Agent-based transport models

Sebastian Hörl

10 January 2025

Passport@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

Agent-based transport simulation

Synthetic travel demand

> Truncate-Replicate-Sample (TRS)

Synthetic travel demand

> Imputation by quantile

Synthetic travel demand

> Direct sampling from OD matrix

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

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

Person ID    

Age

Gender

Home (X,Y)

1

43

male

(65345, ...)

2

24

female

(65345, ...)

3

9

female

(65345, ...)

Synthetic travel demand

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

Synthetic travel demand

Synthetic travel demand

Synthetic travel demand

Synthetic travel demand

+

=

Synthetic travel demand

+

=

Evaluation

• Comparison of population attributes

Evaluation

• Activity chains

Sampling

Current use cases

Current use cases

Current use cases

Current use cases

Current use cases

Current use cases

Current use cases

Current use cases

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

Current use cases

• Thesis proejct of Tjark Gall
  Cairo

Current use cases

• Recent project with TU Munich
  
  Munich

Current use cases

• Recent project with TU Munich
  
  Munich

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.

Projects

Projects

Projects

Questions?

https://slides.com/sebastianhorl/passportx25