starts see Section 22 of the SATURN manual The above information is based on the manual for SATURN version 10 7 10 and may not be appropriate for later versions To minim
14. calculated using 0 AU In B a exp AU b 0 a 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 91 DIADEM User Manual Version 5 0 SATURN Mott MacDonald B 2 The hierarchical model The above multinomial logit model is easily extended to a hierarchical formulation as follows The probabilities calculated at each level are conditional on the choices made at the level s above except for the top level of the hierarchy The change in costs used at each level should be the change in composite costs from the level immediately below except for the bottom level of the hierarchy For a full explanation of the incremental hierarchical logit model see Bates et al 1987 B 2 1 Frequency model The incremental frequency model used is T T explo regAU When origin or doubly constrained distribution is used this formula modifies the origin trip end totals in response to changes in origin composite costs For destination constrained distribution it modifies the destination trip end totals B 2 2 Elasticity model The elasticity model uses a Tanner formulation Y X X expC E Cc B 2 3 Cs where X is flow forecast or reference C is generalised cost forecast or reference A and y are user defined parameters S indicates commodity a particular combination of origin destination time period mode and demand segment 0 indicates reference cost or flow The refere
15. connectors being defined in such as way as to allow OD routes to take place solely on connectors and to have no time or distance 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 78 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 7 2 3 MatrixFile read Cannot open file name for input error This usually occurs very shortly after clicking the Run DIADEM button It is because a path has not been specified correctly a specified file does not exist or a particular file has not been specified at all It can also occur later in the DIADEM run as a result of a failure to skim costs from an assignment Look in the SATLOOK lpl files for clues or SATALL Ipt files if the assignment itself failed Also see Section 8 2 5 for likely causes of this in SATURN 7 2 4 Warning N origin destination demand segment time period combinations have highway trips but no PT trips warning This message will appear if there are zero PT trips but non zero highway trips or vice versa in the reference matrices for a particular origin destination demand segment time period combination In this situation the mode choice model can still be applied but it will not model any change in mode share for that particular combination i e it is fixed at 100 for one mode 7 2 5 Spawn of SATURN process returned error code XXXX error lf this occurs shortly after clicking Run DIADEM and there is no evide
16. contained is now in the reference tour matrices The application of time period choice in the incremental model may subsequently change the number of trips in each outbound return time period combination 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 30 DIADEM User Manual Version 5 0 SATURN Mott MacDonald In the absolute version no reference matrix is input just the trip ends The tour proportions are therefore used to segment the trip ends by each outbound return time period combination Note that for the doubly constrained model the constraints are always applied summed over all time periods and modes 4 5 Public transport in DIADEM DIADEM has a simplified representation of public transport PT Mode choice in DIADEM is a simple two way choice between car and PT If a more detailed representation of mode choice is required this will need to be represented outside DIADEM Examples might include modelling the choice between bus and rail or looking at park and ride submodes In many cases it will be possible to use the public transport assignment model for these choices and to use DIADEM for main mode choice and other demand responses please contact DIADEM support for advice A second simplification is that DIADEM assumes that PT costs are not demand responsive DIADEM will carry out a highway assignment every time it adjusts demand to see how costs are affected but assumes PT costs remain u
17. generalised costs remain unchanged When using TEMPRO to derive reference matrices for input to DIADEM the TEMPRO fuel and income factors should not be used Instead the effect of changing fuel costs and incomes should be represented by changing the generalised cost coefficients See Section 6 5 5 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 11 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Figure 4 3 Pivoting off the do minimum Spm Cost SDs eE e a E E A E E i RM DS D Forecast Year Trips Figure 4 3 illustrates an alternative pivoting approach that can be used when modelling the do something DS scenario provided the do minimum DM has already been modelled From the DIADEM results for the DM we have the DM equilibrium point E This gives us the DM costs and trips which we then use unmodified as the reference case for the DS We can do this because the demand curve does not change between the DM and DS hence the DM equilibrium is a point on the DS demand curve and can be used as the reference case WebTAG recommends the use of incremental models whenever possible 4 1 6 Implementation in DIADEM Absolute and incremental hierarchical logit models are both available in DIADEM for destination choice distribution and mode choice The incremental model can also be used for modelling time period choice The three responses can be placed in any order in the hierarchy
18. help and manual contain mostly the same information though the former does not include the appendices that are part of the manual The DIADEM website http Awww dft gov uk diadem includes an FAQ list which covers a number of issues relating to running DIADEM If you have any questions problems or suggestions for improvements please contact the DIADEM help desk at Mott MacDonald email diadem atkinsglobal com DIADEM is developed on behalf of ITEA division of the Department for Transport E itea dft gsi gov uk Please note that details of support queries will be passed to the Department for their information 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Guidance on all aspects of modelling including assignment and variable demand modelling can be found on WebTAG hittp www dft gov uk webtag Questions on the application and interpretation of WebTAG should be directed towards DfT 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 4 Background information 4 1 Logit models 4 1 1 Absolute logit The logit model is at the heart of the main DIADEM demand model It is one of a family of models referred to as discrete choice models Such models predict the probability that an individual will pick a particular alternative when faced with a choice b
19. is applied to produce demand by time slice ready for assignment For non HADES demand segments and time periods fixed profiles are applied to produce demand by time slice 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 23 DIADEM User Manual Version 5 0 SATURN Mott MacDonald The demand is then assigned and the costs skimmed For HADES time periods there is an iterative loop between the assignment and HADES which is run to convergence as described in section 4 2 3 This is done independently for each time period to which HADES is applied Following the convergence of this loop costs by time period need to be calculated for passing to the demand model This is a three stage process as follows For each OD pair and demand segment the actual arrival time his checked to see if it falls within the time period Based on the actual arrival time and the travel duration the clock time of the midpoint of the trip is calculated to see if it is within the time period If it does the related data is used in the following stages If it doesn t the data is discarded For example the time period might be 0700 1000 If the actual arrival time is 1010 and the travel duration is 30 minutes then the midpoint occurs at 0955 and fits in the time period If the actual arrival time is 1010 and the travel duration is 10 minutes then the midpoint occurs at 1005 and is outside the time period For each preferred arriva
20. looking out for any rows or columns with just one or two non zero entries The solution is to fill in as many of the gaps in the reference matrix as possible Simply seeding the matrix by replacing zeroes with some fixed value is unlikely to be acceptable as it will distort the distribution of trips in the matrix 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 19 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 7 2 7 HADES specific messages 7 2 7 1 Time period boundary outside defined range for linear interpolation This message indicates that the period covered by the arrival time windows is too short relative to the period covered by the assignment model The message will state which origin destination and demand segment are affected It will also report a range of departure times or trip mid points corresponding to the departure time of a trip arriving at the start of the first arrival time window to the departure time of a trip arriving at the end of the last arrival time window The start and end of every assignment time slice must be within this range If not the interpolation method described in Section 4 2 3 5 cannot be used and demand within the assignment time slice will be under represented The solution is to extend the period covered by the arrival time windows or reduce the period covered by the assignment time slices As discussed in Section 4 2 3 5 the arrival time win
21. matrix there must be a corresponding cost in the reference cost file This may not be the case if for instance the reference trip matrix contains OD trips that do not exist in the base matrix Therefore zoning systems in the base and forecast networks must be the same even if some zones do not have any trips in the base matrix 6 6 1 2 Pivoting off the DM Pivoting off the DM requires that DIADEM has already been run for one scenario for the year under consideration Typically the DM would have been pivoted off the base year see above When running DIADEM for the do something DS in the same year it is possible to pivot off the DM rather than the base year In this case the reference demand should be the trip matrix output by the DM DIADEM run and the reference costs from the assignment of that matrix to the corresponding DM network The advantage of this approach is that DIADEM should be starting at a point closer to the DS equilibrium and thus converge more quickly 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 54 DIADEM User Manual Version 5 0 SATURN Mott MacDonald WebTAG recommends pivoting off the DM when modelling the DS 6 6 1 3 Pivoting summary The following table summarises how the reference data can be obtained according to whether the base year or DM is used as the pivot point Reference costs Forecast cost Reference demand ici coefficients Base year Base year costs Base ye
22. on either trip departure times or trip mid point times The choice is made by selecting the appropriate radio button For CONTRAM it should always be departure times for SATURN it is up to the modeller depending on what was assumed when building the model 6 11 4 Input files containing HADES parameters Most HADES related data is defined via four user defined input files These files are in comma separated variable csv format and contain the following data Definitions of arrival time windows Scheduling parameters by destination sector and demand segment The profile of demand by preferred arrival time window by origin destination and demand segment Pre and post peak travel durations by assignment user class 6 11 4 1 Arrival time window definitions This file contains the definition of the arrival time windows The time period being modelled should be divided into a series of contiguous arrival time windows in a similar way to the division of the modelled period into a series of assignment time slices However as explained below the period covered by the arrival time windows will usually need to be longer than that covered by the assignment Every trip in the model is allocated to one of these windows as its preferred arrival time PAT Several sets of windows may be specified Different demand segment destination sector time period combinations may each use a different set It is good practice for a single set to apply only to a
23. page when setting up the DIADEM run 7 1 5 Trip matrices 7 1 5 1 Highway All intermediate OD trip matrices produced by DIADEM are saved with a file name of the form root _HWirips_ TX _DSY_M_N dat where X is the time period Y is the demand segment M is the main iteration number and N is the subiteration number The matrices from the best iteration are copied to root _HWitrips_TX_DSY_best dat The units used are vehicle or PCU trips per hour whichever was used for the input matrices For demand segments that use a PA demand model PA trips are output for each iteration in the form of 24 hour PA and PA tour matrices A 24 hour PA matrix is produced with a file name of the form root_HWirips DSY_M_N dat Tour matrices are produced with file names of the form root_HWirips_ToutX_TretZ_DSY_M_N dat where X is the outbound time period Z the return time period The units used are absolute vehicle or PCU numbers Note that the 24 hour PA matrix is just the sum of the corresponding tour matrices over all outbound and return time periods The UFM matrices from the last subiteration as used in the final assignment are called UFM_TX UFM all assignment user classes stacked together 7 1 5 2 Public transport PT trip matrices are output only for those demand segments for which mode choice is modelled OD trip matrices are called root PTtrips TX _DSY_M_N dat where X is the time period Y is the demand segment M is the main iteration number and N is
24. reference costs are used to produce an initial set of trip matrices to be assigned They then play no further role in the model For the absolute model the reference costs should represent the best guess of what the costs will be in the scenario being forecast The more accurate the guess the less time DIADEM will take to converge There are two main ways of setting up reference data for an incremental model pivoting off the base year or pivoting off the do minimum Reference trios should be defined by time period reference costs by time slice Trips by time period are converted to trips by time slice in one of two ways For demand segments and time periods which use the HADES model the conversion is carried out as part of the HADES process For all other demand segments and time periods the conversion is carried out using fixed demand profiles as defined on the bottom of this page 6 6 1 1 Pivoting off the base year When pivoting off the base year the reference costs should come from the validated base year assignment The reference demand should represent the unrestrained forecast of travel demand in the forecast year being modelled in DIADEM i e assuming no change in costs In the UK this would typically be obtained by taking the base year validated matrix and increasing it with factors obtained from TEMPRO for cars and PT passenger travel and other DfT forecasts for goods vehicles For every non zero cell in the reference trip
25. that the costs and the changes in costs are not usually fully modelled for such trips In DIADEM the decision to use variable demand modelling is made at the trip purpose level So this requires setting up additional trip purposes and demand segments for the fixed trips For example suppose a subset of movements for employer s business trips is to be frozen This suggests the following set up of trip purposes in DIADEM Home based employers business VDM modelled as variable demand PA Non home based employers business VDM modelled as variable demand OD Employers business fixed modelled as fixed demand All three purposes can be combined into a single assignment user class The reference matrices in an incremental model or trip ends for an absolute model for the variable demand segments should exclude those trips which are being treated as fixed demand 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 85 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 9 References Arnott R de Palma A Lindsey R 1994 Welfare effects of congestion tolls with heterogeneous commuters Journal of Transport Economics and Policy Bates J Ashley D and Hyman G 1987 The nested incremental logit model theory and application to model choice Proceedings 15 PTRC Summer Annual Meeting University of Bath England Bates J 2007 Practical modelling of trip re scheduling under conges
26. the hierarchical logit model is also available A more detailed discussion of the logit model and its application in DIADEM can be found in Section 4 1 A simple elasticity model using the Tanner function is available Power and exponential elasticity functions are special cases of the Tanner function It should be noted that WebTAG currenily states that Pending further research it is recommended that simple elasticity models are not used to model the full effects of variable demand although consultation guidance on proportionate appraisal proposes allowing their use on schemes with capital costs less than 20m October 2009 consultation version of Unit 3 10 1 If you are using SATURN then it is usually more efficient to implement elasticity models using the options available within SATURN rather than using DIADEM The functional form of the elasticity model is given in Appendix B DIADEM 5 0 includes the HADES model of arrival time choice This part of DIADEM currently has beta test status It is described in more detail in Section 4 2 For further discussion of these models and how to decide which is appropriate for your application please see WebTAG Unit 3 10 3 Variable Demand Modelling Key Processes 3 3 Help and support DIADEM has a context sensitive help system Pressing F1 or clicking the Help button will bring up help information relevant to the page you are working on The help file can also be accessed via the Help menu The
27. the subiteration number The matrices from the best Iteration are copied to root PTtrios_ TX _DSY_best dat The units used are passenger trips per hour For demand segments that use a PA demand model PA trips are output for each iteration in the form of 24 hour PA and PA tour matrices A 24 hour PA matrix is produced with a file name of the form root PTtrips DSY _M_N dat Tour matrices are produced with file names of the form root PTtrips ToutX_TretZ_DSY_M_N dat where X is the outbound time period Z the return time period The units used are absolute passenger numbers 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 11 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 7 1 6 Other files In addition to the files described above other files may be produced by the assignment software as follows Skim cost matrices from the final assignment tim_TX_UCY dat dis TX _UCY dat cst_TX_UCY dat UFN UFC UFS LPN and LPT files are created during the assignment carried out using the SATURN module The LPT file may provide useful information if DIADEM stops due to a failed SATURN assignment LOG LPX UFM files are created by the MX module which is used to build UFM files from ASCII files output by DIADEM LOG LPL DAT files cst dat tim dat dis dat are created by SATLOOK which is used to skim cost matrices the DAT files in ASCII format from completed assignments Note that very large num
28. trip matrices Results to look at include Total trip numbers are reported in the convergence file these will only change if the trip frequency response is modelled or an elasticity model is used Total trio numbers by mode and time period can be calculated from the output trip matrices These can be used to check the mode and time period choice responses are reasonable The trip matrices can be used in a trip length distribution analysis 7 1 2 Log file This file is called root log This file contains a log of the timing of each step of the DIADEM process It also includes any warnings that are produced Note that these warnings are not serious enough to cause the DIADEM run to fail but should be investigated further see Section 7 2 for more on warning messages Note that this file is appended to rather than replaced i e if the same DIADEM control file is run twice then the log will contain output relating to both runs running it a second time does not delete the output from the first 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 19 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 7 1 3 Convergence files 7 1 3 1 Main demand assignment loop A summary convergence file in comma separated variable format extension _results csv is produced The root of the file name will be the same as the DIADEM control file This file contains the following information for each subiterati
29. want to set up If you are doing mode choice modelling or are modelling non car available demand segments you will also need the following public transport PT data Forecast year reference trip matrices Forecast year scenario travel times and optionally fares Reference case travel times and optionally fares The following is a brief summary of the steps required in setting up and carrying out a run of DIADEM Further explanation of the steps involved and terminology is given elsewhere in the manual and in WebTAG 1 Consult WebTAG and decide on the appropriate segmentation and demand model structure and parameters 2 Prepare forecast network data file s for the scenario and year you are planning to model 3 Prepare the reference trip matrix matrices a If pivoting off the base year these will be unrestrained growth forecasts from e g applying TEMPRO to the validated base year matrix b If running the DS and pivoting off the DM these will be the forecast trip matrices output by the DIADEM DM run 4 Locate previous assignments from which reference costs can be obtained a If pivoting off the base year these will be the base year assignment b If running the DS and pivoting off the DM these will be the costs from the assignment of the forecast DM matrix produced by DIADEM 5 If using a mode split model or modelling non car available person types then carry out PT assignments for the scenario and forecast year
30. which contains zone Allocate demand to arrival time windows Once the utilities Vix have been calculated the demand Tjeck is allocated on an all or nothing basis to arrive in the arrival time window h k with the lowest generalised cost This gives the auxiliary demand Y Yijekh Tijek sicker 4 3 where ok h p5 1 if h h i e h is the arrival time window with the lowest cost for this ijek 0 ick n p 0 otherwise Y is subsequently combined with the current best estimate of trips as described below Convergence algorithm Suppose X ick is the current best estimate of the number of trips from to in class c with a preferred arrival time of k that choose arrival time window N is the iteration number and refers to the number of HADES assignment model loops that have been carried out 2 This is slightly simpler than equation 4 1 In DIADEM the cost is expressed in generalised minutes In DIADEM HADES travel durations are also in minutes therefore we can set a 7 and define the scheduling parameters 6 and y as the scheduling costs per minute of early or late arrival relative to one minute of travel duration 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 19 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Following the assignment of X the skimming of costs and completion of all the steps already described above we end up with the auxiliary solution The two are
31. with absolute demand models reference costs must be available for every zone pair 6 6 3 Entering data Highway reference trip data must be defined for each car available demand segment and time period for which an incremental model is used Potentially there are a large number of combinations of demand segments and time periods so DIADEM uses a file naming convention to reduce the data entry requirements on this screen Reference trip data file names for OD based modelling have the format 2 TX DSY dat for time period X and demand segment Y is any user defined string Xand Y are integers 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 59 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Note that the above data is defined by time period then split by time slice using either HADES if selected or user defined profiles otherwise For PA based modelling the reference trips should be in the form of a 24 hour PA matrix therefore the file name excludes any reference to the time period DSYdat Note that in the PA reference matrix a value of 1 gives two OD trips one from the production zone to the attraction zone and another in the reverse direction Note that Y refers to the number of the demand segment as used in the drop down box on the Model Parameters page The numbering of demand segments is determined by the order in which they are added to the Segmentation page The demand s
32. with respect to journey time can then be estimated as Ftime cfuel al bK This is only a crude estimate If the result is close to the WebTAG recommended limit of 2 0 then please contact DIADEM support for advice on obtaining a more accurate value 8 2 Reducing run times The vast majority of the total DIADEM run time is spent running assignments and skimming costs To reduce run times you therefore need to reduce the time spent on these tasks and minimise the number of iterations that DIADEM does Skimming costs as an average over all used paths in SATURN takes much more time than skimming minimum cost paths As noted in Section 6 6 it is acceptable to skim minimum cost paths provided that the assignment is well converged Assignment run times in SATURN can often be reduced significantly by adjusting the convergence parameters Setting SATURN parameters similar to the following values is often successful in minimising run times NITA 10 MASL 100 AUTOK T KOMBI 0O NITA M 3 DIDDLE T AUTONA T If you are skimming minimum cost paths you can also set SAVEIT F but when final assignments are being run to provide inputs to TUBA it will need to be set to T and NITA_S will need to be sufficiently high to make the SAVEIT assignment as accurate as possible for more information see Section 15 23 of the SATURN manual Of course each network is different and some experimentation with the above parameters may be necessary If you
33. you are modelling and skim forecast costs fare and generalised time Repeat for the scenario from which reference costs will be obtained base year if pivoting off 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 35 DIADEM User Manual Version 5 0 SATURN Mott MacDonald base DM if pivoting off DM 6 Using the DIADEM interface set up the DIADEM data using the segmentation and demand model structure and parameters that you decided upon at step 1 and using the reference trip and cost matrices and PT forecast cost matrices if applicable you obtained above 7 Save the DIADEM control file 8 Click the Run DIADEM button 9 When the DIADEM run has completed check the output csv file to ensure that convergence Is acceptable Use matrices with best appended to the file name for all further analysis Check the log file for warning messages 5 3 Absolute model Before running DIADEM you will need at least the following Forecast year trip ends absolute model or an initial guess of the full trip matrix for the year you are modelling Forecast year scenario e g 2016 Do Minimum network data files Details of the demand model structure you want to set up If you are doing mode choice modelling or are modelling non car available demand segments you will also need the following public transport PT data Forecast year scenario travel times and fares Forecast year reference tri
34. 0 05 0 20 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 98 31 42 91 65 29 18 13 47 4 09 0 72 43 46 23 22 14 54 4 28 0 78 42 82 25 31 14 93 3 13 0 62 43 99 1 67 11 90 3 61 0 03 23 21 5 28 12 97 6 02 0 07 24 34 5 52 3 47 6 05 7 438 22 47 5 54 3 85 4 63 6 42 20 44 0 22 1 53 2 48 6 18 10 41 0 17 1 89 4 24 11 07 17 37 Mott MacDonald Total 48 51 31 83 10 14 9 52 100 00 44 74 34 37 10 61 10 28 100 00 48 73 35 29 7 76 8 22 100 00 21 88 40 30 17 69 20 13 100 00 39 24 48 24 6 11 6 41 100 00 26 96 51 20 10 31 11 53 100 00 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Appendix D History of DIADEM changes The following sections summarise the main functional changes between different versions of DIADEM More details can be found in the release notes on the DIADEM website D 1 Changes between 4 1 and 5 0 HADES model of arrival time choice made available as a demand response for trip purposes that use absolute demand modelling PA trip matrices are now output on each iteration D 2 Changes between 3 1 and 4 1 Fundamental change to the way generalised cost coefficients are defined Cost damping option introduced D 3 Changes between 3 0 and 3 1 DIADEM 3 1 was released in June 2009
35. 1 453538064 DIADEM User Manual Version 5 0 SATURN Mott MacDonald When viewed graphically logit models follow a typical S shaped curve This can be seen in Figure 4 1 which shows the curves for different values of A illustrating how it changes the sensitivity of mode shares to costs Figure 4 1 Typical logit curves 100 90 80 70 60 50 40 Car mode share 30 20 10 0 80 60 40 20 0 20 40 60 80 Bus cost minus car cost In transport modelling logit models have a multitude of uses In DIADEM the absolute model can be used to model choices of mode and destination destination choice models are also referred to as trip distribution models 4 1 2 Mode specific constants size variables and K factors When using an absolute logit model for mode choice it is usually found that generalised cost alone is not enough to explain the observed mode choice For example the logit model would predict that if the generalised costs are the same then the mode share would be 50 50 between car and PT whereas the corresponding observed mode share might be closer to 60 40 This is usually handled by adding a constant to the generalised cost for one of the modes This represents the remaining preference for one particular mode after differences in generalised costs have been taken into account These constants are called mode specific constants Mode specific constants can b
36. 20 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Conversion of demand by arrival time window to demand by assignment time slice Having calculated the demand X by arrival time window it then needs to be converted into demand by assignment time slice At this stage we are only interested in the actual arrival time window h and not the preferred arrival time window k so we can sum the arrival time matrix over k as follows N 1 N 1 jich iickh 4 7 k For tidiness we now drop the ijc subscripts but note that the following steps need to be carried out for each ijc combination First calculate the cumulative arrival time distribution Q i e the total demand arriving before the end of each arrival time window Tend H Q rend gt Xp 4 8 h 1 In addition we can also calculate the cumulative demand at the start of the first arrival time window Q rstart Using the interpolation method set out earlier we then estimate travel durations for arrival at the end of each arrival time window and the beginning of the first one to obtain rend and start For departure time based assignment we can then calculate the cumulative distribution of demand by departure time Q at the corresponding departure times Q rend rend Q rend 4 9 Q rstart rstart Q rstart 4 9 Or for assignments based on trip mid points q rend skrendu O rend 4 10 q rstart sesan Q rs
37. 830 0830 0840 and 0840 0900 1 0800 0820 0830 0840 0900 Arrival time windows within a given set may be of different lengths The different sets are in effect independent Each set may start and finish at different times and the durations of the windows differ between sets However the appropriate start and end times of the set will need to be set appropriately for the time periods being modelled as discussed below The period covered by the arrival time windows clearly needs to be related to that covered by the assignment For example if the arrival time windows go from 0700 1000 and the assignment covers 1600 1700 then no flow will get assigned The arrival time windows should start no later than the arrival time of a trip departing at the beginning of the assignment period This is most easily handled by making sure that the arrival time windows and the assignments start at the same time At the other end the arrival time windows should finish no earlier than the arrival time of a trip departing at the end of the assignment period The end of the last arrival window should ideally be no earlier than the end of the period covered by the assignment plus the duration of the longest trip in the network If these conditions are not met then it is likely that the assignment will be missing trips i e in reality the assignment period includes trips which arrive outside of the period defined by the arrival time windows but because these a
38. Algorithm frame It is recommended that the default values are used to start with The step length for FSL can be tweaked to improve run times and convergence as explained in Section 8 3 For an explanation of the role of these parameters please see Appendix A The Convergence frame is used to specify the stopping criteria for DIADEM Maximum Iterations for MSA and FSL there is one assignment per iteration For Algorithm 1 there may be more than one particularly as equilibrium is approached Maximum Flow Change close to convergence DIADEM may make very small changes in the demand matrix which are virtually imperceptible to the assignment When the largest change in cell values is less than the value of this criterion DIADEM will stop Absolute Gap DIADEM will stop when the absolute gap falls below this value The value achievable is very dependent on the size of the problem being solved so it is usually advisable to set this to zero Relative Gap DIADEM will stop when the relative gap falls below this value See Section 1 5 of WebTAG Unit 3 10 4 Variable Demand Modelling Convergence Realism and Sensitivity for advice about convergence in variable demand models for the formula used to calculate the gap and for the appropriate value for this parameter 6 10 2 HADES assignment loop This section will be greyed out unless HADES has been selected for at least one demand segment The FSL and MSA algorithms are also available fo
39. CADIADEM 2011DMAMPTTimes dat Run DIADEM Help Close This page is only used if mode choice or non car available demand segments are being modelled DIADEM assumes that PT forecast costs times and fares do not change in response to changes in PT or highway demand unlike highway costs PT forecast costs are therefore fixed for the duration of the DIADEM process whereas DIADEM automatically skims forecast highway costs from the latest assignment PT reference and forecast costs which may be different must be input by the user Reference trips and reference and forecast times must be defined for all car available demand segments for which mode choice is being modelled and for all non car available demand segments for all time periods Definition of reference and forecast fares is optional However it is recommended that they are always defined unless the reference and forecast fare cost coefficients are the same and the reference and forecast fares themselves are the same All fares must be entered in the same price base consistent with that used elsewhere e g values of time even if they relate to different modelled years 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 59 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Please see Section 7 6 1 for an explanation of reference costs and demand The forecast times and fares are the times and fares for the scenario being modell
40. D into the drive If this does not happen or you have received the software through a different medium you will need to run the program called setup exe To complete the installation you will need to accept the terms of the licence agreement The following files will be installed to the DIADEM program files directory DIADEM software DIADEM help file Licence agreement licence txt CONTRAM and SATURN versions of the manual DIADEM works with SATURN version 10 8 21 and later and CONTRAMS8 The system requirements are Windows XP or Vista Minimum 1024x768 screen resolution We are not aware of any problems using DIADEM with Windows 7 though this has not yet been fully tested The amount of memory RAM and disk space required depend on the size of the network being used with DIADEM and the level of segmentation Typically the most significant performance improvements are obtained by ensuring that enough RAM is available for DIADEM not to need to use virtual memory 256MB is the recommended minimum but 1GB or more may be required for large models If there is insufficient RAM DIADEM will use virtual memory instead but this will significantly increase run times 7MB of disk space is required for the DIADEM software itself In addition sufficient space must be available to store DIADEM outputs as follows For CONTRAM DIADEM creates one trip matrix per iteration For SATURN DIADEM creates one matrix per iteration for each of
41. DIADEM User Manual Version 5 0 SATURN Mott MacDonald Appendix B Demand model functions B 1 Logit model B 1 1 Incremental multinomial logit Three types of choice model can use the incremental logit formulation in DIADEM distribution mode and time period choice They can be joined together in a hierarchy in any order The standard incremental multinomial logit model is given by p explBAU i5 0 B 1 gt Pj exp AU j where P is the forecast probability of choosing alternative p is the reference case probability of choosing alternative calculated from the input reference demand 0 is the scaling parameter always 1 for the bottom level of the hierarchy AU isthe change in the utility of alternative For the choice at the bottom level of the hierarchy the change in utility is given by AU NC C where C is the reference generalised cost and C is the forecast generalised cost skimmed from the latest assignment is the spread or dispersion parameter defined by the user it should be negative For choices above the bottom level of the hierarchy the change in utility is the composite change over the alternatives in the level below AU In gt p exp AU i This model formulation is used for mode choice time period choice and singly constrained distribution In the latter for OD based demand modelling origin constrained distribution is treated as a model of destination choice and destinat
42. Future year PPM and PPK values will need to be adjusted to reflect changes in values of time and fuel costs as discussed above The use of this option is recommended unless there is particular requirement to use different cost functions in the demand and assignment models This option cannot be used if cost damping is to be applied If this option is selected for all car available variable demand segments then the user can choose between skimming costs on the minimum cost path or as an average over all used paths This choice is made on the Highway Reference Trip Cost data page See Section 6 6 4 for more information and advice on the preferred option Otherwise only the average option is available All money charges specified in the network data files such as tolls must be in the same price base in all networks even if they relate to different modelled years 6 5 5 3 Units for PT time and fare skims Unlike highway costs PT cost components are a user input and DIADEM has no control over which units are used DIADEM therefore needs to know which units have been used in the input time and fare matrices so that it can calculate generalised costs correctly Input time skims may be in hours minutes or seconds and fare skims may be in pounds or pence 6 5 5 4 Including the full trip costs For absolute demand models and or if cost damping is being used it is particularly important that the generalised cost for each zone pair represent t
43. M OutTimePropns dat Browse Return Proportions Filename C DIADEM ReturnTimePropns dat Browse Tour Proportions Filename C DIADEM InitialTourPropns dat Browse Run DIADEM Help Close This page is used to define three file names related to PA tour demand modelling The files should contain the following data File contents Outbound proportions The proportion of all trips that go out in each time period defined by production and attraction sector demand segment and mode These proportions are typically obtained from local survey data Return proportions As outbound proportions but for the return trips Tour proportions An initial estimate of tour proportions which will then be adjusted using the above data Defines the proportion of trips going out and returning in each combination of outbound and return time periods by origin and destination sector demand segment and model Typically these will based on the data file supplied with DIADEM which comes from an analysis of NTS data but in some cases may be based on local survey data Note that the former assumes all zones are grouped together into a single sector The files need only contain data for demand segments for which PA based demand modelling is being used whether it is an incremental or absolute model 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 61 DIADEM User Manual Version 5 0 SATURN Mott MacDonald For
44. More formally the total generalised cost is given by a r y r PAT2 if T gt PAT2 late arrival cost a r if t e PAT1 PAT2 on time arrival 4 1 a r B PAT1 r if r lt PATI early arrival where T is the arrival time PAT1 PAT2 are the beginning and end of the PAT window E t is the travel duration for arrival time T a is the cost coefficient for travel duration B is the cost coefficient per unit time for early arrival y is the cost coefficient per unit time for late arrival 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 14 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Note that HADES does not currently include distance or toll related costs HADES is an equilibrium model and seeks to allocate trips to arrival times in such a way that no traveller can reduce their generalised cost by changing their arrival time Equation 4 1 can be used when the scheduling constraint is at the destination end of the trip and is often applied when considering the journey to work or travel to a business appointment It may not be applicable to the return leg of such trips and thus is perhaps best suited to modelling the AM peak 4 2 3 Implementation of HADES in DIADEM 4 2 3 1 Overview This section explains the details of how HADES has been implemented in DIADEM It should be noted that HADES is currently implemented only as an absolute demand model whereas DIADEM is most often
45. The following example defines that destination sector 2 demand segment 5 time period 1 should use arrival time windows with ID 1 with B 0 3 and y 0 8 2 5 1 0 3 0 8 1 Table 6 3 is taken from the Good Practice Guide for the previous version of HADES Mott MacDonald 2004 and presents a Summary of evidence for the 6 and y parameters These are for one minute of early or late arrival relative to one minute of travel duration The table also includes a column for Lateness penalty This is a fixed cost of late arrival regardless of exactly how late that is Note that this is not included in the HADES costs within DIADEM Table 6 3 Summary of the evidence on the valuation of schedule delay Early arrival 3 Late arrival vi Lateness penalty Small 1982 0 61 2 40 5 47 Polak and Bates 1996 Arrival constrained 0 13 0 64 Arrival departure constrained 0 16 0 42 No constraints 0 02 0 25 Whole sample 0 13 0 43 Polak 1991 Trondheim Fixed hours commuters 0 66 1 02 Flexible hours commuters 0 61 0 77 Other 0 78 0 52 All 0 52 0 49 Polak 1994 LCC dataset Commuters 2 81 3 48 Employers business 0 55 1 40 Shopping and Leisure 0 66 0 40 Wardman 1998 average of 13 0 64 0 69 7 4 European studies Source Mott MacDonald 2004 6 11 4 3 Demand profile by PAT window This file defines the demand profile for each Preferred Arrival Time PAT window for each origin destination OD zone pair and demand segm
46. a description of PA modelling in DIADEM and more details of how the data in these files is used please see section 4 2 6 8 1 File formats All files are in comma separated variable format as follows Outbound and return time period proportions production sector attraction sector demand segment mode proportion in period 1 proportion in period 2 etc Initial tour proportions production sector attraction sector demand segment mode outbound time period r proportion in going out in r returning in period 1 proportion going out in r returning in period 2 etc Notes For modes mode 1 represents highway mode 2 is public transport When this data is used with an absolute model with mode choice then the proportions should not be segmented by mode this is indicated by putting mode 0 All proportions should be between 0 and 1 inclusive For a given demand segment and mode the sum of the outbound proportions over all time periods should equal one Similarly for the return time periods For a given demand segment and mode the sum of the initial tour proportions over all outbound and return time periods should equal one 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 62 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 6 9 Page 6 Absolute Model Data Figure 6 6 Page 6 Absolute Model Data D DIADEM C DIADEM 2011DoMin xml ej x File Time Periods User Classes View Help
47. ak C Inter peak IPM peak J Off peak Assignment Timeslices Are Based On Departure Times Trip Midpoints Arrival Time Window Definitions ATDefinitions dat Browse Scheduling Parameter Definitions SchedulingParams dat Browse Demand profile by PAT window DemandProfileByPAT dat Browse Pre and post peak travel times PrePostPeakTimes dat Browse Run DIADEM Help Close 6 11 1 HADES log file The HADES log file is described in Section 7 1 4 It contains very detailed information and for large networks can take up a lot of disk space It is recommended that the Output HADES Log File option is selected unless the output log file is so large that it causes problems 6 11 2 Selection of HADES time periods The selection of which demand segments which use HADES was made on the Model Parameters page Here it is necessary to define to which time periods HADES is applied 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 68 DIADEM User Manual Version 5 0 SATURN Mott MacDonald The theory behind HADES is based on the idea of a preferred arrival time at the destination and was developed to explain the behaviour of commuters in the AM peak Arguably it is less applicable to other time periods This section therefore allows the user to choose to apply HADES in certain periods only 6 11 3 Interpretation of assignment time slices Assignments may be assumed to be based
48. and included the following new features Greatly reduced memory requirements reducing the risk of out of memory errors while running DIADEM The option to carry out SATURN assignments in parallel with significant reductions in run time subject to appropriate hardware being available Greater detail in reporting in the log file D 4 Changes between 2 1 and 3 0 DIADEM 3 0 was released in March 2008 and included the following new features Demand modelling functionality extended to include production attraction tour based modelling This gave full compatibility with WebTAG guidance for the modelling of home based trips The absolute version of hierarchical logit model was added A spatial segmentation option was introduced This allowed the use of different distribution model parameters for different zone pairs Anew option to estimate intra zonal costs This allowed the choice between inter and intra zonal travel to be included as part of the distribution model A re engineered user interface A new format for the control file using xml eXtensible Markup Language 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 99 DIADEM User Manual Version 5 0 SATURN Mott MacDonald The location of SATURN executable files now stored with the control file This made it easier to work with multiple versions of SATURN and ensure that the correct version is used for a given DIADEM run
49. api dll open 1 453538064 49 DIADEM User Manual Version 5 0 SATURN Mott MacDonald When pivoting off the base year the reference cost coefficients must be the base year coefficients The forecast cost coefficients must be the future year coefficients Values of time increase in the future as a result of increasing incomes Similarly operating costs change as a result of changing fuel costs and improved vehicle efficiency These changes should be accounted for when calculating cost coefficients for future years This means that VOT and VOC values for the forecast scenario should be different from the reference values when pivoting off the base year When modelling the DS by pivoting off the DM they would normally be the same The most common exception to this would be if fuel costs change between DM and DS as would be the case when undertaking realism testing see Section 8 1 2 See section 4 1 5 for a fuller discussion of pivoting 6 5 5 2 Using assignment costs SATURN Wherever possible it is recommended that the same generalised cost function is used in the assignment and demand models Otherwise there may be problems achieving convergence If the Use Assignment Costs option is checked then DIADEM will skim the total generalised cost from the SATURN model Implicitly this means that the same values of time PPM parameters and vehicle operating costs PPK parameters will be used in the assignment and demand models
50. ar coefficients Forecast year Unrestrained growth coefficients from base year to forecast year applied to base year matrix Do minimum DM costs from Forecast year Forecast year DM demand from converged DIADEM coefficients coefficients converged DIADEM run same forecast run same forecast year year Innumerable pivoting options are possible but the above are the most common 6 6 2 Important note on reference costs lt is important that reference costs for incremental models are defined for all origin destination time period demand segment mode combinations that have non zero trips in the reference trip matrix The most common reason for not doing this occurs when the forecast network has additional zones that are not in the networks used to provide the reference costs All networks in DIADEM reference and forecast PT and highway must have the same zoning system An example of this is as follows Suppose we are pivoting off the base year i e reference costs are skimmed from the base year assignments and additional development zones are added to the forecast networks In this case no reference costs are available to and from the development zones and the demand model calculations will be incorrect The solution in this case is to make sure the development zones are included in the base year network even if there are no trips in the base year matrix This will ensure that reference costs can be skimmed for all necessary movements When used
51. at costs in the assignment are as stable as possible If you are skimming costs over an average of used paths in SATURN then the accuracy of the SAVEIT assignment is also important it should match the actual assignment as closely as possible and you may need to increase the value of the NITA_S parameter See Section 15 23 of the SATURN manual for further information CONTRAM users can improve convergence by using smaller packet sizes It is recommended that the gap is used as the stopping criterion in SATURN This requires setting KONSTP 1 in the SATURN dat network file and STPGAP equal to the stopping value 0 1 is recommended as a good starting point and can be reduced later if required It can be worth experimenting with different algorithms Algorithm 1 is normally the most effective but can be sensitive to assignment convergence noise in which case the Fixed Step Length method may give better convergence As a last resort Method of Successive Averages can be used but this is likely to be slow to converge to an acceptable level 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 84 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 8 4 Freezing particular movements In many variable demand modelling applications it is desirable to freeze certain movements in the trip matrix i e they are not subject to variable demand This is most often done for external to external trips on the basis
52. ation choice in a hierarchical structure often referred to as nested logit and sometimes tree logit Mode Car Bus Destination ZING ANN D1 D2 D3 D1 D2 D3 In effect the logit model is being applied several times once for mode choice and once for destination choice for each mode separately These results are then combined together On the probability side the probabilities at the bottom level are calculated conditional on the choice s made at the higher level This is denoted pjim The combined probability of a traveller from zone choosing to travel to destination j by mode mis then exp 8Uj m explU m Pimi PmiP jim S exp 0U ism Y expU m m J Note the additional scaling tree parameter 8 This must be greater than zero and less than or equal to 1 It represents how sensitive the upper level choice is to cost relative to the lower level The response most sensitive to cost appears at the bottom of the hierarchy with other responses less and less sensitive the higher up the tree they are On the cost side the bottom level model uses generalised costs converted to utilities Ujm in the normal way The higher level then uses utilities U which are in some sense the average over the choices in the nest below These are referred to as composite utilities and are calculated as follows U m In S exp Ujim j 4 1 4 Spatial segmentation Spatial segmentation is a feature available in DIADEM whereby the distribution mode
53. ation page the assignments must then be run one after the other so that the correct queues can be passed from one time period to the next NB If you are using the option to run SATURN assignments in parallel then you must put the SATURN network files for each time period in a separate directory This will reduce the risk of file opening errors with different SATURN programs trying to access the same file at the same time 6 13 Running DIADEM Once all the data has been set up the control file should be saved The DIADEM process is started by clicking on the Run DIADEM button at the bottom of the window After a few moments an assignment will start When the first assignment finishes a small DIADEM progress window will appear reporting the iteration number and the current value of the relative gap function Depending on the convergence criteria a number of further assignments will be carried out If any errors arise during the process a window will appear with an error message Otherwise DIADEM will have successfully completed when all the assignments have stopped Note that for very large models there may be a gap of up to a few minutes between assignments When DIADEM has finished a message will appear saying DIADEM run completed 6 13 1 Batch mode Several DIADEM control can be set up to run consecutively without any user intervention To do this create a plain text file Each line of this file should contain the name of a con
54. avail amp C DI4DEM 201 1 DoMinPM dat amp non home based EB Duration mins 180 car avail Off peak Other 8 CADIADEM 2011DoMinOP dat home based other Duration mins 720 car avail amp non home based other car avail GYS B Gys GYS Browse Clear Clear Run DIADEM Help Close The first step is to define the time periods that are being modelled Time periods can be further divided into time slices Time periods are intended to be fairly long say the whole of the AM peak e g 0700 1000 or the inter peak e g 1000 1600 whereas time slices are typically much shorter say 10 to 30 minutes long The main purpose of timeslices is to provide a profile of demand and travel costs within the time period for the HADES arrival time choice model Before the HADES option was introduced it was standard practice to use one time slice per time period Note that the main demand model operates at the time period level using costs averaged over all time slices in the period with demand by time period then being split to time slices either using HADES or by applying user defined profiles depending on the options selected by the user A SATURN network file dat must be defined for each time slice representing the scenario you are forecasting 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 43 DIADEM User Manual Version 5 0 SATURN Mott MacDonald To defi
55. bers of LOG files MX LOG SATLOOK LOG may be produced It is usually safe to delete them 7 2 Dealing with error and warning messages 7 2 1 Overview Error messages in DIADEM will usually appear in their own window and will cause the program to stop before it has successfully completed Warning messages will appear in the log file They do not prevent DIADEM running to completion but may indicate problems with the input data that may mean the results are unreliable Some of the more common messages are discussed below For more help with these and any other messages please contact DIADEM technical support 7 2 2 Division by zero in elasticity demand function calculation error This message appears in a pop up window and will include information on the origin destination demand segment and time period concerned It will usually occur just after the first assignment It can only appear when an elasticity model is being used with a non zero y parameter DIADEM will report this message if either the reference or the forecast cost Is zero This is usually a result of not setting the Assign zero packets parameter to TRUE This will usually be because the reference trip matrix includes intra zonal trips the skimmed cost will be zero for these trios Because intra zonal trips are not assigned they do not affect the costs for other trips and can safely be removed from the reference trip matrix lt may also be a result of centroid
56. creasing over time 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 31 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 4 6 Segmentation in DIADEM 4 6 1 Demand segments DIADEM demand models operate with two levels of segmentation trip purpose and person type This is illustrated with an example in Figure 4 10 Total travel is first split into 3 purposes commute employer s business and other Travel within each purpose is then further divided into trips made by two distinct person types those with a car available for the trip and those without Figure 4 10 Segmentation by trip purpose and person type All Travel Commute EB Other Car avail No car Car avail No car Car avail No car As well as car availability the other common division of person types is according to income or willingness to pay bands for example to model the response to road tolling This can also be represented in DIADEM Each trip purpose is treated independently for demand modelling For example in a doubly constrained distribution model distinct trip end constraints are used for each purpose and are applied separately The way person types are grouped into trip purposes only matters when a doubly constrained distribution model is being used In this case the trip end constraints are applied at the purpose level not to individual person types within that purpose This can be seen as different person types competin
57. d passenger trips when calculating mode choice It represents the average number of private vehicle occupants for the demand segment It should be greater than or equal to 1 If the input reference highway trips are in units of pcus then the occupancy should be the average number of occupants per pcu unit if they are in units of vehicles then it should be the average number of occupants per vehicle 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 52 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 6 6 Page 3 Highway Trip Cost Data Figure 6 3 Page 3 Highway Trip Cost Data SATURN version D DIADEM C DIADEM 201 1DoMin xml E Mf x File Time Periods User Classes View Help D H C CONTRAM SATURN Segmentation Model Parameters Highway Trip Cost Data PT Trip Cost Data PA Model Data Absolute Model Data DIADEM Parameters HADES Data SATURN Settings Define UFS Files for Reference Costs Define Reference Cost Matrices UFS Files Matrices 1 AM peak 0000 0300 Select Root Directory C ADIADEMBaseYr M ufs Browse Apply ciidiadem Browse 01 C DIADEM BasevrAM ufs 02 C DIADEM BaserIP ufs Trips 201 1DMRefTripsHwW 03 C DIADEM Base rPM ufs 04 C DIADEM Basev rOP ufs Time SATURN GC Distance Toll Clear m Skim Costs Average over all paths Minimum Gost path only Demand profile for non HADES segmen
58. departure The first iteration On the very first iteration it is assumed that all travellers choose to arrive at their preferred arrival time The user defined pre peak travel durations are then used to convert this to departure times or trip mid points and then to demand by time slice for assignment 4 2 4 Integrating HADES with other demand responses The process described above refers to HADES iterating with the assignment only with no other demand responses involved However DIADEM allows HADES to be integrated with other responses specifically absolute logit models of mode choice and distribution the other DIADEM responses of frequency and time period choice are not currently available in absolute form The overall process is illustrated in Figure 4 8 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 22 DIADEM User Manual Version 5 0 SATURN Figure 4 8 Integrating HADES with other demand responses Main demand model mode choice amp distribution Demand by time period HADES segments periods Run HADES model Demand by assignment time slice Assignment Costs by assignment time slice Costs by time period Mott MacDonald Demand by time period non HADES segments periods Apply fixed profiles The main demand model is shown at the top This produces trip matrices by time period For HADES demand segments and time periods the HADES process described earlier
59. distance on the network for the zone pair a is a parameter greater than or equal to zero and less than one k is a positive parameter d is a cut off distance below which cost damping is not applied with k d if d gt 0 According to WebTAG dshort should be calculated by skimming distances along minimum distance paths built between all origin destination pairs using a base year network In forecasting there would only be a need to recalculate these distances if the structure of the network changed significantly between base and forecast years 4 3 2 2 Damping by a power function of cost B damped cost u 60 t s VOT where 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 26 DIADEM User Manual Version 5 0 SATURN Mott MacDonald B is a parameter greater than zero and less than or equal to one u is a positive parameter 4 3 2 3 Varying the non working value of time by distance damped cost 60 t VOT y where VOT is the value of time which varies with distance and is calculated using vor vor Max d de d do VOT isthe average value of time do is a calibrated parameter value to ensure that the average value of time is consistent with that derived from either WebTAG or local data Nc is the elasticity of VOT with respect to distance de is a calibrated parameter value designed to prevent short distance trips particularly intra zonal trips beco
60. dows should start no later than the arrival time of a trip departing at the beginning of the assignment period and should finish no earlier than the arrival time of a trip departing at the end of the assignment period If this message appears in relation to early iterations but not for the final few iterations of the HADES run it is less important to take action This can happen because demand is too peaked in the early iterations everyone arriving close to their preferred time leading to excessive travel durations As the iterations progress the peak flattens out travel durations reduce and all trips now arrive within the period covered by the arrival time windows 7 2 2 Pre peak travel durations not increasing Post peak travel durations not decreasing As described in Section 4 2 3 5 interpolation and extrapolation is used to estimate the travel duration for any given arrival time For arrivals near the beginning of the modelled period this works best if travel durations are increasing i e the pre peak travel duration is less than the travel duration from the first assignment time slice which is less than that from the second time slice Conversely at the end of the modelled period travel durations should be decreasing This message may indicate that the fixed pre or post peak travel durations are too high or that the period covered by the assignment is too short hence travel durations do not increase at the beginning of the period
61. e However redistribution of trips between inter and intra zonals is a possible response To model this correctly a reasonably accurate intra zonal cost is required DIADEM estimates this as a factor p multiplied by the minimum inter zonal cost p 0 5 is a sensible value to use It is possible to define a minimum intra zonal cost This can be useful if for any reason the inter zonal costs in the network model are not accurate e g the minimum inter zonal cost may be zero because of unusual centroid connector configurations for adjacent zones 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 48 DIADEM User Manual Version 5 0 SATURN Mott MacDonald For incremental models intra zonal costs are only relevant if there are intra zonal trips in the reference matrix If this is the case it is important that the intra zonal trips are a reasonable estimate and not just an artefact of matrix building procedures For absolute models it is recommended that intra zonal cost estimation is always used Spatial segmentation is a feature in DIADEM whereby the distribution model parameter A or 8 depending on its position in the hierarchy can vary according to the zone pair When this option is selected the distribution parameters are defined in a separate file which has the following comma separated variable format origin sector destination sector demand segment mode distribution model parameter The zone to sector co
62. e instead it relies on other software packages to carry out assignments Currently DIADEM can formally be linked to CONTRAM and SATURN highway assignment models although a DIADEM user has developed a link to VISUM and links to other packages may be possible contact DIADEM support for advice Any public transport PT assignment package can be used provided it can produce trip and cost data in the required format The software structure is illustrated in Figure 1 The PT and highway assignment models are external to DIADEM Outputs are produced in comma separated variable CSV or plain text format to allow further analysis ie 3 1 DIADEM software structure User s PT Interface Assignment Parameters Model structure Costs AE Model Engine Assignment e Equilibrium trip matrix amp convergence results 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 3 2 Types of demand model The main form of demand model available in DIADEM is the incremental hierarchical logit model as recommended in WebTAG This can be used to model trip distribution mode choice and time period choice and can also be linked to an incremental trip frequency model The incremental model works by adjusting an input reference demand matrix according to changes between forecast travel costs and input reference travel costs An absolute version of
63. e defined in DIADEM These constants must be in the same units as the generalised cost function usually generalised minutes A positive value for the constant reflects a dislike of that mode i e other things being equal as the constant increases the mode share will decline Similarly destination choice cannot usually be explained by travel costs alone For example suppose Zones X and Y have the same travel costs from Zone Z If Zone X has twice as many shops as Zone Y we would expect it to attract about twice as many shoppers This is reflected through the use of size variables 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 DIADEM User Manual Version 5 0 SATURN Mott MacDonald which are added to the basic functional form as follows _ By exp U Pa gt B exp U j where Piji is the probability of someone travelling from zone choosing to travel to zone B is the size variable for zone j Because of the way size variables are used it is only their relative size that affects the model results the actual units used are not important Typical size variables will depend on the trip purpose For instance commuting trips will often use the number of jobs shopping trips might use the amount of retail floorspace or the number of retail employees Size variables cannot be used with the doubly constrained distribution model i e where the number of trips to and from each zone is fixed Wh
64. e dotted line and is controlled by DIADEM A summary of trip and cost data requirements for the different tyoes of demand model is shown in the table below Table 5 1 Summary of trip and cost data requirements io Incremental model Absolute model Reference trips Required by time 24 hr Not required period Reference costs Required Required when trip ends are defined rather than initial guess Tripends ends Not Notrequied Either trip ends or initial guess matrix is required mee guess E required matrix Initial tour Not required Required Not required Required proportions outbound and return time period proportions An absolute model with HADES requires the following additional trip data Profile of trios over preferred arrival time windows for each HADES demand segment time period Profile of trios over time slices for each non HADES demand segment time period 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 34 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 5 2 Incremental model Before running DIADEM you will need at least the following Assigned validated base year highway networks if pivoting off the base Assigned DM highway networks if running the DS and pivoting off the DM Forecast year reference highway trip matrices Forecast year scenario e g 2016 Do Minimum network data files Details of the demand model structure you
65. ear network with the distance coefficient increased according to 3 No other changes from the base year network should be made 5 Obtain T The method for doing this depends on the demand model being used a Incremental OD T comes from the validated base model assignments 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 81 DIADEM User Manual Version 5 0 SATURN Mott MacDonald b Incremental PA and Absolute PA OD T obtained from assigning the results of a DIADEM run set up to model the base year without the fuel cost increase 6 Setup a DIADEM control file for the increased fuel cost scenario The inputs should be as follows a Forecast network files should be those created in 4 above b For an incremental model i Reference costs should be base year costs li Reference trip matrices should be base year trip matrices c For an absolute model i Initial guess matrix should be base year trip matrices 7 Run DIADEM to convergence 8 Extract forecast vehicle kilometres T from the assignments and calculate the elasticity of vehicle kilometres with respect to fuel cost using the above formula This can be done by user class and time period lf the outturn elasticity is not acceptable then some adjustment of the demand model parameters may be required The elasticity is roughly proportional to the distribution lambda parameters input to DIADEM assuming distribution is at the bottom
66. ector and time period The definition of scheduling parameters 6 and y is done by demand segment destination sector and time period The definition of demand by PAT window is done by origin destination pair demand segment and time period 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 15 DIADEM User Manual Version 5 0 SATURN Mott MacDonald The ability to define PAT windows and scheduling parameters by destination sector is designed to recognise that there may be variations in trip scheduling constraints even within a demand segment For instance commuters travelling to one area may be predominantly factory workers who will have different constraints compared to workers travelling to a different area who may be mainly office workers The details of how to enter HADES data in DIADEM are covered in Section 6 11 4 2 3 4 What does the assignment represent For a dynamic assignment model like CONTRAM it is well defined that the trip matrix for say the time slice 0810 0820 represents all trips departing in that time slice For a static model like SATURN it is less clear cut and to some extent is up to the modeller So a SATURN time slice for 0810 0820 could either represent trips departing in that time or trips with a mid point in that time the mid point being midway in time between the departure time and arrival time of the trip Within DIADEM the user can choose either option 4 2 3 5 A
67. ed Origin only OD destination constrained Destination only OD doubly constrained Origin and destination PA singly constrained Origin only PA doubly constrained Origin and destination 6 9 3 Constants Definition of constants is optional For the distribution model it is possible to define size variables for origins for destination constrained models or for destinations for origin constrained models It is also possible to define K factors Note that size variables are ignored for doubly constrained models For the mode choice model mode constants can be defined For more details on the use of constants refer to Section 4 1 2 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 64 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 6 9 4 File formats All files are in comma separated variable format except highway initial guess for CONTRAM as follows Initial guess uses the same file format and file naming convention as reference highways trips SATURN version origin zone destination zone trips trips are trips per hour for OD 24 hour total for PA Trip ends PA modelling zone number demand segment trip end total Trip ends OD modelling zone number demand segment trip ends in time period 1 trip ends in time period 2 etc Size variables zone number demand segment size variable K factors origin zone destination zone demand segment K factor Mode constants mode de
68. ed by DIADEM By definition the PT reference demand for car available demand segments should exclude any trips that are captive to public transport DIADEM is designed to be used with outputs from any PT assignment package so a simple matrix format is used and it is assumed that each matrix file contains data for only one data type demand segment and time period The format used is a comma separated variable format with three numbers on each line representing the origin zone destination zone and matrix value For example 101 102 23 5 104 103 42 1 defines a value of 23 5 from zone 101 to zone 102 and a value of 42 1 from zone 104 to zone 103 This is the same as TUBA format 2 The files are defined as follows select a data type from the drop down menu and specify a file name Then select to which time period s and demand segment s the data applies click on the appropriate check boxes It is expected that cost data will quite often apply to more than one demand segment and possibly time period but it is unlikely that trip data will It is possible that the same cost data apply to both the reference and forecast cases In this situation the file only needs to be defined once provided the Use reference costs as forecast costs box is checked Having done all that click the Use button a summary of the input data then appears in the box in the bottom half of the page Note that facilities to edit data once it appears in this box a
69. egment number for each purpose person type combination can be viewed in the Demand segment drop down box on the Model Parameters page Rather than specifying the file name separately for each individual demand segment and time period combination it is only necessary to define the root name in the above examples and the directory where the files are located For example suppose the reference trip files take the form reftrip_ TX _DS Y dat then just reftrip is entered in the Trips box on this screen reftrip_T1_DS3 dat would be the reference trip matrix for time period 1 demand segment 3 The format used in these files is a comma separated variable format with three numbers on each line representing the origin zone destination zone and matrix value For example 101 102 23 5 104 103 42 1 defines a value of 23 5 from zone 101 to zone 102 and a value of 42 1 from zone 104 to zone 103 This is the same as TUBA format 2 It is important to note that in the SATURN version of DIADEM zone names rather than sequential numbers should be used in all input matrices DIADEM relies on the reference trip matrix to build up a complete list of zone names Therefore each zone name should appear at least once in the reference trip matrix If for some reason there are zones with no trips to or from them in the reference matrix then the simplest thing to do is to add them as an intra zonal OD pair with zero trips e g for zone 101 pu
70. ellers less sensitive to cost differences in the higher cost scenario 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 25 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Table 4 1 Example of the effect of cost damping on choice probabilities Mode shares choice probabilities N i Tere Undamped ee Without damping With damping a E e c E E Scenario 1 PT 800 E E ar 0 9 Scenario 2 PT 0 WebTAG 3 10 2 Variable demand modelling scope of the model sets out 4 different cost damping mechanisms All of these are available in DIADEM For consistency with the notation in WebTAG the following sections use c to refer to money costs in DIADEM this is either the combination of toll and vehicle operating cost for highway trips or fare for PT trios The basic cost function is then 60 t lt Grad The following sections set out the different cost damping functions Further information on obtaining Suitable parameter values can be found in WebTAG All the functions have been previously used in practice but there is little empirical evidence to prefer one method over another A thorough review of cost damping methods and their theoretical basis can be found in Daly 2010 4 3 2 1 Damping by a function of distance a Ashort g0 t _ _ itdshort gt a k VOT damped cost 60 t if dshort lt d VOT where dshort is the shortest
71. en this model is used then in effect DIADEM estimates its own size variables to ensure that the specified destination trip end constraints are met In a similar vein K factors are sometimes used in distribution models They depend on the zone pair and are added to the model as follows bee exp U pi gt Kj explU J They are used to give a better fit between modelled and observed data K factors are much abused in distribution modelling and often lead to over specified models K factors can be used in conjunction with size variables KB explU Pp N gt K B exp U J Alternatively this can be written as gt exp U In k In B us X exp U In K In B j In other words the size variables and K factors can be incorporated into the utilities This formulation is used in DIADEM in the calculation of composite utilities See following section Section 6 9 explains how the various constants are entered into DIADEM by the user 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 4 1 3 Hierarchical logit The model becomes more complicated when more than one choice is being considered Suppose a traveller has to choose between 2 modes and 3 destinations a total of 6 different combinations The multinomial logit could be used to model the choice between these 6 options but it is more common to have mode and destin
72. ent i e the proportion of travellers within each PAT window It is used to split the total demand by time period to demand by PAT window Depending on which other demand model responses have been selected the total demand may be the output from the main demand model or a fixed user supplied value 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 71 DIADEM User Manual Version 5 0 SATURN Mott MacDonald The format is origin zone destination zone demand segment time period proportion of demand for preferred arrival time window 1 proportion of demand for preferred arrival time window 2 etc The first four values are integers the rest are real numbers greater than or equal to zero and less than or equal to one The sum of the proportions must not exceed 1 0 otherwise DIADEM will issue an error message and stop If the sum of proportions is less than 1 0 then a warning message will be issued to the log file but DIADEM will continue to run this is to allow the situation where the defined trip numbers represent the demand over a longer period than that defined by the arrival time window though this unlikely to be appropriate where HADES is being used in conjunction with a mode choice or distribution model Note that different OD pairs demand segments and time periods may use different sets of arrival time windows and that the number of windows within each set might be different This means that
73. ernatives have costs of 115 and 120 as when the alternatives have costs of 15 and 20 However some modellers argue that this overestimates the sensitivity of longer trips to differences in costs and that in the former case the split should be closer to 50 50 than in the latter case One way to achieve this is to use cost damping The idea behind cost damping is to adjust the cost for longer trips so that their sensitivity to individual cost components like fuel cost or travel time is reduced A typical impact of cost damping is shown in Figure 4 9 This shows that the cost used in the demand model is reduced compared to the undamped cost and that a greater reduction is applied the higher the undamped cost Figure 4 9 Effect of cost damping on the cost used in the demand model 80 70 Undamped Damped 60 50 40 30 20 Cost used in demand model 10 0 10 20 30 40 50 60 70 80 Undamped cost Table 4 1 shows how cost damping can affect mode shares in a model of the choice between car and PT In Scenario 1 the two modes have costs of 115 and 120 units respectively in Scenario 2 the costs are 15 and 20 The difference in costs between the two modes is the same in both scenarios 5 units Without cost damping the mode shares are the same in both scenarios 60 40 With cost damping the shares in Scenario 1 become more even 55 45 but there is little change in Scenario 2 i e cost damping makes trav
74. etween a number of options For instance in the context of mode choice they can be used to predict the probability of someone choosing to travel by bus When applied to a large number of people these probabilities can be interpreted as mode shares e g the proportion of people who will choose to travel by bus These proportions can then be multiplied by the total number of people to obtain the number of people using each mode Algebraically the multinomial logit model is given by 0 exp U X explU j where 0 is the probability of choosing alternative U is the utility of alternative Utility is a term used in economics to describe the amount of satisfaction derived from consuming a product or service In general the utility of travel is negative and Is related to the generalised cost of travel by U AC where Cj is the generalised cost of alternative A is a negative scaling parameter The scaling parameter can be interpreted as a factor to convert from generalised cost to utility it can also be seen as a measure of the sensitivity of travel choices to generalised cost Taking an example with just two alternatives say bus and car it can be shown that the formula reduces to exp U ar 1 This shows an important property of logit models the choice probabilities depend only on the absolute differences in costs between the alternatives 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open
75. exp AUjmipc Piimpe N Brslitips exp A U Janine k 1 B 4 7 Updated trip matrix The application of the conditional probabilities gives an updated trip matrix _ 70 T mntpe Ipc Pmi ipc Pil impcP j imtpc and updated origin totals 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 95 DIADEM User Manual Version 5 0 SATURN Mott MacDonald _ 70 Oimtpe TipcPmiiocPryimpc B 4 8 Iteration The origin totals Ojn calculated in B 4 7 implicitly depend on the balancing factors B through the calculation of composite utilities and conditional probabilities However these balancing factors were based on a potentially different set of origin totals The result is that the trip matrix calculated in B 4 7 may no longer meet the destination trip end constraints The solution is to iterate steps B 4 4 to B 4 7 until a consistent result is obtained i e the matrix from B 4 7 satisfies the trip end constraints To aid convergence trip ends are averaged between iterations B 4 9 Application of frequency model The frequency model is only applied after the above process has converged This gives the final trip matrix from the demand model freq i 0 rttpe explo AU VT Prip PrimocP j imtpc This matrix will be further modified according to the DIADEM algorithm being used before being assigned 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 96
76. external preparation of data files is required beforehand The data set up in the DIADEM interface can be saved to the DIADEM control file The data does not need to be complete to be saved indeed it is recommended that data is saved regularly as you go through the data set up process Saved files can of course be opened later Saving and opening files is done through the File menu or via the Save and Open toolbar buttons The interface consists of a number of separate pages Sections 6 4 onwards describe the data requirements for each of these pages in turn All data should be entered for a page before moving on to the following page 6 2 The DIADEM Control file 6 2 1 Overview All data entered via the GUI is saved to a control file Control files are saved and opened using the familiar File gt Open Save and Save As menu commands New files are created using File gt New From version 3 onwards of DIADEM control files are in xml format and have a xml extension xml stands for eXtensible Markup Language The basic idea of xml is that it uses tags in angle brackets to identify and describe the data For example lt version gt 5 0 lt version gt indicates that the data item version takes a value of 5 0 i e this control file is from DIADEM version 5 0 lt is by no means necessary to understand xml to be able to use DIADEM All editing of data can and in most cases should be done through the DIADEM GUI and there is no need to wo
77. f vehicle kilometres with respect to the cost of fuel This is calculated from the following formula where T and C are the base model vehicle kilometres and fuel cost respectively and 7 and C are the equivalent quantities after an increase in the cost of fuel WebTAG recommends that the calculation is done on a network basis i e vehicle kilometres summed over links and a matrix basis i e vehicle kilometres summed over matrix cells In both cases vehicle kilometres should exclude movements and vehicle types that are not subject to variable demand To obtain the required data from DIADEM the procedure is as follows 1 Decide on what increase in fuel cost to test Web TAG suggests 10 In some cases this small increase can get lost in convergence noise so 20 may be better This gives you the value of 1 1 fora 10 increase 1 2 for 20 2 For each demand segment user class calculate how much of the distance coefficient in the generalised cost represents fuel costs If non fuel VOCs are not included then this will be 100 if they are included it will be less 3 From 1 and 2 calculate how much the distance coefficient needs to be increased to reflect your chosen fuel cost increase For example if you increase fuel costs by 20 and fuel costs represent 60 of your distance coefficient then you need to increase your distance coefficient by 20 x 60 12 4 Create forecast scenario network file s This should be the base y
78. f xml editors is available on the internet many of which are free for commercial use Please contact DIADEM support if you require further advice in this area 6 2 4 Backwards compatibility DIADEM 5 0 can open control files created in all previous versions of DIADEM This includes plain text based control files created using version 2 1 However it will always save control files in DIADEM 5 0 xml format 6 3 General information Before entering the data page by page the choice between using SATURN and CONTRAM assignment models should be made 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 42 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 6 4 Page 1 forecast network files s and segmentation Figure 6 1 Page 1 Segmentation SATURN version D DIADEM C DIADEM 201 1DoMin xml i oj x File Time Periods User Classes View Help D ca ed C CONTRAM SATURN Segmentation Model Parameters Highway Trip Cost Data PT Trip Cost Data PA Model Data Absolute Model Data DIADEM Parameters HADES Data SATURN Settings Use PASSO to Link Time Periods Highlight Time Period then select browse button to allocate SATURN File Time Periods User Classes Commute amp C DIADEM 201 1 DoMinaM dat S Commute Duration mins 180 car avail Inter peak no car amp C DIADEM 201 1DoMinIP dat Emp business Duration mins 360 8 home based EB PM peak car
79. g for a single set of trip ends Each person type can have its own demand model structure and associated parameters The only restriction is that if one person type within a purpose has a doubly constrained distribution model then all person types within that purpose must have one Person types can be marked as being car not available Mode choice is not modelled for these person types they are assumed to be captive to public transport The main reason for including such person types would be to ensure that they are included in the competition for trip ends in a doubly constrained distribution model The purpose person type combinations used in the demand model have to be mapped to the different user classes in the assignment model This is described in detail in section 6 4 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 32 DIADEM User Manual Version 5 0 SATURN Mott MacDonald In the rest of this document the term demand segment is used to refer to a particular purpose person type combination Demand segments can also represent a particular vehicle type For example HGVs can have their own demand segment which may be treated as having fixed demand i e no variable demand model is applied 4 6 2 Time periods If time period choice is modelled then time periods will be linked together through the structure of the model hierarchy If time period choice is not modelled then the demand
80. gments and time periods this is done with user defined profiles These are specified via an input file that is defined on this section of the page The file should be in comma separated variable csv format as follows origin zone destination zone demand segment time period proportion of demand in first time slice within the time period proportion of demand in second time slice in period etc The first four values are integers the rest are real numbers greater than or equal to zero and less than or equal to one The sum of the proportions for a given origin destination demand segment and time period must equal 1 0 Data must be defined for all combinations of origin destination demand segment and time period for which HADES is not used 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 58 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 6 7 Page 4 PT Trip Cost data Figure 6 4 Page 4 PT Trip Cost data D DIADEM C DIADEM 201 1DoMin xml Oj x File Time Periods User Classes View Help D bal C CONTRAM SATURN Segmentation Model Parameters Highway Trip Cost Data PT Trip Cost Data Pa Model Data Absolute Model Data DIADEM Parameters HADES Data SATURN Settings Data Type Forecast Time X Demand Segment Time Periods 2 Commute no car C 2 Inter peak C 3 PM peak C 4 Off peak Use Reference Costs as Forecast Costs D Forecast Time
81. he OD representation loses the link between outbound and return trips This means it is difficult to ensure consistency of travel behaviour between the two legs For example someone might choose to go out by car but return by bus This is one of the main disadvantages of OD based demand modelling Where matrices are in PA format it is still necessary to convert them to OD matrices by time period for assignment to the transport network This is done using a series of factors which for example represent the proportion of trips that go out in the AM peak or the proportion that return in the inter peak Conversion from OD to PA is usually not possible as the link between the two legs has normally been lost as has the information on which end of the trip is home Variable demand modelling can be done using OD or PA format matrices WebTAG recommends that PA is used for home based trips This has the advantage that it ensures consistency between mode and destination choice for outbound and return trips and that these decisions are affected by costs on both legs PA modelling is normally only used for home based trips Non home based trips can be modelled on an OD basis 4 4 2 Trips and tours DIADEM actually uses a development of 24 hour PA modelling This is necessary because traditional PA modelling does not support the modelling of time period choice in a way that is consistent with WebTAG This is explained further in the paper by Gordon et
82. he full trip costs i e the costs on the centroid connector must be an accurate representation of the costs of that section of the trip that does not take place on the 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 50 DIADEM User Manual Version 5 0 SATURN Mott MacDonald main model network Similarly on the PT side it is important that fares and times are included so that the full costs are taken into account by the model 6 5 5 5 Cost damping Cost damping is explained in Section 4 3 2 Clicking the Cost Damping button brings up the following data input screen Cost Damping Parameters mm a a 0 te je o aS oi o o o b E we o H H e H H _ Commute 0 0 0 0d 0 EB a Other O 0 0 0 0 0 0 Ooo S e e e e e a a se a a Parameters are defined by trip purpose and by mode 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 51 DIADEM User Manual Version 5 0 SATURN Mott MacDonald The default parameters are such that cost damping does not actually alter the cost compared to the undamped value This means that if you want to use say the power function of cost damping mechanism you only need to change the parameters in that section Note that the varying non working time by distance option should by definition not be applied to employer s business trips Also it should not be used at the same time as the log p
83. hr 550 0 650 0 VOC p km Units For time skims Chs mins secs Absolute P Purposes Elasticity Purposes Units For fare skims Occupancy 1 2 Cost Damping Run DIADEM Help Close This page is used to define the demand model structure and associated parameters for each demand segment 6 5 1 Defining the demand model type The first step on this page is to specify the type of demand model used for each purpose Initially all purposes appear in the Fixed Purposes box Trip purposes to be treated as variable demand then need to be dragged to one of the other boxes depending on the desired type of demand model incremental or absolute OD or PA or even simple elasticity The data that needs to be filled in on the rest of the page then varies according to which model form has been selected 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 46 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 6 5 2 Model hierarchy The demand segment is selected using the drop down box at the top of the page The numbering of demand segments is determined by the order in which they are added to the Segmentation page The model hierarchy needs to be specified for all variable demand segments except those using an elasticity model The available model responses appear in the left hand side of the Model Hierarchy frame The right hand side of the frame displays the response
84. icable you obtained above 5 Save the DIADEM control file 6 Click the Run DIADEM button 7 When the DIADEM run has completed check the output csv file to ensure that convergence is acceptable Use convergence results to pick best DIADEM matrix and use this for all further analysis Check the log file for warning messages 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 3 7 DIADEM User Manual Version 5 0 SATURN Figure 5 1 Structure of a DIADEM run incremental model pivoting off the base year Shaded background indicates user specified input Base year validated trip matrices Skimming Reference cost matrices PT amp HW 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 TEMPRO growth Forecast trip matrices Demand model Forecast highway assignment Forecast cost matrices PT amp HW a O ce a a S E S A Nala eh Sc ac yc EE EERTE AEREO A E ct eth See ee eek eth bP te OE PIO EE E TEE PEA E TOEI S EE E IAT E OTENE EE E NEAN E EEE EEE eter ere rere rrr errr errr rr rrr rrr reir rr rere errr rer rrr rrrrrrre reir rrr rrr rrr rrr rer rrr rr rrr reer rrr err rr errr rrr err rr reer re rr rire rrr rrr reer rrr rrr rr rrr rrr trier rrr errr rr rrr rrr rrr errr rrr r rrr rr 38 Mott MacDonald DIADEM User Manual Version 5 0 SATURN Figure 5 2 Structure of a DIADEM run incremental model
85. imes for time period 1 user class 2 The units used should be hours for SATURN GC and time kilometres for distance pence for tolls Method 2 This method is simpler than Method 1 and reduces the risk of errors being introduced due say to skimming costs in incorrect units For each time slice a SATURN UFS file must be defined from which DIADEM will skim the reference costs automatically For instance if you are pivoting off the base year then you would specify the UFS files for the base year assignment If you are skimming an average over used paths see section 6 6 3 the corresponding SATURN output UFC file s must be in the same directory as the UFS file s but do not need to be defined by the user Reference costs and trips for non car available demand segments are specified on the PT Trip cost data page 6 6 4 Skimming costs with SATURN There are two options for skimming costs from SATURN assignments Averaging flow weighted over all used paths so called forest skims and Skimming the current minimum cost path only In a perfectly converged assignment the generalised costs skimmed using the two methods are identical However individual components time distance toll are unlikely to be The minimum cost path option is recommended provided that a the assignment converges very well e g assignment gap of 0 1 or less and b the demand model uses the same cost function as the assignment which also precludes the
86. imise the local data requirements This can be illustrated with reference to the following table Table 4 2 Tour and PA modelling data requirements Return time period om rp m oP moa o S S S ST p m O Q O Consider a particular zone pair mode and demand segment For tour based modelling we need to know the proportion of trips for each of the 16 unshaded cells in the table e g 45 of commuting trips by car go out in the AM peak and return in the PM peak However typically we may only know the proportions in the shaded cells e g 70 of commuting trips by car go out in the AM peak The approach used in DIADEM is to take initial estimates for the unshaded cells also called tour proportions for which local information may not be available and to use local data for the shaded cells to furness the unshaded cells Initial estimates for the tour proportions cells by purpose and mode have been calculated from National Travel Survey NTS and reproduced in Appendix C for information The user then needs to just specify local data for the shaded cells These tour proportions are used slightly differently in the incremental and absolute implementations of the model In the incremental version the tour proportions are applied to the input reference 24 hour PA matrix to obtain reference tour matrices by outbound and return time periods The tour proportions have no further use as the information they
87. in first out FIFO principle by looking for any cases where later departure implies earlier arrival A warning message will be issued to the log file if this is found to be the case 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 18 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Cost of arrival in each arrival window For a trip for a given origin destination and demand segment with a preferred arrival time window k the next step is to calculate the cost of arrival in each possible arrival time window in order to then be able to calculate the optimum arrival time i e the one with the minimum cost The cost of arrival in a given arrival time window is calculated assuming arrival in the middle of the window The travel duration interpolated as described earlier is added to any scheduling cost The scheduling cost will be zero for any actual arrival time window h that is the same as the preferred arrival time window k For a trip from zone ito zone jin demand segment c with PAT window k the generalised cost of arriving in the middle of a particular arrival time window h at time 7 is given by io Th Yueh PAT 2 if T gt PAT2 late arrival Viickh Eich if rt PAT1 PAT2 on time arrival 4 2 E jon B PAT1 1 if T lt PAT1 early arrival Eien is the interpolated travel duration for demand segment c from zone ito zone j arriving at time Tp J is the sector
88. ing relied upon by any other party or being used project only It should not be relied upon by any other party or for any other purpose or containing any error or omission which used for any other purpose is due to an error or omission in data supplied to us by other parties This document contains confidential information and proprietary intellectual property It should not be shown to other parties without consent from us and from the party which commissioned it Mott MacDonald Stoneham Place Stoneham Lane Southampton SO50 9NW United Kingdom T 44 0 23 8062 8800 F 44 0 23 8062 8801 W www mottmac com DIADEM User Manual Version 5 0 Content Chapter Title 1 Introduction 2 Installation 3 Overview of DIADEM 3 1 Purpose of DIADEM 3 2 Types of demand model 3 0 Help and support 4 Background information 4 1 Logit models 4 2 HADES 4 3 Generalised costs and cost damping 4 4 OD and PA 4 5 Public transport in DIADEM 4 6 Segmentation in DIADEM 5 Preparation for a DIADEM run 5 1 Overview 5 2 Incremental model 5 3 Absolute model 6 Entering DIADEM data 6 1 Overview 6 2 The DIADEM Control file 6 3 General information 6 4 Page 1 forecast network files s and segmentation 6 5 Page 2 Model Parameters 6 6 Page 3 Highway Trip Cost Data 6 7 Page 4 PT Trip Cost data 6 8 Page 5 PA Model Data 6 9 Page 6 Absolute Model Data 6 10 Page 7 DIADEM Parameters 6 11 Page 8 HADES Data 6 12 Page 9 SATURN Setti
89. initial guess matrix is very similar to defining highway reference trip data For all PT data and SATURN highway data 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 63 DIADEM User Manual Version 5 0 SATURN Mott MacDonald OD trips 2 TX DSY dat for time period X and demand segment Y is any user defined string Xand Y are integers For PA based modelling the initial guess trips should be in the form of a 24 hour PA matrix therefore the file name excludes any reference to the time period DSYdat The root of the file name which need not be limited to four characters is defined separately for HW and PT initial guess in the appropriate text box 6 9 2 Trip ends When applying an absolute model a full estimate of the trip matrix may not be available in which case just the trip ends can be defined instead DIADEM then needs to distribute the trip ends to get a matrix suitable for the first assignment It does this using reference costs defined on the previous highway and PT data pages Note that the trip ends may be total trip ends over all modes or separately for each mode depending on the modelling options chosen Responses modelled for demand segment interpretation of trip ends Mode choice modelled Total over all modes No mode choice Single mode highway if car available PT if no car available Form of distribution model Bri ends required OD origin constrain
90. ion constrained distribution is a model of origin choice B 1 2 Absolute multinomial logit The absolute multinomial model is 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 90 DIADEM User Manual Version 5 0 SATURN Mott MacDonald explo U gt exp OU j Composite utilities are given by U ind explU i The generalised cost C might include mode constants Additional constants B size variables and K K factors can be used for a distribution model BK explU p O EE SB Kix expleU k B 1 3 Extension to doubly constrained distribution A modified version of the incremental model is used for doubly constrained distribution Ti O N B 2 gt B Ti exp BAU k 1 where Tj is the forecast number of trips travelling from zone ito zone j T is the reference case number of trips travelling from zone ito zone j O is the number of trips travelling from zone B are destination based constants normalised so that B j Ss equal to the number of zones J The B are calculated by DIADEM to ensure that the destination trip end constraints are met the model formulation ensures the origin constraints are always met This is done using a Furnessing procedure Note that destination constraints are summed over all person types within a purpose and across all modes and time periods if those choices have been modelled The change in composite utility for origin zone a is
91. ise the number of iterations it is worth experimenting with different algorithms to see which works best for your model It can also be worth experimenting with the initial step length for Algorithm 1 and the fixed step length algorithm The values in the Max Flow Change column of the _results csv file can provide a useful indication of how to change the step length A sequence of values of the same sign is usually an indication that the step length can be increased for example 9 98 7 85 6 37 5 16 etc Conversely alternating signs can be an indication that the step length needs to be decreased 9 98 8 56 7 45 6 78 etc High step lengths tend to be more successful in relatively uncongested networks and vice versa 8 3 Improving convergence There are a number of things to adjust to try to improve DIADEM convergence Increase the maximum number of iterations Decrease the stopping values for the gap values Decrease the value of the maximum flow change parameter Improve your assignment convergence Adjust the initial step length for Algorithm 1 and Fixed Step Length In the majority of cases improving assignment convergence is the most effective action The main measure of this is the assignment gap post simulation in SATURN Values of 0 1 or lower may be required However a low global gap value can sometimes hide local problems that cause difficulties for DIADEM Since DIADEM uses the costs from the assignment it is important th
92. l parameters A or 9 depending on the position of distribution in the hierarchy can vary by OD pair 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 4 1 5 Incremental logit Incremental models are used where the costs and demand or probabilities for a reference case are Known as we want to estimate how a change in cost will affect demand In DIADEM the form of incremental logit model is D p exp AU i gt PF exp AU j where AU U U A ci C where A lt 0 om is the generalised cost from the reference case p is the reference case probability There are two key properties of the model apparent from this equation m The results depend on the absolute changes in travel costs for each option This removes the need to consider size variables and mode specific constants as they simply cancel out when taking the differences Ifthe reference case probability is zero then it will remain zero in the forecast case These models are also known as pivot point models because they pivot around the reference case The reference case has a number of interpretations For instance it may be thought of as a hypothetical situation saying that ifthe costs were c then the demand would be pf Alternatively it can be thought of as specifying a point on the demand curve In DIADEM the user has to input the data for the reference case Commo
93. l time k the minimum cost over all actual arrival times h is found A flow weighted average of this minimum cost over all k is then calculated This gives the cost by OD pair demand segment and time period which is passed to the main demand model Note that for HADES demand segments and time periods the costs used in the main demand model do not include distance related or toll costs This may be changed in a future version 4 3 Generalised costs and cost damping 4 3 1 Generalised costs DIADEM works with generalised costs in time units specifically generalised minutes The cost function is oie sc Se for highway trips VOT pose for PT trips VOT where t is the time in hours d is the distance in kilometres toll is the toll or parking charge in pence fare is the PT fare in pence ppk is the vehicle operating cost in pence per kilometre VOT isthe value of time in pence per hour Generalised costs are converted to utilities for use in the demand model as described in Section 4 1 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 24 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 4 3 2 Cost damping lt was noted in section 4 1 1 that the choices predicted by the multinomial logit model depend only on the difference in utilities or generalised costs between alternatives This means that for a choice between two alternatives the model gives the same choice probabilities when the alt
94. lices in the period only the end time needs to be defined the assumption is that time slices are contiguous so the start time of each time slice must be the end time of the previous one The specified durations must cover the full period represented by the assignment model even if the model is only an average hour from that period For example if the inter peak period from 1000 to 1600 is modelled with a single time slice representing an average hour within the period then the start and end times of the time slice should be entered as 1000 and 1600 respectively When using PA modelling it is important that together the time periods cover a full 24 hour period In the current version of DIADEM there are some restrictions on the way the SATURN network files should be set up PASSQ should be set to FALSE within the dat file If you want to link time periods using the PASSQ option you should check the Use PASSQ box in DIADEM SAVEIT should be set to TRUE if you want to skim average costs see section 6 6 FILTIJ i e the trip matrix name should not be specified The GONZO parameter should be set to 1 0 the default DIADEM will check how many user classes are specified in the first SATURN network and set up the correct number of user classes in the user class window The user classes can be renamed via the user class menu main menu or right click 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll ope
95. lly be C SATWIN XEXES If more than one version of SATURN is installed then this should be the location of the executable files for the version that should be used for this particular DIADEM run The last chosen location is remembered by DIADEM and will be used as the default for new control files 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 73 DIADEM User Manual Version 5 0 SATURN Mott MacDonald lt is possible to set up DIADEM so that assignments for different time periods are run in parallel provided appropriate hardware is available minimum of a dual core processor This can greatly reduce run times for example running four SATURN assignments in the same time it takes to run one This page will report how many processors the operating systems reports are available It is recommended that the number of assignments set to run in parallel should not exceed this value there will be no run time advantages in doing so and there is a risk the whole DIADEM run may take longer Run time reductions from this option are likely to be small if you are using the multi core version of SATURN For this option to work it is necessary that the file SAT1O0KEY DAT is read only DIADEM will look for this file in DAT folder relative to the location of the exes and offer to make it read only if it is not already Running assignments in parallel does not apply if the PASSQ option has been selected on the Segment
96. log plus linear cost method and the varying non working value of time by distance method should not be used at the same time 4 4 OD and PA 4 4 1 Introduction As discussed in WebTAG Unit 3 10 2 Variable Demand Modelling the Scope of the Model trip matrices can be represented in origin destination OD or production attraction PA format Consider someone who lives in zone A and works in zone B They commute to work in the AM peak and return home in the PM peak This behaviour would be represented in the two matrix formats as follows OD matrix AM peak OD matrix PM peak AB a e ae ft A ojo ete o oB st 8 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 28 DIADEM User Manual Version 5 0 SATURN Mott MacDonald PA matrix 24 hrs AB oa ot oB o fo In the PA format the value of 1 with production zone A and attraction zone B implies the outbound trip from A to B and the return trip from B to A Some notations would require a value of 2 to give a single outbound and a single return trip However in the convention used in DIADEM a value of 1 implies a single trip in each direction The key differences can be summarised as follows OD Bea Usually broken down by time period Usually represent a 24 hour total Direction of travel is always from origin to destination Production end is always the home end of the trip regardless of the direction of travel It can be seen that t
97. lus linear cost method for a given demand segment The following restrictions apply to the parameter values Table 6 1 Restrictions on values of cost damping parameters Bb acceptable values k u gt 0 d de do Y gt 0 if d gt 0 then must have d k a Ne 20 lt 1 0dq n lt 1 B gt 0 lt 1 Clicking the Reset option will make all parameters revert to their default values and in effect turn off cost damping The following table shows the default values for each cost damping function Table 6 2 Parameter values needed to turn off cost damping options Damping option Parameter values to turn off defaults Function of distance k 1 a 0 d 0 Power function of cost y 1 B 1 Varying non working time by distance Ne 0 de 0 dost Log plus linear cost 0 0 1 y 0 The four text boxes at the bottom of the screen are used to define the shortest distance for each OD pair This information is only required if the damping by a function of distance or varying non working value of time by distance is being used i e if either a or n is changed from its default value of zero A file should be defined for reference and forecast for highway and PT trips the actual file used in each case may be the same These files should be in TUBA format 2 i e comma separated variable origin zone destination zone distance in kms 6 5 6 Occupancy Occupancy is used to convert between vehicle trips an
98. ly to each time period Travel times within each period are taken from the assignment model but travel times before and after the period must be defined by the user 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 72 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 6 12 Page 9 SATURN Settings Figure 6 9 Page 9 SATURN Settings D DIADEM C DIADEM 201 1DoMin xml E i Oj x Fie Time Periods User Classes View Help D S ad C CONTRAM SATURN Segmentation Model Parameters Highway Trip Cost Data PT Trip Cost Data PA Model Data Absolute Model Data DIADEM Parameters HADES Data SATURN Settings Settings Run SATURN in quiet mode v SATURN Exes Location C ASATWIN XEXES Browse Number of Assignments to Run In Parallel fi The operating system reports 2 processors are available Run DIADEM Help Close This page contains settings relating to the way SATURN should be run SATURN may be run in quiet mode which switches off all screen output when running SATURN modules such as SATALL This means that while DIADEM is running there is little visible indication of progress other than a small DIADEM progress window Windows Task Manager can be used to monitor program activity The second box specifies the path where the SATURN executable files bat exe are located If the default options were used when installing SATURN this will usua
99. mand model the origin trip ends are calculated from the reference demand matrix Oimtpc Tage j For subsequent iterations they are obtained from the application of the conditional probabilities described in section B 4 6 B 4 5 Composite utilities The change in the composite utility from the distribution time period choice and mode choice stages is then calculated Le AV impe In gt B jo a a exp AU imipc j O mpe AU moc In gt Prime explo AU t Ming 1N Pinion exple TEN fae car available person type m AU AU mpc car not available person type m PT 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 94 DIADEM User Manual Version 5 0 SATURN Mott MacDonald The reference case probabilities are calculated from the input reference demand as follows 0 Tiimtpc 0 _ jt Pm icp 70 ijktpc jtk 0 gt Timipe Phi E imcp gt Timkpe jk B 4 6 Conditional probabilities Having calculated the change in the composite utilities it is possible to calculate the conditional utilities for each level of the model Mode choice Pm ipc explo AAU aie car available person type gt Priioc explo 00e A U kpc k Pmlipc 1 if m public transport Pmiipc p p car not available person type 0 otherwise Time period choice p Phimpe explota Uinc ae 2 Pk impc amp XP eA Uimkpc k Distribution destination choice 0 Biol imp
100. mand segment value of constant Notes Origin trip ends are only required for demand segments using an origin or doubly constrained model Destination trip ends are only required for demand segments using a destination or doubly constrained model The units used for size variables and K factors are irrelevant it is only their relative value that is important Size variables and K factors must be non negative A value of zero will result in no trips for the zone or zone pair in question Any size variables or K factors not specified in the files are assumed to be 1 For modes mode 1 represents highway mode z2 is public transport The mode constants should be in the same units as the generalised cost function This is usually generalised minutes 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 65 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Mode constants only need to be defined for one mode it is only the difference in the constants between modes that matters 6 10 Page 7 DIADEM Parameters Figure 6 7 Page 7 DIADEM Parameters D DIADEM C DIADEM 201 1DoMin xml File Time Periods User Classes View Help D ca C CONTRAM SATURN Segmentation Model Parameters Highway Trip Cost Data PT Trip Cost Data P Model Data Absolute Model Data DIADEM Parameters HADES Data SATURN Settings Main demand supply loop
101. mentation page it is then possible to move to the next page Model Parameters 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 45 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 6 5 Page 2 Model Parameters Figure 6 2 Page 2 Model Parameters SATURN version CONTRAM version is very similar D DIADEM C DIADEM 201 1DoMin xml lol xj File Time Periods User Classes View Help Dy bed CONTRAM SATURN Segmentation Model Parameters Highway Trip Cost Data PT Trip Cost Data P Model Data Absolute Model Data DIADEM Parameters HADES Data SATURN Settings Demand Model Type Segment Data Demand Segment 1 Commutejcar avail Go to Next ps Copy To Next DS Model Hierarchy Parameters Logit Incremental OD Purposes Available Responses Selected Responses non home based EB Fixed Purposes non home based other Time Period Choice Mode Choice Frequency Distribution Prod Constrained Distribution Doubly Constrained i i Frequency gt N Time Period Incremental P Purposes Mode Choice 0 68 Commute lt v home based EB Distribution HW 0 065 home based other Distribution PT 0 033 Absolute OD Purposes se Generalised Cost Coefficients ae Achiancedl E A Reference Forecast Reference Forecast Parameters Elasticity A Elasticity Use Assignment Costs E VOT pf
102. ming unduly sensitive to cost changes 4 3 2 4 Log cost plus linear cost damped cost 60 t elog c yc where 5 is a small constant e g 1p and E Y are coefficients greater than or equal to zero When models of this type are used the implied value of time pence per hour can be obtained from the formula VOT 1 Vr C 0 These values of time need to be acceptable over all appropriate values of c 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 2 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 4 3 2 5 Implementation in DIADEM All of the cost damping mechanisms can be combined into a single function 60 t e log ppk d toll 5 Ua ane l cost y Sele y ppk d toll S 60 aca for highway k vor eshon d 0 60 t e log fare 5 p 0 a cost U asnon y fare S e for PT k vor mesran 2 0 where S is a binary switch that takes the values ff if e y 0 10 otherwise In other words when the log plus linear cost option is applied the final term inside the brackets is not used it would duplicate the linear cost term that has the coefficient By setting appropriate values for each parameter particular cost damping mechanisms can be turned on or off and different methods can be used together This is explained further in section 6 5 5 5 which describes how cost damping data can be entered into DIADEM Note that the
103. model is generally applied independently to each time period for OD modelling or each combination of outbound and return time period for PA modelling The exception is when the doubly constrained distribution is used in which case the constraints are applied summed across all time periods 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 33 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 5 Preparation fora DIADEM run 5 1 Overview Chapter 6 sets out the process of setting up a DIADEM run in detail This chapter describes some of the data preparation required before starting the DIADEM software and gives a brief overview of the DIADEM set up process DIADEM will need to be run separately for each scenario Do Minimum DM and Do Something DS and forecast year The data preparation and DIADEM set up are slightly different for absolute and incremental models and are set out separately in the following sections Note that it is possible to mix absolute and incremental model demand segments within the same DIADEM run Simplified representations of a DIADEM run are shown in Figure 5 1 to Figure 5 3 at the end of this chapter representing an incremental model pivoting off the base year an incremental model pivoting off the Do Minimum and an absolute model respectively The shaded boxes represent information specified by the user via the interface program The iterative loop is shown within th
104. n doubly constrained B 4 2 Inputs Inputs to the demand model are Ciimtoc reference generalised cost from zone ito zone j by mode min time period t trip purpose p person type c O corresponding forecast generalised cost skimmed from latest assignment j A corresponding reference demand defined via the user interface In all the above there is no data for the highway mode for the no car person type B 4 3 Bottom level utilities The first step is to calculate the change in utility for the lowest level of the hierarchy d AU inpe Ae C Chace where oe is the mode specific distribution A parameter B 4 4 Doubly constrained distribution Since the lowest level is a doubly constrained distribution model we need to find the balancing factors B This requires solving the set of equations given by 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 93 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Tiimtpc gt Oimtpc N BoT ikmipc exp A U ease k 1 such that the destination trip end constraints are met gt Timo Dp imtc The destination constraints are calculated from the reference demand matrix 22 0 Dip gt Timtpo imtc Note that the destination trip end constraints depend on destination and trip purpose only The balancing factors are normalised so that gt Bip N j where Nis the number of destination zones On the first iteration only of the de
105. n 1 453538064 44 DIADEM User Manual Version 5 0 SATURN Mott MacDonald It is then necessary to define the correspondence between assignment user classes and purposes It is possible for more than one purpose to be grouped into a single user class perhaps to minimise assignment run times Each demand segment can have a completely different demand model but to keep run times down it may be desirable to aggregate some demand segmenis together for the purposes of assignment For example there may be demand segments of home based employer s business and non home based employer s business which are combined in a single employer s business user class for assignment Thus the time and distance skims used in the demand model will be the same for each demand segment within the user class Purposes are added to user classes by right clicking on the user class and selecting Add Purpose Once they have been created Purposes can be deleted and renamed At least one Purpose must be created for each user class One person type is automatically created for each purpose Additional person types can be added by right clicking on the purpose and choosing either Add Person Type or Add No Car Person Type DIADEM does not include trips for no car person type in the matrices used in the SATURN assignment The right click and Purpose menus can be used to rename and delete purposes and person types Having completed the Seg
106. nce cost and flow are input by the user The forecast cost is skimmed from an assignment The power and exponential elasticity functions are special cases of the Tanner function obtained by setting one or other of the parameters to zero B 3 A general note on the demand models For the incremental model all trip end constraints etc are calculated by demand segment from the input reference demand matrix 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 92 DIADEM User Manual Version 5 0 SATURN Mott MacDonald The demand model is essentially the same for OD and PA based modelling The key difference is in the interpretation of any time dimension In the OD model this simply refers to the time period t in which the trip takes place In the PA model it is a combination of the outbound time period r and return time period S B 4 Example implementation of incremental logit model B 4 1 Introduction This section shows all the calculations involved in the application of the OD based incremental hierarchical logit for a particular model structure In this example we assume the following Single trip purpose split into Two person types say car available and car not available Car available hierarchy from top to bottom frequency mode choice time period choice distribution doubly constrained Car not available hierarchy from top to bottom frequency time period choice distributio
107. nce of SATURN being run then it is likely that DIADEM cannot find the SATURN program files Check that the path to the SATURN executable files that you defined is correct If this occurs later in the DIADEM run and SATURN is being run then it indicates a failure in one of the SATURN modules used by DIADEM The DIADEM error message will often be preceded by a SATURN error message In this case check the LP file from the latest SATURN module to be run for information on the cause of the failure Common reasons for SATURN failures include Spaces in file and path names not a problem with SATURN 10 5 or later SATNET errors in forecast network dat file s SATALL semi fatal errors in SATNET meaning UFN file cannot be used for assignment incompatibility of zoning systems in matrix and network files See Section 7 6 3 about including all zone names in reference trip matrix file SATLOOK previous failure of SATALL meaning costs cannot be skimmed from UFS file s 7 2 6 Furness not converged in doubly constrained distribution error The doubly constrained distribution model uses furnessing to ensure trip end constraints are met Furnessing is an iterative procedure and usually converges very quickly This message will be issued if the furnessing process does not converge The most likely cause is using an incremental version of the model with a very sparse reference trip matrix i e one with lots of zeros In particular it is worth
108. nchanged This has two implications The level of crowding if modelled is assumed to remain constant and will not change as public transport passenger numbers change Bus travel times are not automatically linked to changes in highway travel times If either of these effects is significant then it might be necessary to include an additional manual outer loop in the DIADEM process for example 1 Obtain an initial set of forecast PT costs for input to DIADEM 2 Run DIADEM with these PT costs 3 Based on DIADEM outputs PT passenger demand highway travel times update the forecast PT costs and if they have changed for those previously input rerun DIADEM 4 Repeat as necessary until PT costs have stabilised WebTAG states that it is acceptable to use fixed PT costs where the mode share is less than 5 As noted above it will also be acceptable if a crowding is not significant and b travel times for the PT mode s being considered do not depend significantly on highway travel times In all other cases a manual outer loop similar to that described above will be required Note that although PT costs remain fixed within a given DIADEM run they can change between different DIADEM runs to reflect different scenarios whether that it due to changes in the individual cost components e g reduced travel times as a result of infrastructure improvements or changes in the cost coefficients e g increased values of time as a result of incomes in
109. ne a time period you can Access the time period menu on the main menu or right click in the time period box and select Add Time Period or Click the Add Time Period toolbar button Time periods can be renamed and deleted via the same time period menu To define time slices and simultaneously specify the SATURN network file for the time slice choose Add time slice SATURN Network from the right click menu If time slices are linked using the PASSQ option then this can be indicated using the check box above the time periods box It is strongly recommended that this is selected if the HADES model is to be used otherwise there is a significant risk that the FIFO principle first in first out will be violated If selected PASSQ will be applied within each time period but not between time periods NB If you are using the option to run SATURN assignments in parallel see Section 6 11 then you must put the SATURN network files for each time period in a separate directory This will reduce the risk of file opening errors with different SATURN programs trying to access the same file at the same time lt is also necessary to specify the duration of each time slice This is done by right clicking on the SATURN network file name for the time slice and choosing Change Duration For the first time slice in the period the start and end times must be defined using the 24 hour clock e g 0800 1700 etc For subsequent time s
110. ngs 6 13 Running DIADEM 7 DIADEM output 7 1 Output files ie Dealing with error and warning messages 8 Hints and tips 8 1 Realism testing with DIADEM 8 2 Reducing run times 8 3 Improving convergence 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 Mott MacDonald Page 34 34 35 36 41 41 41 42 43 46 53 59 61 63 66 68 73 74 19 75 78 81 81 83 84 DIADEM User Manual Version 5 0 Mott MacDonald 8 4 Freezing particular movements 85 Sa References 86 Appendices 87 Appendix A Description of algorithms 88 A 1 Definitions and notation 88 A 2 Algorithm 1 MSA FSL 88 Appendix B Demand model functions 90 B 1 Logit model 90 B 2 The hierarchical model 92 B 3 A general note on the demand models 92 B 4 Example implementation of incremental logit model 93 Appendix C Initial tour proportions from NTS data 97 Appendix D History of DIADEM changes 99 D 1 Changes between 4 1 and 5 0 99 D 2 Changes between 3 1 and 4 1 99 D 3 Changes between 3 0 and 3 1 99 D 4 Changes between 2 1 and 3 0 99 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 1 Introduction This document provides guidance on how to run version 5 0 of DIADEM Dynamic Integrated Assignment and DEmand Modelling It should be read in conjunction with the Department for Transport s latest WebTAG guidance on va
111. nly there are two possible sources for this data referred to as pivoting off the base year and pivoting off the DM These are illustrated below 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 10 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Figure 4 2 Pivoting off the base year SBase Year Cost en oa Oe ee E eee Cee R D Forecast Year D Base Year base Trips Figure 4 2 depicts the base year situation with demand curve Dgase year and Supply Curve Spase year From the calibrated and validated base year model we know the costs C and trips T Typically in DIADEM we will be forecasting for a future year The demand curve in the future will tend to shift to Derorecast Year AS a result of economic and demographic effects e g increased car ownership higher population and smaller households DIADEM s job is then to estimate the future year equilibrium position E for simplicity we assume in this example that the supply curve does not change To do this it requires a reference case Typically this will be point R which is a combination of base year costs and applying TEMPRO growth to the base year trip matrix to obtain reference trips making sure any specific local developments are included see WebTAG Unit 3 15 2 Use of TEMPRO Data for more details on how to do this Note that TEMPRO gives a reference case growth rate i e what the growth will be if
112. of the hierarchy So if the elasticity for a particular demand segment is 50 too high then the distribution lambda parameter should be factored by 1 1 5 There is no need to adjust the theta parameters for responses higher up the hierarchy these just reflect the relative strengths of the different responses and should be maintained More information on model adjustment can be found in WebTAG 3 10 4 8 1 3 Journey time elasticity WebTAG also recommends the calculation of other elasticities Elasticities with respect to parking charges and public transport fares can be calculated by adapting the above procedure Elasticities with respect to car travel times are more problematic and require a more approximate approach The elasticities of vehicle kilometres with respect to fuel costs and journey times are related as follows time gtme Fiuel p p where p isthe cost of travel as a proportion of total generalised cost and p is the cost of fuel as a proportion of total generalised cost If you Know the total vehicle kilometres K and the total vehicle hours T then you can calculate an time p average value for _ as p uel p s 7 aT piel T bK where a is the cost per hour from the generalised cost function and b is the cost per km 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 82 DIADEM User Manual Version 5 0 SATURN Mott MacDonald The elasticity of vehicle kilometres
113. on Step length see Appendix A for explanation The maximum change in a matrix cell value compared with the matrix produced at the end of the last main iteration Objective function value See Appendix A for explanation The absolute and relative gap values see WebTAG for formulae Statistics on the stability of costs at the matrix cell level relative average absolute difference average absolute difference root mean square difference of cells changing by less than 5 Statistics on the stability of trio numbers at the matrix cell level same measures as cost stability The total trips in the system this should be constant unless an elasticity or frequency model is being used The total cost in the system Note that some statistics are only output for the last subiteration in each main iteration Advice on appropriate levels of convergence can be found in Section 1 5 of WebTAG Unit 3 10 4 Variable Demand Modelling Convergence Realism and Sensitivity Note that the trip matrix closest to equilibrium the one with the minimum gap may not be the final trip matrix output by DIADEM The convergence results should be used to identify the best iteration subiteration and the corresponding trip matrix should be used as the basis of subsequent analysis and appraisal As described in the following section DIADEM automatically identifies the trip matrices from the best iteration subiteration defined as the one with the lo
114. ost for commodity s DMM only a step length R factor for reducing step length NSUCC number of iterations without a reduction in step length before step length is increased A 2 Algorithm 1 MSA FSL A 2 1 Framework N is the main iteration counter M is the subiteration counter L counts the number of iterations since the step length was decreased 1 Initialise N 1 M 1 L 1 2 Calculate demand XN For N 1 use reference demand otherwise calculate search direction U and XN XN QyNt Any negative elements of X should be reset to zero 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 88 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Note for MSA we always have a 1 N for FSL a remains constant throughout 3 Assign demand X to obtain associated costs C X 4 Calculate objective function V V X 5 If N gt 1 and algorithm is not MSA or FSL check if V lt V If not set a Ra L 0 recalculate X as in step 2 M M 1 check max flow change not below user defined limit if it is then whole algorithm is deemed to have converged and go to step 3 6 Check for convergence If converged then exit loop 7 Set N N 1 M 1 L L 1 8 If L nsucc then a 2a does not apply to MSA 9 Goto step 2 A 2 2 Algorithm 1 objective function A 2 3 Algorithm 1 and MSA search direction 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 89
115. p matrices If you are specifying trip ends rather than an initial guess matrix then you will also need to provide some reference costs for DIADEM to carry out an initial distribution of the trip ends These will usually come from the modelling results of some other year scenario that has already been carried out for example the base year model the do minimum model when modelling the do something or the scheme opening year when modelling the design year The following is a brief summary of the steps required in setting up and carrying out a run of DIADEM Further explanation of the steps involved and terminology is given elsewhere in the manual and in WebTAG 1 Consult WebTAG and decide on the appropriate segmentation and demand model structure and parameters 2 Prepare forecast network data file s for the scenario and year you are planning to model 3 If using a mode split model or modelling non car available person types then carry out PT assignments for the scenario and forecast year you are modelling and skim forecast costs fare and 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 36 DIADEM User Manual Version 5 0 SATURN Mott MacDonald generalised time 4 Using the DIADEM interface set up the DIADEM data using the segmentation and demand model structure and parameters that you decided upon at step 1 and using the reference trip and cost matrices and PT forecast cost matrices if appl
116. pivoting off the Do Minimum Shaded background indicates user specified input DM DIADEM output matrices Skimming Reference cost matrices PT amp HW 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 Unmodified RAO GO ee ee ee a Forecast trip matrices Demand model Forecast highway assignment Forecast cost matrices Ceeeesessosocoocecscsoocoescossoeooesosooosooesesosscococesocssooocecscsooooescessoooocecesosooooesososoeocecosososoeosecssscocecscococeoocessososceesososocooesosososocesesosocosocosesoosooceesesest 39 Mott MacDonald DIADEM User Manual Version 5 0 SATURN Figure 5 3 Structure of a DIADEM run absolute model Shaded background indicates user specified input 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 RAO Go et Te ties Forecast trip matrices Demand model Forecast highway assignment Forecast cost matrices Ceecesccccesoescecoccceccescecceccoccesceesocsoecosceesecsoecoeceeseesoeccescescesoessecooscesoescecocecesoeesecocecescecsecsosceccesceesosccecosceesosocesosccesosscesosccesoescesosscessesseeoeesst 40 Mott MacDonald DIADEM User Manual Version 5 0 SATURN Mott MacDonald 6 Entering DIADEM data 6 1 Overview DIADEM input data is set up from a Windows interface As discussed earlier some
117. r the HADES assignment loop In addition the Social Pressure algorithm can be used The latter is described in Section 4 2 3 5 The stopping criteria are similar to the main demand supply loop but note that the gap is the HADES gap as defined in Section 4 2 3 5 6 10 3 Sector file This page can also be used to define a sector file This is mandatory if PA based modelling HADES or Spatial segmentation is being used A sector is a group of zones and the file contains a correspondence list between model zones and sectors The file should be in comma separated variable format with one line for each model zone zone number sector number 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 67 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Zone and sector numbers must be positive integers It may help to avoid confusion if there is no overlap between zone numbers and sector numbers For example if the highest zone number is 935 the sector numbering could start at 1001 6 11 Page 8 HADES Data Figure 6 8 Page 8 HADES Data D DIADEM C DIADEM 201 1DoMin xml jol xj File Time Periods User Classes View Help D S Cd C CONTRAM SATURN Segmentation Model Parameters Highway Trip Cost Data PT Trip Cost Data PA Model Data Absolute Model Data DIADEM Parameters HADES Data SATURN Settings J Output HADES Log File HADES applies to the Following time periods AM pe
118. re currently limited although lines can be deleted by right clicking on them and choosing Delete Selected or Delete All OD trip matrices should be defined in units of passengers per hour PA matrices should be total passenger PA trips over the 24 hour period remembering that a value of 1 in a PA matrix cell implies one outbound trip and one return trip in 24 hours PT time matrices can be generalised time matrices and include components such as wait time walk time and transfer penalties which may have been weighted Some PT packages include fares when skimming generalised time These can be used in DIADEM as long as care is taken that there is no double counting with the fare matrices PT time matrices can be in hours minutes or seconds and fare matrices may be in pounds or pence The units used should be specified in the Generalised Cost Coefficients section of the Model Parameters page 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 60 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 6 8 Page 5 PA Model Data Figure 6 5 Page 5 PA Model Data D DIADEM C DIADEM 201 1DoMin xml oj xj File Time Periods User Classes View Help D ca ed C CONTRAM SATURN Segmentation Model Parameters Highway Trip Cost Data PT TripfCost Data PA Model Data Absolute Model Data DIADEM Parameters HADES Data SATURN Settings Outbound Proportions Filename C DIADE
119. re not represented in the HADES model they will not be included in the assignment A warning will be issued to the log file if this is the case 6 11 4 2 Scheduling parameter definitions This file is used to define for each destination sector demand segment and time period which set of arrival time windows to use and the early and y late scheduling parameters Note that within HADES in DIADEM generalised costs are expressed in time units The 6 and y parameters in this file should therefore be expressed relative to a unit of travel time For example a 6 value of 0 3 would indicate that 1 minute of early arrival has the same cost as 0 3 minutes of travel duration The format for each row in the file is destination sector demand segment time period early arrival parameter 8 late arrival parameter y arrival time window ID There should be a row in the file for each destination sector and demand segment for which the HADES demand model has been selected 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 70 DIADEM User Manual Version 5 0 SATURN Mott MacDonald The destination sector must be an integer and must have been defined in the sector definition file that is specified on the DIADEM parameters page Section 6 10 3 b and y must be non negative The arrival time window ID must have been defined in the arrival time window definitions file as described in 6 11 4 1
120. riable demand modelling which provides information on appropriate demand modelling structures and associated parameters http www dft gov uk webtagq DIADEM fulfils two main roles t provides a relatively simple way to link highway assignment models to a variable demand model Currently links to CONTRAM and SATURN assignment models are supported though links to other packages may be possible It provides a means of achieving convergence between assignment Supply and demand models DIADEM is intended to extend existing highway assignment models to variable demand modelling as easily as possible It may not be appropriate for every possible demand modelling situation In some cases more complex demand modelling software will be required for example DIADEM does not include an explicit representation of walking and cycling modes There are separate versions of the manual for SATURN and CONTRAM users The manual version is shown in the header of each page of the manual The DIADEM website http Awww dft gov uk diadem provides information on DIADEM and access to the latest versions of the software and manuals 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 2 Installation You must have administrator rights on your pc to install DIADEM If you receive the software on CD the installation process should start automatically on inserting the C
121. rk with the xml files directly If you do want to find out more there is plenty of material on the internet a search for xml tutorial will bring up some possibilities 6 2 2 Viewing xml data The control file data can also be viewed via the menu option View gt Data as tables This displays the data in your default internet browser It can then be printed or copied to a word processor for example This could be useful if you want someone else to review the file or to include it in a report 6 2 3 Editing xml data It is recommended that all editing of DIADEM data is done via the GUI as this will ensure that the integrity of the data is maintained However other methods of editing xml data are available and may be used if 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 41 DIADEM User Manual Version 5 0 SATURN Mott MacDonald only very minor changes are required e g altering a file name or parameter values Major changes involving altering the structure of the model such as adding a demand segment should not be made in this way xml files can be opened and edited in any standard text editor However it is far easier to use an editor specifically designed for use with xml as these will interpret the file so that the data can be displayed in a more structured way which makes it easier to distinguish between the tags which shouldn t be changed and the data itself which can be A huge number o
122. ro time period choice model in DIADEM which deals with the choice between travelling in for example the AM peak and the inter peak HADES was originally developed as an experimental stand alone program see Mott MacDonald 2004 2005a 2005c The version of the HADES method known as HADES B has now been implemented in DIADEM and is available for use on a beta test basis This means that the software may not be as robust as a full release version As part of the beta testing process users are asked to provide feedback to DIADEM support diadem atkinsglobal com on their experience in using the software and to report any bugs as soon as they are encountered 4 2 2 Equilibrium scheduling theory EST HADES is based on the principles of equilibrium scheduling theory EST as set out by Vickrey 1969 and later developed by Small 1982 1992a 1992b and Arnott et al 1994 The main idea behind EST is that a traveller has a preferred arrival time PAT for their trip with a cost known as the scheduling cost associated with arriving either earlier or later than this Instead of arriving at their preferred time the traveller may choose to arrive either earlier or later provided they reduce their time spent travelling the travel duration by enough to offset the increased scheduling cost Note the use of travel duration instead of the more usual travel time this is to avoid any possible confusion with arrival and departure times
123. rrespondence list is defined on the PA Model Data page Mode 1 for highway and mode 2 for PT mode 0 can be used in cases where distribution is above mode choice in the hierarchy to indicate that the parameter is not segmented by mode Note that the options selected on this page apply to all demand segments 6 5 5 Generalised cost coefficients The generalised cost function used in DIADEM is A ra 2 cc for highway trips VOT BOs hie ae for PT trips VOT where t is the time in hours d is the distance in kilometres toll is the toll or parking charge in pence fare is the PT fare in pence ppk is the vehicle operating cost in pence per kilometre VOT isthe value of time in pence per hour t d tolland fare are obtained by DIADEM by automatically skimming the costs or via user supplied files see subsequent input pages for details On this screen it is necessary to define the value of time and for highway trips the vehicle operating cost coefficient Values of time and vehicle operating costs for the calculation of the cost coefficients can be found in WebTAG Unit 3 5 6 Values of Time and Operating Costs 6 5 5 1 Differences between reference and forecast cost coefficients In an incremental demand model the forecast demand depends on the difference between the latest forecast generalised cost from the scenario currently being modelled and a reference generalised cost 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llis
124. s that DIADEM will model this is initially blank Model responses are added to the right hand side by either double clicking the response on the left or highlighting the response and clicking the button between the two boxes Deselecting responses works in a similar way There are certain restrictions on which combinations of choices can be selected for each demand segment Only one form of distribution model can be selected Frequency can only used in incremental models and can only be selected if a distribution option is also selected No responses can appear above frequency HADES is only available in absolute models and if selected must be at the bottom of the hierarchy The different distribution options are discussed in Section 4 1 6 The hierarchy of responses is also defined in this box To change the position of a response in the hierarchy highlight it with a left mouse click and then use the up or down button to move it The most sensitive response should be at the bottom of the hierarchy and the least sensitive at the top This should be reflected in the values of the parameters associated with each response see below Once all the data has been defined for a particular demand segment it is possible to move on to the next one This can be done in a number of ways Select a different demand segment from the drop down box Click the Go to next DS button to go to the next demand segment Click the Copy to next
125. single time period so as a minimum there would be one set of windows for each time period However it is also possible to define a single set of windows covering the whole day and to use that set for each time period setting demand in some windows to zero as appropriate for the time period The set to be used for each demand segment destination sector and time period is defined in the scheduling parameters definition file See below It is expected that for the vast majority of applications a single set of arrival time windows for each time period should be sufficient The format for each row in the file is ID for arrival time window set start of first window start of second window start of last window end of last window The ID should be a unique integer identifying this set i e the same ID cannot be used on any other row in the file Times are in hhmm format for example 0830 or 1115 Times should be strictly increasing from left 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 69 DIADEM User Manual Version 5 0 SATURN Mott MacDonald to right The format ensures that the windows are contiguous and non overlapping There is no practical limit on the number of windows within each set The following example defines a set of four windows with ID 1 with the start of the first window at 0800 and the end of the last window at 0900 The times covered by the four windows are 0800 0820 0820 0
126. ssary to associate this duration with a fixed point in time If the assignment is based on departure times or trip mid points then the travel duration is assumed to be that for a trip departing at or with a mid point at the middle of the time slice i e if the time slice represents 0810 to 0820 then the skimmed travel duration is assumed to be for a trip departing or with a mid point at 0815 Travel durations by arrival time From the previous step for each OD pair and demand segment we have the travel duration for a discrete set of departure times or trip mid points As noted earlier HADES is more concerned with arrival times 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 17 DIADEM User Manual Version 5 0 SATURN Mott MacDonald so the data are translated to travel duration by arrival time as follows For assignments based on departure time arrival time is departure time plus travel duration For assignments based on trip mid points arrival time is trip mid point time plus half the travel duration Travel durations for arrivals at other times are interpolated from the above values This can only give travel durations for a period similar to that covered by the assignments For example if the assignment covers in total the period 0800 0900 then the above might only give us travel durations for arrival between 0810 and 0910 assuming 10 minute travel durations For this reason
127. ssignment HADES iteration Overview Although sometimes referred to as a departure time choice model HADES is strictly speaking a model of arrival time choice On the other hand assignment models typically work on the basis of departure times or occasionally trip mid point times Interfacing between the HADES demand model and an assignment model therefore involves a translation of data between the two paradigms The overall process is shown in Figure 4 5 below 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 16 DIADEM User Manual Version 5 0 SATURN Mott MacDonald Figure 4 5 Assignment HADES iteration Assignment trip matrix Allocate demand to assignment time slices Estimate travel durations as function of arrival time Adjusted arrival time matrix Convergence algorithm Arrival time matrix Allocate demand to Calculate cost of each arrival time arrival in each window arrival time window The details behind each step are described in more detail below Time skimming After an assignment travel durations are skimmed as per a conventional DIADEM run However the way these skims are used is slightly different Within HADES it is assumed that travel duration varies continuously with arrival time and therefore also by departure time For each OD pair and demand segment the skim will return a single value for the travel duration for each time slice It is then nece
128. t 101 101 0 in the reference trip matrix file OD trip matrices should be defined in units of vehicle or pcu trips per hour PA matrices should be total vehicle or pcu PA trips over the 24 hour period remembering that a value of 1 in a PA matrix cell implies one outbound trip and one return trip in 24 hours If vehicle trips are used then the occupancy on the Model Parameters page should be occupancy per vehicle if pcu trips are used then it should be occupancy per pcu 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 56 DIADEM User Manual Version 5 0 SATURN Mott MacDonald There are two available methods for entering reference cost matrix SATURN GC time and distance data Method 1 This is similar to the way reference trip matrix data is entered Reference cost data must be defined for each user class and time period As for reference trips a file naming convention is used as follows 2 TSX_UCY dat for time slice X and user class Y Note that OD costs should be defined in this format even for demand segments using PA modelling DIADEM will calculate PA costs from OD The root is defined separately for SATURN GC time distance and optionally toll matrices Note that reference trips are defined by demand segment and reference costs by user class For example suppose the root is defined as refTime then DIADEM would expect the file refTime_T1_UC2 dat to contain reference t
129. tart Using linear interpolation we then evaluate Q at the end of each assignment time slice r Call this Q The demand to be allocated to timeslice r for assignment is then just Q Q 1 where Q is Q evaluated at the beginning of the first time slice Note that not all demand will be allocated to assignment time slices Some may be allocated to pre and post peak periods and will not be assigned This is not necessarily zero as HADES can shift demand to arrive before the start of the first arrival time window 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 21 DIADEM User Manual Version 5 0 SATURN Mott MacDonald The principle is illustrated in Figure 4 7 which shows cumulative demand distributions by arrival and departure times and how the latter is used to calculate the demand allocated to a particular time slice 0800 0815 using the difference in the cumulative demand function departure time between the beginning and end of the time slice If the 0800 0815 time slice were the first assignment time slice then any trips departing before 0800 would not be assigned Figure 4 7 Use of cumulative demand distributions to allocate demand to assignment time slices 160 T e By arrival cme By departure time 120 ke c 100 o Oo S 80 ahd A gt 60 5 U 40 Demand allocated to 20 0800 0815 time slice 0 07 30 08 00 08 30 09 00 09 30 Time arrival or
130. ted conditions Transportation Research Part A 41 2007 pp788 801 Daly A 2010 Cost Damping in Travel Demand Models Report of a study for the Department for Transport Available from hitp www dft gov uk pgr economics rdg costdamping Accessed 24 March 2010 Gordon A Daly A Bates J and Oladeinde F 2007 Modelling time period choice in large scale hierarchical demand models some problems and a solution Proceedings of European Transport Conference Available from hitp etcproceedings org paper modelling time period choice in large scale hierarchical demand models some pr Accessed 30 April 2010 Mott MacDonald 2004 HADES Good Practice Guide http www dft qgov uk pgr economics rdg dtc phase2 hades accessed 18 11 09 Mott MacDonald 2005a HADES Extension Validation Report Available from http www dft qov uk pgr economics rdg dtc phase3 accessed 02 11 09 Mott MacDonald 2005a HADES B Final Report Available from http www dft gov uk pgr economics rdg dtc phase3 accessed 02 11 09 Mott MacDonald 2005a HADES User Manual 4 0 Available from http www dft gov uk pgr economics rdg dtc phase3 accessed 02 11 09 Mott MacDonald 2011 Incorporating HADES into DIADEM Results of testing programme Report reference 241930 05 Small K A 1982 The scheduling of consumer activities work trips American Economic Review 72 3 467 479 Small K A 1992a Trip scheduling in urban transportation analysis
131. the number of items on each row of this file may vary Obtaining data on preferred arrival times is one of the biggest challenges in using HADES Software has previously been developed for DfT for inferring preferred arrival times from actual arrival times but that is not on general release An alternative approach that has been suggested is to assume that preferred arrival times equal actual arrival times in the model base year HADES can then be used in forecasting with or without a scheme to give a broad indication of how arrival and departure time profiles might change in the future 6 11 4 4 Pre and post peak travel times Pre and post peak travel times are defined in this file For an explanation of their use see Section 4 2 3 5 specifically the section discussing the interpolation of travel durations by arrival time The format is origin zone destination zone user class time period pre peak travel duration post peak travel duration The first four values are integers the last two are real numbers greater than zero Travel durations should be specified in minutes Note that durations are defined by assignment user class not by demand segment However they are applied by demand segment with the demand segment to user class correspondence defined on the Segmentation page used to identify which user class values to apply to a given demand segment These travel times are defined by time period HADES is applied independent
132. the user must also define pre peak and post peak travel times When modelling the AM peak the former will be close to free flow times but the latter are likely to be a bit higher Durations for arrival before or after the time interval defined by the above process are then extrapolated until they hit the user defined pre and post peak times This gives us travel durations for a much wider range of arrival times than implied by the assignment This has implications for the length of the period covered by PAT windows compared to that covered by the assignment This is discussed further in Section 6 11 The process is illustrated in Figure 4 6 The red squares represent the travel durations for a set of departure times as defined by the assignment Converting these to travel durations for a set of arrival times gives us the blue diamonds the horizontal distance between the two represents the travel duration The blue line shows the interpolation and extrapolation of these values including the user defined pre peak and post peak durations represented by the horizontal sections of the line Figure 4 6 Obtaining travel durations as a function of arrival time 50 45 40 35 30 25 Travel duration 20 As function of arrival time E As function of departure time 08 00 08 15 08 30 08 45 09 00 09 15 09 30 09 45 10 00 Time At this stage DIADEM will check for violations of the first
133. then combined to obtain the next estimate With the fixed step length FSL and method of successive averages MSA algorithms this is done as follows john 1 a X okh A Vickn 4 4 where a is the step length as defined by either the fixed step length FSL or method of successive averages MSA algorithm If the Social Pressure algorithm is used the formula is N N i XiiCkh min i a Prl Xickn Tfh icy XN N l Nt 4 5 jckh Xin Y minja Pioen Xien if hic 4 5 h hic where Viickh T V cur l 7 e if Vo gt 0 ne i jCkhoack Pich ijCkhoack ijCkhoack Convergence monitoring Because HADES uses a deterministic choice model it is possible to calculate a delta gap function analogous to that used in Wardrop equilibrium assignment models gt x pl Vicu a min Vic en ne T 4 6 gt X jickhiickh ijckh This is referred to as the HADES gap in the output files It is not directly comparable to the demand supply gap usually reported by DIADEM The gap will be zero at equilibrium The HADES gap value can be used as a stopping criterion in the iterative process An alternative convergence measure the average excess cost AEC is discussed in Mott MacDonald 2011 Itis not automatically output by DIADEM but can be calculated from standard outputs Please contact DIADEM support if you would like details 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064
134. trips time distance and generalised cost for each time period and user class The actual amount of disk space required may vary from a few megabytes to several hundred megabytes depending on the size of the model network and matrix files If SATURN is being used as the assignment model DIADEM can reduce run times by carrying out assignments in parallel provided suitable hardware is available minimum dual core processor Software updates are made available via the website http www dft gov uk diadem Downloading the software requires a user name and password which are provided to licence holders when they purchase the software 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 3 Overview of DIADEM 3 1 Purpose of DIADEM DIADEM aims to provide a user friendly method for setting up a multi stage transport demand model and then finding equilibrium between demand and supply using an external assignment package as the supply model The process iterates between demand calculations and assignments meaning that several assignments are needed in a single DIADEM run As with all such iterative processes equilibrium is not found exactly but convergence criteria are used to determine when the solution is close enough to equilibrium More details of the algorithms used in this process can be found in Appendix A DIADEM does not include an assignment modul
135. trol file with the full path and including the xml extensions e g c diadem test1 xml c diadem test2 xml etc In the user interface go to File gt Batch File and open the above text file The first control file will start running immediately Any error messages will be written to a log file 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 74 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 7 DIADEM output 7 1 Output files There are various DIADEM output files a series of demand files a log file and a summary convergence file In addition there will be a series of files produced by the assignment software In the following descriptions root represents the name of the DIADEM control file it is used in the names of several output files 7 1 1 Checking results The following sections describe the DIADEM output files in more detail Briefly the following should be checked for any DIADEM run Deal with any error messages issued during the run Look in the log file to see if there are any warning messages and deal with these as appropriate Look at the convergence file to make sure DIADEM has reached an adequate level of convergence Check that the travel behaviour is consistent with the scenario being modelled For the DM it would be sensible to compare the output trip matrices with the reference trip matrices the DS output trip matrices can be compared with the DM
136. ts and time periods Browse Run DIADEM Help Close 6 6 1 Overview This page is used to define the highway reference demand and cost data which is used mainly for incremental demand models However reference costs are also needed for absolute demand models with trip ends defined For an incremental model the reference data represent a hypothetical situation i e ifthe forecast costs were equal to the reference cost then the demand would be equal to the reference demand In other words it defines a point on the demand curve However it is not expected to represent the equilibrium situation for the current scenario this is because when the reference demand is assigned to the network the output forecast costs will not be the same as the reference costs 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 53 DIADEM User Manual Version 5 0 SATURN Mott MacDonald The incremental demand model in DIADEM adjusts the input reference demand according to the differences between the reference cost and the actual forecast costs This process is know as pivoting see Section 4 1 3 for more information Reference trips should not be defined for demand segments using the absolute logit model Reference costs may be needed depending on the way the absolute model is set up but have a different role from when they are used with an incremental model When trip ends are defined for the absolute model the
137. use of cost damping Indeed this option will be greyed out unless the latter condition is true for all variable demand car available demand segments 244465 IT D ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 57 DIADEM User Manual Version 5 0 SATURN Mott MacDonald In other situations the average option should be selected When using this option it is essential that the SAVEIT assignment carried out in SATURN is as accurate as possible to minimise the amount of noise in the costs passed to DIADEM This usually requires setting the SATURN parameter NITA_S sufficiently high For more information see Section 15 23 of the SATURN manual version 10 9 Skimming average cost paths can take much longer than minimum costs If the reference cost matrices are input manually into DIADEM rather than letting DIADEM skim them from UFS files they should be consistent with the skim method selected e g if average skims are selected then the reference costs should represent average skims 6 6 5 Demand profile for non HADES segments and periods This file is only needed if there is more than one time slice per time period Since the main demand model operates at the time period level DIADEM needs a way to disaggregate demand by time period from the demand model to demand by time slice for assignment For HADES demand segments and time periods this is done using the HADES arrival time choice model For other demand se
138. used to implement an incremental model The notation used below does not explicitly refer to time periods Within DIADEM HADES is applied independently between time periods i e the choice of arrival time within one time period will not affect the choice of the arrival or departure time of the other leg of the trip in a different time period 4 2 3 2 Limitations The current implementation of HADES in DIADEM has some limitations Only travel durations and scheduling costs are included in the generalised cost function Distance based and toll costs are not included at the present time Convergence between the assignment and HADES demand model is slow and in some cases it may not be possible to achieve acceptable levels of convergence HADES can only be used for highway trips not public transport HADES can only be used as a stand alone demand model or linked to an absolute logit model of mode and destination choice It cannot be linked to an incremental logit model Some or all of these limitations may be addressed in future versions of DIADEM 4 2 3 3 Segmentation The levels of segmentation associated with the HADES model in DIADEM are Whether to use HADES is defined separately for each demand segment even for demand segments that do use HADES it may be turned off in selected time periods for example it may only apply to commuting trips in the AM peak The definition of PAT windows is done by demand segment destination s
139. west relative gap If the best trip matrices are not the ones from the final subiteration then it will be necessary to reassign the best matrices to obtain cost matrices link flows etc for subsequent analysis and appraisal 7 1 3 2 HADES assignment loop If HADES is being used a Summary convergence file for each HADES assignment loop is produced for each time period The loop number and the time period are included in the file name which has the format lt root gt _lterxX_ TY HADES _results csv This is very similar to the main results csv file but it reports the HADES gap value for each iteration of the HADES assignment loop and does not report an objective function value or the absolute gap value 244465 ITD ITW 4 F 7 February 2011 http pims01 pims llisapi dll open 1 453538064 76 DIADEM User Manual Version 5 0 SATURN Mott MacDonald 7 1 4 HADES log file This is an extra file in csv format containing the following information for every iteration origin destination demand segment preferred arrival time and actual arrival time Travel duration Total generalised cost Number of trips The scheduling cost can be calculated by subtracting the travel duration from the total generalised cost As with the HADES assignment loop convergence file a separate file is produced for each time period and iteration of the main demand assignment loop This file can get quite large so its output can be suppressed on the HADES Data

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