SATURN and DIADEM Practical Experience Toni Dichev 31 st Oct 2008

 What is DIADEM?  DIADEM Approach  Model Requirements  Realism Tests / Convergence Criteria  Issues & Solutions  Practical Example  Summary: Lessons Learned & Conclusions Overview

 Variable Demand Modelling Advice (VADMA) – WebTAG Guidance  Any change to transport conditions will, in principle, cause a change in demand. The purpose of variable demand modelling is to predict and quantify these changes  Trip Frequency  Trip Distribution  Mode Choice  Time of Day Choice  DIADEM (Dynamic Integrated Assignment, and Demand Modelling) software - allows you to implement variable demand modelling as recommended by WebTAG DIADEM and Variable Demand

Specify Demand Model Structure Select Model Parameters and Realism Test Prepare Forecast Networks and Reference Trip Matrices for Demand Model Assignments Run DIADEM for different Time Periods Identify Best Iteration & Reassign networks with best matrices Demand Modelling Using DIADEM

Model needs to have appropriate split by journey purpose Which Responses to Include? Circumstances and Policy interests of assessment; Availability of Data; Dft Guidance what responses to consider DIADEM Demand Model Structure

Realism Tests - The Essentials Base Year Validated Network Base Year Revised Network Demand Segments Base Year Assigned Model DIADEM RUN Calculate Elasticities Fuel Price Journey Time Ask Questions Realistic? Reasonable? Experience from Past Studies Judgement (subjective) Take advise from the expert Contact the supplier Select Parameters Select/Tweak Sensitivity (Lambda) Parameters Yes Realistic? – Is it in accordance with general experience? Reasonable?- a subjective judgement with which you can “convince” others No Base Validated Matrix

Problems Running times – can be extensive High number of computers (one for each time period/year/scenario) Difficult to reach convergence specifically for future years – unacceptable Gap values (0.2% recommended - new Guidance 0.15%) Mode Choice modelling (can’t cope with the nested Mode Choice, doesn’t pass the changes in speeds etc. to PT)

Help with SATURN Convergence Importance of well converged SATURN assignment (Delta <0.1%) MASL,NITA,NITA_M – convergence parameters MONACO = T – reduces problems with blocking back at single lane junctions (right turners) NUC => 50 – improves accuracy (hence instability) of signals Latest versions of SATURN help greatly in achieving convergence

Help with DIADEM Convergence Change the assignment method 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

Help with DIADEM run time Skimming minimum costs within DIADEM (only if SATURN assignment convergence < 0.1%) SAVEIT = F Assignment Method Algorithm 1 tends to converge quicker Use of the best (high spec) machines NITA = 10 MASL = 100 AUTOK = T KOMBI = 0 NITA_M = 5 DIDDLE = T

DIADEM – Future Year Assignments The same convergence criteria is required – can be difficult to achieve The same structure of the files as for the Realism Test Can significantly increased the running times due to assignment and DIADEM convergence Best matrices are reassigned to produce the final assignment

Example – Strategic Motorway Link SATURN assignment Large model with assignment times > 1.5 hours Six journey purposes AM, IP and PM models Three modelled years and 4 scenarios DIADEM – distribution and frequency responses only considered (no mode choice), WebTAG guidance used for the Lamda Values, Method of Successive Averages

Practical Experience IssueSolutionPositive EffectNegative Effect SATURN assignment Delta > 0.15% Increased MASL/NITA etc.  Delta < 0.04%  Potential to use minimum costs  Improved GAP in DIADEM Significantly increased the running time of the SATURN assignment DIADEM GAP > 0.2As above plus increased the number of iterations and change of the DIADEM algorithm Reduce DIADEM GAP < 0.2 Significantly increased the running time of the DIADEM Lamda Values WebTAG recommended not providing sensible results Decrease the Lamda Values Improve the DIADEM convergence and the elasticises Not within WebTAG guidance, however excepted by the client Issues with elasticity (average < -0.3) Indentify local area conditions (make up of the local journey purpose split) Able to demonstrate sensible elasticises for the model area Not within WebTAG guidance, however excepted by the client Run times too highHigher spec computers and Cordon the model Reduce run timesCosts

Summary Lessons Learned Aware of the guidance How to select sensitivity parameters Convergence Criteria Running Times

SATURN and DIADEM Practical Experience Toni Dichev 31 st Oct 2008