Open synthetic travel demand for France

Sebastian Hörl

13 September 2023

14 September 2023

EDF/SMACH Seminar

IRT SystemX

  • Research institute situated in Paris Saclay
     
  • Fostering digital transformation in France
     
  • Current research domains:
    • Cybersecurity
    • Transport
    • Circular economy
    • Health
       
  • Three project types:
    • Individual industry projects
    • Collaborative industry projects
    • European projects

IRT SystemX

IRT SystemX

Why agent-based transport simulation?

  • Zones
  • Flows
  • Peak hours
  • User groups

Aggregated

Why agent-based transport simulation?

0:00 - 8:00

08:30 - 17:00

17:30 - 0:00

0:00 - 9:00

10:00 - 17:30

17:45 - 21:00

22:00 - 0:00

  • Discrete locations
  • Individual travelers
  • Individual behaviour
  • Whole day analysis

Disaggregated

Icons on this and following slides: https://fontawesome.com

Agent-based transport simulation

Robotaxis

Rabattement

Intermodality

Decarbonation

Agent-based transport simulation

  • Flexible, extensible and well-tested open-source transport simulation framework
     
  • Used by many research groups and companies all over the world
     
  • Extensions for parking behaviour, signal control, location choice, freight, ...

matsim-org/matsim-libs

Agent-based transport simulation

Synthetic demand

Agent-based transport simulation

Mobility simulation

Synthetic demand

Agent-based transport simulation

Decision-making

10:00 - 17:30

17:45 - 21:00

22:00 - 0:00

Mobility simulation

Synthetic demand

Agent-based transport simulation

Decision-making

Mobility simulation

Synthetic demand

Agent-based transport simulation

Decision-making

Mobility simulation

Analysis

Synthetic demand

Scenarios

Synthetic travel demand

Population census (RP)

> Truncate-Replicate-Sample (TRS)

Population census (RP)

Income data (FiLoSoFi)

Synthetic travel demand

> Imputation by quantile

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Synthetic travel demand

> Direct sampling from OD matrix

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

0:00 - 8:00

08:30 - 17:00

17:30 - 0:00

0:00 - 9:00

10:00 - 17:30

17:45 - 21:00

22:00 - 0:00

Synthetic travel demand

> Assignment of activity chains through statistical matching

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

Enterprise census (SIRENE)

Address database (BD-TOPO)

Synthetic travel demand

> Specifically designed approach to find secondary locations

Hörl, S., Axhausen, K.W., 2021. Relaxation–discretization algorithm for spatially constrained secondary location assignment. Transportmetrica A: Transport Science 1–20. https://doi.org/10.1080/23249935.2021.1982068

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

Enterprise census (SIRENE)

Address database (BD-TOPO)

Person ID    

Age

Gender

Home (X,Y)

1

43

male

(65345, ...)

2

24

female

(65345, ...)

3

9

female

(65345, ...)

Synthetic travel demand

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

Enterprise census (SIRENE)

Address database (BD-TOPO)

Person ID    

Activity

Start

End

Loc.

523

home

08:00

(x,y)

523

work

08:55

18:12

(x,y)

523

shop

19:10

19:25

(x,y)

523

home

19:40

(x,y)

Synthetic travel demand

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

Enterprise census (SIRENE)

OpenStreetMap

GTFS (SYTRAL / SNCF)

Address database (BD-TOPO)

Synthetic travel demand

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

Enterprise census (SIRENE)

OpenStreetMap

GTFS (SYTRAL / SNCF)

Address database (BD-TOPO)

Synthetic travel demand

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

National HTS (ENTD)

Enterprise census (SIRENE)

OpenStreetMap

GTFS (SYTRAL / SNCF)

Address database (BD-TOPO)

Synthetic travel demand

EDGT

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Enterprise census (SIRENE)

OpenStreetMap

GTFS (SYTRAL / SNCF)

Address database (BD-TOPO)

Synthetic travel demand

Open
Data

Open
Software

+

=

Reproducible research

Integrated testing

National HTS (ENTD)

EDGT

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Enterprise census (SIRENE)

OpenStreetMap

GTFS (SYTRAL / SNCF)

Address database (BD-TOPO)

Synthetic travel demand

Open
Data

Open
Software

+

=

Reproducible research

Integrated testing

National HTS (ENTD)

EDGT

Evaluation

  • Comparison of population attributes

Evaluation

  • Activity chains

Sampling

Current use cases

Nantes

  • Noise modeling

Current use cases

Lille

  • Park & ride applications
  • Road pricing

Current use cases

Toulouse

  • Placement and use of shared offices

Current use cases

Rennes

  • Micromobility simulation

Current use cases

Paris / Île-de-France

  • Scenario development for sustainable urban transformation
     
  • New mobility services

    Mahdi Zargayouna (GRETTIA / Univ. Gustave Eiffel)
    Nicolas Coulombel (LVMT / ENPC)

Current use cases

Paris / Île-de-France

  • Cycling simulation

Current use cases

Paris / Île-de-France

  • Simulation of dynamic mobility services
     
  • Fleet control through reinforcement learning

Current use cases

Lyon (IRT SystemX)

  • Low-emission first/last mile logistics

Current use cases

eqasim-org/eqasim-java

eqasim-org/ile-de-france

Current use cases

eqasim-org/sao_paulo

eqasim-org/california

Balac, M., Hörl, S. (2021) Synthetic population for the state of California based on open-data: examples of San Francisco Bay area and San Diego County, presented at 100th Annual Meeting of the Transportation Research Board, Washington, D.C.

Sallard, A., Balac, M., Hörl, S. (2021) Synthetic travel demand for the Greater São Paulo Metropolitan Region, based on open data, Under Review

Sao Paulo, San Francisco Bay area, Los Angeles five-county area, Switzerland, Montreal, Quebec City, Jakarta, Casablanca, ...

Current use cases

  • Latest addition: Cairo
     
  • Germany work-in-progress
     

Replicability

  • 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.

Work in progress

  • Continuously updated
     
  • Example: Integrating buildings
    • Open data: BAN (Base d'addresses nationale)
    • Open data: BD-TOPO building census
       
  • Benchmarking methodology
    • Bayesian networks, HMMs, deep learning, ...

Anthropolis Chair 3

Questions?

https://slides.com/sebastianhorl/smach23