Towards replicable mode choice models for transport simulations in France 

Sebastian Hörl

21 June 2023

ISTDM 2023

Introduction

• Reproducibility
  • Low in transport modelling / simulation, especially with agent-based models
  • Can increase acceptance, uptake and more widespread use of these models
     
• Increasingly available open data sources make reproducibility possible, but processes aren't standardized or not easily accessible as open source
   
• Our goal: Have pipeline from raw data to a calibrated large-scale agent-based transport simulation that is nearly 100% replicable with reproducible results.

Context

• Existing synthetic demand process
  • Open-source pipeline
  • Based on open data in France
  • Reference implementation for Paris and Île-de-France region
     
• Adaptations for various places in France by multiple stakeholders

Context

• Existing synthetic demand process
  • Open-source pipeline
  • Based on open data in France
  • Reference implementation for Paris and Île-de-France region
     
• Adaptations for various places in France by multiple stakeholders
   
• Compatible with MATSim simulation

Context

• Problem
  • Choice model used in the simulation is not reproducible, hard-coded
  • Based on a model for Zurich that has been recalibrated
     
• Challenge
  • Use available data to estimate a new choice model for Île-de-France
  • Generalizable to other use cases
  • Use as much open data as possible
     
• Synthesis pipeline already processes, cleans, harmonizes many HTS data sets in France

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General process

• Formalize the data processing and estimation process
• Focus on open data sets vs. proprietary APIs

General process: Cleaning

• Harmonize French HTS data into the same format
  • Enquête Globale de Transport (Île-de-France)
    • Available upon request
    • Paris / Île-de-France
    • 2010/2011, new version coming up
  • Enquête Nationale Transport et Déplacements (France)
    • Open data
    • All France
    • 2008/2009
  • Various semi-standardized surveys designed by CEREMA
    • Enquête Déplacement Grand Territoire
    • Some open data (Nantes, Lille , ...)
    • Others available upon request
       
• Yielding connected households, persons, trips and legs

General process: Spatialization

• Most HTS do not provide detailed trip origin and destination information (exception EGT)

General process: Spatialization

• Most HTS do not provide detailed trip origin and destination information (exception EGT)
   
• Possible to impute likely locations based on
  • Euclidean distance between origins and destinations along the trip chain
  • Identifiers of origin and destination zones
  • Shapes of origin and destination zones
     

• Balac, M., Hörl, S., Schmid, B., 2022. Discrete choice modeling with anonymized data. Transportation.

General process: Road routing

• Plenty of APIs available (HERE, Bing, Google, TomTom, ...)
   
• Goal: Use open data and make process very easy to use
   
• Based on a OpenStreetMap dump (for instance, from Geofabrik)
   
• Based on osmnx library in Python
   
• Problem: Only speed limits are known

General process: Road routing

• We use open information from the TomTom Traffic Index to inflate OSM travel times to realistic ones

Source: TomTom Traffic Index Paris

General process: Road routing

• We use open information from the TomTom Traffic Index to inflate OSM travel times to realistic ones
   
• Uniform adjustment of the factors based on travel times in the HTS

General process: Transit routing

• Same idea: Avoid the use of APIs, allow for local processing
   
• Based on GTFS data (usually available in France), sometimes from different periods
   
• Routing of the trips using the RAPTOR algorithm (standalone implementation in MATSim)
   
• Problem: How to choose the routing parameters?

General process: Transit routing

• For calibration, we only look at transit trips in the HTS
   
• We adjust the routing parameters:
  • Utility of transfer
  • Utility of travel time per mode (bus, tram ...)
     
• Using CMA-ES blackbox optimization
   
• Objective:
  • Distribution of transfers (0, 1, 2, 3+)
  • Mode share of transit modes

General process: Transit routing

• Optimization using CMA-ES

General process: Transit routing

• Fit of the distributions
  • Baseline: Minimize travel time (-1 u/h and -1 u/transfer

General process: Additional components

• Parking pressure based on open data
  • Registered vehicles in zone divided by accessible road network

General process: Cost structure

• Need to make hypotheses on the costs (for 2010)
  • Car: 20 ct/km
  • Parking: 3 EUR/h (Paris 2010, based on duration of following activity)
  • Public transport: Per ticket or per duration
     
• Special case for Île-de-France / Paris
  • For free if person has public transit subscription (person attribute)
  • 1.80 EUR for trip within Paris or only us or metro
  • Otherwise, regression model for regional tickets (Abdelkader DIB, IFPen)

Distances: OP = Origin > Paris; DP = Destination > Paris; D = Direct

Model structure

Model structure

Model structure

Model estimation

• Using Biogeme's Python interface
   
• 18 parameters
• R2 = 0.53

Simulation

• Model has been implemented in the simulation, so we have first results
• Currently, calibrating network parameters

Conclusion and outlook

• First prototype of the pipeline works for Île-de-France
• Currently packaging up the code and preparing a paper on the baseline case
  
  
  
  
  
   
• Model improvements
  • Improve simplified travel time estimation
  • Integrate walking and bicycle routing (but few data available in 2010)
  • Include weather information, generally complexify model formulation
     
• Porting to other areas in France
  • Path 1: Compare models for different areas, hopefully they are similar
  • Path 2: Estimate a joint model for France with (ideally non-significant) regional dummies

✓

Questions?

https://slides.com/sebastianhorl/istdm23