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
RP
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
RP
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
RP
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
RP
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
RP
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
RP
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
RP
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, ...
RP
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, ...
RP
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
RP
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, ...
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
Synthetic populations: Pipeline
RP
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
Lille
Paris
Strasbourg
Lyon
Toulouse
Bordeaux
Nantes
Rennes
Synthetic populations: Community
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.
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.
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
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
- Making use of the French ENTD 2008 as a first attempt!
- Could be replaced by German survey (but difficulty in accessing it)
Secondary activity locations
- Making use of OpenSteetMap locations
- Proceeding as in the French pipeline
Adaptation to Bavaria
Road network
- OpenSteetMap
GTFS
- Consolidated feed available for Germany!
Rail and metro stops
Adaptation to Bavaria
Adaptation to Bavaria
Adaptation to Bavaria
Validation
- Digitalized all plots from the MiD for Munich
- In lack of the raw data sets
- In lack of the raw data sets
- Comparison between synthetic population / simulation and MiD statistics
Adaptation to Bavaria
Validation
- Digitalized all plots from the MiD for Munich
- In lack of the raw data sets
- In lack of the raw data sets
- Comparison between synthetic population / simulation and MiD statistics
Adaptation to Bavaria
Validation
- Digitalized all plots from the MiD for Munich
- In lack of the raw data sets
- In lack of the raw data sets
- Comparison between synthetic population / simulation and MiD statistics
Adaptation to Bavaria
Validation
- Digitalized all plots from the MiD for Munich
- In lack of the raw data sets
- In lack of the raw data sets
- Comparison between synthetic population / simulation and MiD statistics
...
Adaptation to Bavaria
A new instance of a replicable agent-based transport simulation
Thank you!
sebastian.horl@irt-systemx.fr
Icons throughout the presentation: https://fontawesome.com
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