Large-scale agent-based transport simulation models for Île-de-France and Bavaria

Sebastian Hörl

14 November 2025

Journée d'étude inter-displinaire Espace & Interaction

The street today

https://commons.wikimedia.org/wiki/File:Atlanta_75.85.jpg

The street of tomorrow?

Julius Bär / Farner

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

Agent-based transport modeling

How to set up agent-based transport simulations?

* with reproducible results

* in a replicable way

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

Households and persons
Primary activity locations
Activity chains
Secondary activity locations

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

Data

Goals

Generate individual households and persons
Choose a distinct place of residence

French population census

Household ID	Person ID	Zone	Age	Sex	...	Weight
512	1	75013	35	f	...	3.2
512	2	75013	32	m	...	3.2
516	1	75019	42	m	...	4.1
...	...		...	...	...	...

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

Synthetic populations: Pipeline

Pipeline

Households and persons
Primary activity locations
Activity chains
Secondary activity locations

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

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

Households and persons
Primary activity locations
Activity chains
Secondary activity locations

Data

Goal

Choose work places and education locations

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

French work
commuting matrix

Synthetic populations: Pipeline

Pipeline

Households and persons
Primary activity locations
Activity chains
Secondary activity locations

Data

Goal

Choose work places and education locations

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

French work
commuting matrix

National enterprise
database

with facilities by number of employees

Synthetic populations: Pipeline

Pipeline

Households and persons
Primary activity locations
Activity chains
Secondary activity locations

Data

Goal

Choose work places and education locations

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

French education
commuting matrix

Permanent facility
database

with education facilities and attendants

Synthetic populations: Pipeline

Pipeline

Households and persons
Primary activity locations
Activity chains
Secondary activity locations

Data

Goal

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

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

Statistical Matching

National Household Travel Survey 2008

(Local Household Travel Surveys)

Synthetic populations: Pipeline

Pipeline

Households and persons
Primary activity locations
Activity chains
Secondary activity locations

Data

Goals

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

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

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

Households and persons
Primary activity locations
Activity chains
Secondary activity locations

Data

Output

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

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

household_id	income	number_of_cars	...
1024	85,000	2	...

household_id	person_id	age	sex	employed	...
1024	1	34	f	true	...
1024	2	36	m	true	...

household_id	person_id	activity_id	start_time	end_time	type	location	...
1024	1	1	00:00	08:00	home	(x, y)	...
1024	1	2	09:00	18:00	work	(x, y)	...
1024	1	3	19:00	24:00	home	(x, y)	...

Synthetic populations: Pipeline

Pipeline

Households and persons
Primary activity locations
Activity chains
Secondary activity locations

Data

Output

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

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

Place of residence

Commuting trips

Hourly work activities

Synthetic populations: Pipeline

Pipeline

Households and persons
Primary activity locations
Activity chains
Secondary activity locations

Data

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

Validation

Comparison with census data, HTS data, ...

Synthetic populations: Pipeline

Pipeline

Households and persons
Primary activity locations
Activity chains
Secondary activity locations

Data

Validation

Comparison with census data, HTS data, ...

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

Synthetic populations: Pipeline

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

BAN

BD-TOPO

MOBPRO

MOBETUD

SIRENE

BPE

ENTD

Open data

Open source

Agent-based simulation

GTFS

OpenStreetMap

Synthetic demand

Driving car

Metro / Train

Work activity starts

Use cases

On-demand mobility systems

How to optimally manage vehicles and drivers?
How benefits from such services?
What are expected costs and requirements?

Use cases

Low-traffic zones

Who is affected in which way?
What is the impact on traffic?

Use cases

Electrification

What are expected charging choices of the population?
Where is demand for infrastructure development?

Lille

Paris

Strasbourg

Lyon

Toulouse

Bordeaux

Nantes

Rennes

Contributors

Users

Synthetic populations: Community

eqasim workshop in November 2025

https://eqasim.org/

Lille

Paris

Strasbourg

Lyon

Toulouse

Bordeaux

Nantes

Rennes

Synthetic populations: Community

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

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

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

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

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

https://hal.science/hal-05012391

Adaptation to Bavaria

Generation of individuals

Replacing the French census data
Available: marginal distributions
- population by age and sex per municipality
- employed population by sex and district (Kreis)
- driving licenses by municipality, sex, age
Tensor from Iterative Proportional Fitting

Home locations

Drawn from Bavarian buildings registry
Avoids unrealistic placements (lakes, forests)

municipality, age group, sex, driving license (yes/no), employed (yes/no)

Density of home locations

Adaptation to Bavaria

Work commuting model

Replaces French commuter matrix (MOBPRO)
Available
- Employed inhabitants by municipality
- Employees working in each municipality
Double-constrained gravity model
- Resistance based on Euclidean distance

Generated commuting locations

Generated commuting relations

Adaptation to Bavaria

Work locations model

Make use of buildings and POIs from OpenStreetMap
- Filtered by specific tags

Density of assigned home locations

Adaptation to Bavaria

Work locations model

Make use of buildings and POIs from OpenStreetMap
- Filtered by specific tags

Education locations model

Make use of buildings and POIs from OpenStreetMap
- Filtered by specific tags
- Assigned by age category (kindergarten, school, ...)

Density of assigned education locations

Adaptation to Bavaria

Enrichment

MiD (Mobilität in Deutschland)
German Household Travel Survey
Replaces certain attributes that are otherwise sampled from the French HTS
Using various plotted distributions from the report to enrich the synthetic population

Belz, Janina. Brand, Thorsten. Eggs, Johannes. Ermes, Bernd. Follmer, Robert. Gruschwitz, Dana. Kellerhoff, Jette. Pirsig, Tim. Roggendorf, Martina (2020): Mobilität in Deutschland – MiD Regionalbericht Stadt München, Münchner Umland und MVV-Verbundraum. Studie von infas, DLR, IVT und infas 360 im Auftrag des Bundesministeriums für Verkehr und digitale Infrastruktur (FE-Nr. 70.904/15). Bonn, Berlin.

Adaptation to Bavaria

Activity chains