Large-scale agent-based simulations of the transport system Replicability and application cases in France

Macroscopic transport modeling

Classic four-step models

• Travel demand generated in (large) zones
• Focus on large flows between these zones
• For the morning or evening commute
• For limited user groups
   
• Question: Where to add capacity?

Macroscopic transport modeling

Classic four-step models

• Travel demand generated in (large) zones
• Focus on large flows between these zones
• For the morning or evening commute
• For limited user groups
   
• Question: Where to add capacity?

Macroscopic transport modeling

Classic four-step models

• Travel demand generated in (large) zones
• Focus on large flows between these zones
• For the morning or evening commute
• For limited user groups
   
• Question: Where to add capacity?

Agent-based transport modeling

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

• Individual travellers with daily activities
• Moving from one activity to another
• Simulation of the entire day
• Highly detailed interaction between travellers and services
   
• Multitude of (design) questions can be answered

Transport modeling chain

Raw data

Synthetic population

Agent-based transport simulation

Use cases

Results

Transport modeling chain

Raw data

Synthetic population

Agent-based transport simulation

Use cases

Results

Synthetic populations: Introduction

Definition

• Representation digital version of the real population of a territory
• Persons (single-level) or households with persons (two-level) population
• Households and persons with individual attributes
• Persons with individual activity chains

0:00 - 8:00

08:30 - 17:00

0:00 - 9:00

10:00 - 17:30

17:45 - 21:00

22:00 - 0:00

17:30 - 0:00

Synthetic populations: Pipeline

Pipeline

1. Households and persons
2. Primary activity locations
3. Activity chains
4. Secondary activity locations

RP

Data

Goals

• Generate individual households and persons
• Choose a distinct place of residence

French population census

Upsampling of persons using Truncate-Replicate-Sample (TRS)

Synthetic populations: Pipeline

Pipeline

1. Households and persons
2. Primary activity locations
3. Activity chains
4. Secondary activity locations

RP

BAN

BD-TOPO

Data

Goals

• Generate individual households and persons
• Choose a distinct place of residence

Sampling by number of housing units per building

French bulding database

French address database

Synthetic populations: Pipeline

Pipeline

1. Households and persons
2. Primary activity locations
3. Activity chains
4. Secondary activity locations

Data

Goal

• Choose work places and education locations

MOBPRO

French work
commuting matrix

Synthetic populations: Pipeline

Pipeline

1. Households and persons
2. Primary activity locations
3. Activity chains
4. Secondary activity locations

Data

Goal

• Choose work places and education locations

MOBPRO

SIRENE

French work
commuting matrix

National enterprise 
database

with facilities by number of employees

Synthetic populations: Pipeline

Pipeline

1. Households and persons
2. Primary activity locations
3. Activity chains
4. Secondary activity locations

Data

Goal

• Choose work places and education locations

MOBETUD

BPE

French education
commuting matrix

Permanent facility
database

with education facilities and attendants

Synthetic populations: Pipeline

Pipeline

1. Households and persons
2. Primary activity locations
3. Activity chains
4. Secondary activity locations

Data

Goal

• Generate activity sequences (type, start and end time) for each person

ENTD

Statistical Matching

National Household Travel Survey 2008

(Local Household Travel Surveys)

Synthetic populations: Pipeline

Pipeline

1. Households and persons
2. Primary activity locations
3. Activity chains
4. Secondary activity locations

Data

Goals

• Choose locations of secondary (shopping, leisure, ...) activities

BPE

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

Synthetic populations: Pipeline

Pipeline

1. Households and persons
2. Primary activity locations
3. Activity chains
4. Secondary activity locations

Data

Output

• Three main tables: households, persons, activities
• Supplementary tables: commutes, trips, ...

RP

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

Synthetic populations: Pipeline

Pipeline

1. Households and persons
2. Primary activity locations
3. Activity chains
4. Secondary activity locations

Data

Output

• Three main tables: households, persons, activities
• Supplementary tables: commutes, trips, ...

RP

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

Place of residence

Synthetic populations: Pipeline

Pipeline

1. Households and persons
2. Primary activity locations
3. Activity chains
4. Secondary activity locations

Data

RP

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

Validation

• Comparison with census data, HTS data, ...

Synthetic populations: Pipeline

Pipeline

1. Households and persons
2. Primary activity locations
3. Activity chains
4. Secondary activity locations

Data

Validation

• Comparison with census data, HTS data, ...

RP

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

Synthetic populations: Pipeline

RP

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

Open data

Open source

+

=

Replicable research in agent-based transport simulation

eqasim-org/ile-de-france

Synthetic populations: Pipeline

RP

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

Open data

Open source

+

=

Synthetic populations: Community

Lille

Paris

Strasbourg

Lyon

Toulouse

Bordeaux

Nantes

Rennes

Contributors

Users

eqasim workshop in November 2025

https://eqasim.org/

Synthetic populations: Community

https://hal.science/hal-05018081v1

Lille

Paris

Strasbourg

Lyon

Toulouse

Bordeaux

Nantes

Rennes

Synthetic populations: Adaptations

Screenshot Sao Paolo

Copy & paste of the code base

Difficulty of maintenance

São Paulo

Almost same open data available as in France

California

Substantial modifiations required

Switzerland

Not based on open data
(for now)

Paper published in
Regional Studies, Regional Science (2020)

Paper presented at the Annual Meeting of the Transportation Research Board (2021)

Work in progress at ETH Zurich

Synthetic populations: Adaptations

Cairo: Extreme case, very few data available and not in the right format

Idea: Use data to generate "fake" input to the French pipeline and reuse the code!

Gall, T., Vallet, F., Reyes Madrigal, L.M., Hörl, S., Abdin, A., Chouaki, T., Puchinger, J., 2023. Sustainable Urban Mobility Futures, Sustainable Urban Futures. Springer Nature Switzerland, Cham.

Synthetic populations: Adaptations

Cairo: Extreme case, very few data available and not in the right format

Idea: Use data to generate "fake" input to the French pipeline and reuse the code!

Bavaria: Set up a robust and replicable pipeline with data replacement

Hörl, S., Burianne, A., Natterer, E., Engelhardt, R., Müller, J. (2025) Towards a replicable synthetic population and agent-based transport model for Bavaria, paper presented at the 23rd International Conference on Practical applications of Agents and Multi-Agent Systems (PAAMS 2025), June 2025, Lille, France.

As part of the national project MINGA

Synthetic populations: Adaptations

Cairo: Extreme case, very few data available and not in the right format

Idea: Use data to generate "fake" input to the French pipeline and reuse the code!

Bavaria: Set up a robust and replicable pipeline with data replacement

As part of the national project MINGA

Hörl, S., Burianne, A., Natterer, E., Engelhardt, R., Müller, J. (2025) Towards a replicable synthetic population and agent-based transport model for Bavaria, paper presented at the 23rd International Conference on Practical applications of Agents and Multi-Agent Systems (PAAMS 2025), June 2025, Lille, France.

eqasim-org/bavaria

Transport modeling chain

Raw data

Synthetic population

Agent-based transport simulation

Use cases

Results

Transport modeling chain

Raw data

Synthetic population

Agent-based transport simulation

Use cases

Results

Agent-based simulation: Introduction

GTFS

OpenStreetMap

Synthetic demand

+

Driving car

Metro / Train

Work activity starts

Agent-based simulation: Introduction

Synthetic demand

Agent-based simulation: Introduction

Mobility simulation

Synthetic demand

Daily mobility plans

Agent-based simulation: Introduction

Decision-making

Mobility simulation

Synthetic demand

Experienced travel times, crowding, ...

Daily mobility plans

Agent-based simulation: Introduction

Decision-making

Mobility simulation

Synthetic demand

Experienced travel times, crowding, ...

Daily mobility plans

Agent-based simulation: Introduction

Decision-making

Mobility simulation

Synthetic demand

Experienced travel times, crowding, ...

Daily mobility plans

Update

Agent-based simulation: Introduction

Decision-making

Mobility simulation

Synthetic demand

• Maintained by TU Berlin, ETH Zurich, (IRT SystemX)
• 50+ research users world-wide, SBB, Volkswagen, ...

• Contributor since ~2016

Mode shares

Traffic patterns

Emissions

Noise

Agent-based simulation: eqasim-java

eqasim-java: A streamlined set-up of MATSim for our standardized synthetic populations

Agent-based simulation: Calibration

Capacity calibration

* not the latest results

Agent-based simulation: Calibration

Capacity calibration

* not the latest results

Agent-based simulation: Calibration

Capacity calibration

* not the latest results

Agent-based simulation: Calibration

Capacity calibration

* not the latest results

Transport modeling chain

Raw data

Synthetic population

Agent-based transport simulation

Use cases

Results

Transport modeling chain

Raw data

Synthetic population

Agent-based transport simulation

Use cases

Results

Automated taxi

Pickup

Dropoff

Use cases: On-demand mobility

• An operator centrally controls a fleet of vehicles
   
• Each vehicle is represented as an agent that receives instructions in each time step
   
• Customer agents sent requests to be transported
   
• Objectives: maximize operator revenue, minimize empty distance, ...

Automated taxi

Pickup

Dropoff

Use cases: On-demand mobility

• An operator centrally controls a fleet of vehicles
   
• Each vehicle is represented as an agent that receives instructions in each time step
   
• Customer agents sent requests to be transported
   
• Objectives: maximize operator revenue, minimize empty distance, ...

R1

Use cases: On-demand mobility

• Different dispatching strategies provide different outcomes in terms of empty distance, revenue, and wait times

amodeus-science/amodeus

AI Driving Olympics challenge at NeurIPS 2018

Use cases: On-demand mobility

Cost model

Discrete choice model

Mobility simulation

Estimation

Fare per trip and km

Wait time

Outcomes

• The problem becomes even more interesting when customer agents have the choice to use the service or not (dynamic demand)

Use cases: On-demand mobility

• Provides an understanding of a mobility service that doesn't exist today
   
• Shows pathways for policy and regulation

Hörl, S., Becker, F., & Axhausen, K. W. (2021). Simulation of price, customer behaviour and system impact for a cost-covering automated taxi system in Zurich. Transportation Research Part C: Emerging Technologies, 123, 102974.

Use cases: On-demand mobility

Hörl, S., Balac, M., & Axhausen, K. W. (2019). Dynamic demand estimation for an AMoD system in Paris. IEEE Intelligent Vehicles Symposium (IV 2019), 260–266.

On-demand mobility: Intermodality

• How to combine on-demand mobility systems with public transport?
  
   
  
  
  
• How to take into account rejection rates in discrete choice models?
  
  
  
  
  
  
• Various other publications ...

Chouaki, T., Hörl, S., Puchinger, J., 2023. Towards Reproducible Simulations of the Grand Paris Express and On-Demand Feeder Services, in: 102nd Annual Meeting of the Transportation Research Board (TRB 2023). Washington D.C, United States.

Chouaki, T., Hörl, S., Puchinger, J., 2023. Control-based integration of rejection rates into endogenous demand ride-pooling simulations, in: 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS 2023). IEEE, Nice, France, pp. 1–6.

• How to combine on-demand mobility systems with public transport?
  
   
  
   
• How to take into account rejection rates in discrete choice models?
  
  
  
  
   
• Various other publications ...

Chouaki, T., Hörl, S., Puchinger, J., 2023. Towards Reproducible Simulations of the Grand Paris Express and On-Demand Feeder Services, in: 102nd Annual Meeting of the Transportation Research Board (TRB 2023). Washington D.C, United States.

Chouaki, T., Hörl, S., Puchinger, J., 2023. Control-based integration of rejection rates into endogenous demand ride-pooling simulations, in: 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS 2023). IEEE, Nice, France, pp. 1–6.

On-demand mobility: Intermodality

• How to combine on-demand mobility systems with public transport?
  
   
  
   
• How to take into account rejection rates in discrete choice models?
  
  
  
  
   
• Various other publications ...

Chouaki, T., Hörl, S., Puchinger, J., 2023. Towards Reproducible Simulations of the Grand Paris Express and On-Demand Feeder Services, in: 102nd Annual Meeting of the Transportation Research Board (TRB 2023). Washington D.C, United States.

Chouaki, T., Hörl, S., Puchinger, J., 2023. Control-based integration of rejection rates into endogenous demand ride-pooling simulations, in: 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS 2023). IEEE, Nice, France, pp. 1–6.

On-demand mobility: Intermodality

On-demand mobility: Algorithmic fairness

Do dispatching algorithms discriminate against certain user groups?

• Standard algorithms aim at minimizing wait times, travel times and maximizing revenue
   
• Do standard algorithms reject mobility-impaired person with longer interactions or larger groups more frequently than others?
   
• Yes, they do!
  
  
  
  
   
• Can we mitigate the problem?
   
• Opens a whole new section of research in fleet management

Chouaki, T., Hörl, S., 2024. Comparative assessment of fairness in on-demand fleet management algorithms, in: The 12th Symposium of the European Association for Research in Transportation (hEART). Espoo, Finland.

Infrastructure: Charging behaviour

How do people choose between public, home and work chargers for their electric cars?
 

• Very few data available (surveys and use)
   
• Idea 
  - Assign electric vehicles to the population, then force them to charge (to avoid zero SoC)
  - What is their ideal charging configuration, given the provided infrastructure?
   
• Collective charging strategy selection process (home, work, public) through maximization of scores
   
• Negative scores for zero SoC, falling below a minimum SoC during the day or at the end, monetary costs, ...

Infrastructure: Charging behaviour

How do people choose between public, home and work chargers for their electric cars?
 

• Very few data available (surveys and use)
   
• Idea 
  - Assign electric vehicles to the population, then force them to charge (to avoid zero SoC)
  - What is their ideal charging configuration, given the provided infrastructure?
   
• Collective charging strategy selection process (home, work, public) through maximization of scores
   
• Negative scores for zero SoC, falling below a minimum SoC during the day or at the end, monetary costs, ...

Transport policy: Limited traffic zones

What is the impact of the Limited Traffic Zone in the center of Paris?
 

• Rule: Non-residents that are not performing an activity in the center of Paris are not allowed to go through the center zone
   
• We can analyze which persons (agents) are affected by that policy
   
• We can measure the impact of the policy on the surrounding traffic
   
• High level estimation of traffic and emission impact

Residents

Transit

Visitors

Transport policy: Limited traffic zones

What is the impact of the Limited Traffic Zone in the center of Paris?
 

• Rule: Non-residents that are not performing an activity in the center of Paris are not allowed to go through the center zone
   
• We can analyze which persons (agents) are affected by that policy
   
• We can measure the impact of the policy on the surrounding traffic
   
• High level estimation of traffic and emission impact

Transport policy: Limited traffic zones

What is the impact of the Limited Traffic Zone in the center of Paris?
 

• Rule: Non-residents that are not performing an activity in the center of Paris are not allowed to go through the center zone
   
• We can analyze which persons (agents) are affected by that policy
   
• We can measure the impact of the policy on the surrounding traffic
   
• High level estimation of traffic and emission impact

Overall flow related to the ZTL

Transport policy: Limited traffic zones

Transit flow related to the ZTL

What is the impact of the Limited Traffic Zone in the center of Paris?
 

• Rule: Non-residents that are not performing an activity in the center of Paris are not allowed to go through the center zone
   
• We can analyze which persons (agents) are affected by that policy
   
• We can measure the impact of the policy on the surrounding traffic
   
• High level estimation of traffic and emission impact

Transport policy: Limited traffic zones

Transit flow related to the ZTL

Difference after introduction of ZTL

What is the impact of the Limited Traffic Zone in the center of Paris?
 

• Rule: Non-residents that are not performing an activity in the center of Paris are not allowed to go through the center zone
   
• We can analyze which persons (agents) are affected by that policy
   
• We can measure the impact of the policy on the surrounding traffic
   
• High level estimation of traffic and emission impact

Transport policy: Parcel deliveries

A detailed study of environmental policies on parcel deliveries
 

• Obtaining a daily synthetic parcel demand based on a synthetic population for Lyon and statistics (Gardrat)
   
• Identifying all logistics centers in the area
   
• Cost structures (vehicles, drivers, operational) for ICVs and BEVs (small, medium, large)
   
• Definition of one Heterogeneous Fleet VRP per logistics center, sensitive to cost inputs
   
• Testing of CO2 tax, ICV tax, qualitative policies

Transport policy: Parcel deliveries

A detailed study of environmental policies on parcel deliveries
 

• Obtaining a daily synthetic parcel demand based on a synthetic population for Lyon and statistics (Gardrat)
   
• Identifying all logistics centers in the area
   
• Cost structures (vehicles, drivers, operational) for ICVs and BEVs (small, medium, large)
   
• Definition of one Heterogeneous Fleet VRP per logistics center, sensitive to cost inputs
   
• Testing of CO2 tax, ICV tax, qualitative policies

Hörl, S., Briand, Y., & Puchinger, J. (2025). Decarbonization policies for last-mile parcels: A replicable open-data case study for Lyon. Transportation Research Part D: Transport and Environment, 146, 104893.

Communication: Interface development

• Ambition to provide project results through interactive interfaces

Raw data

Synthetic population

Agent-based transport simulation

Use cases

Results

Transport modeling chain

Raw data

Synthetic population

Agent-based transport simulation

Use cases

Results

Replicability?

Raw data

Synthetic population

Agent-based transport simulation

Use cases

Results

Replicability?

Yes, eqasim-synpop for France and a handful of other cases.

Working on generalizing the methodology.

Raw data

Synthetic population

Agent-based transport simulation

Use cases

Results

Replicability?

Yes, eqasim-synpop for France and a handful of other cases.

Working on generalizing the methodology.

Partly, eqasim-java is accessible. Goal to publish a calibrated fully replicable simulation for Île-de-France in the coming months.

Raw data

Synthetic population

Agent-based transport simulation

Use cases

Results

Replicability?

Yes, eqasim-synpop for France and a handful of other cases.

Working on generalizing the methodology.

Partly, eqasim-java is accessible. Goal to publish a calibrated fully replicable simulation for Île-de-France in the coming months.

Using the new baseline simulation, our goal is to publish upcoming studies in a fully replicable way.

Outlook: Surrogate modeling

Natterer, E. S., Rao, S. R., Tejada Lapuerta, A., Engelhardt, R., Hörl, S., & Bogenberger, K. (2025). Machine learning surrogates for agent-based models in transportation policy analysis. Transportation Research Part C: Emerging Technologies, 180, 105360.

Thank you!

sebastian.horl@irt-systemx.fr

Icons throughout the presentation: https://fontawesome.com

https://sebhoerl.github.io/

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