eqasim update + demo
latest features & developments

Sebastian Hörl

12 June 2026

Journées populations synthétiques 2026

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

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

https://eqasim.org/

Synthetic populations: Community

Lille

Paris

Strasbourg

Lyon

Toulouse

Bordeaux

Nantes

Rennes

https://eqasim.org/

Mattermost

Framateam Population Synthétique

Channel Groupe suivi eqasim

Annual eqasim workshop

60 participants in December 2025

Monthly exchange

Connection link on Framateam

Discussion on latest developments, projects, ...

Synthetic populations: Community

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

Lille

Paris

Strasbourg

Lyon

Toulouse

Bordeaux

Nantes

Rennes

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

GTFS

OpenStreetMap

Synthetic demand

+

Driving car

Metro / Train

Work activity starts

Agent-based simulation

Recent updates

Integration with MobiSurvStd

• Package that converts any schedule in France (EGT, EDGT, EMC2, ...) to a standardized format in parquet
   
• Developed by Lucas Javaudin (CY Cergy > LVMT)
   
• Integrated into eqasim
  • Replaces individual code blocks for different EDGT, EMC2, ...
  • Easy to set up in config.yml

MobiSurvStd/MobiSurvStd

Force field in secondary location assignment

• Current secondary location approach only ensures correct Euclidean distances between activities
   
• Added a force field that drags secondary activities to zones of higher opportunity density
  
  
  
   
• Current development at tellae: Define specific attraction factors for different shop sizes (surface, type) in BPE

Langrognet, P.-A., Côme, É., Hörl, S., & Oukhellou, L. (2026). Improving the spatial distribution of secondary activities in synthetic populations through guidance forces. Computers, Environment and Urban Systems, 127, 102431. https://doi.org/10.1016/j.compenvurbsys.2026.102431

feat: force field and weighted locations in secondary location assignment #482

EMG 2023 and weekly schedules

Objective 1: Integrate EMG 2023 (Institut Paris Region) into eqasim

• EMG 2023 is a weekly schedule: 3,300 persons (Monday to Sunday)
• Ask IPR for access: They want to share and make it used as much as possible!
   
• Step 1: Integrated EMG into MobiSurvStd
  • Now gives us a standardized version that is compatible with other HTS in France
     
• Step 2: Adapt it to eqasim (almost done)

feat: supporting week surveys (like emg 2023) #481

EMG 2023 and weekly schedules

Three options of using a weekly schedule:

1. Generate weekly activity schedules
2. Sample an individual day for each person (attention: weekend!)
3. Duplicate each person 7x (or 5x) to inflate the data set

EMG 2023 and weekly schedules

Adaptation for existing (daily) surveys

• They already contain information on the survey weekday (ENTD, EMC2, ...)
• We can select only observations for a specific weekday: gives us a Wednesday, Thursday etc. population

feat: filter activity chains by weekday #474

EMG 2023 and weekly schedules

Adaptation for existing (daily) surveys

• They already contain information on the survey weekday (ENTD, EMC2, ...)
• We can select only observations for a specific weekday: gives us a Wednesday, Thursday etc. population

feat: filter activity chains by weekday #474

Use cases

France-wide synthetic population

Support for DOM

Tarek Chouaki

IRT SystemX

Quick demo

Demo & automatic data provision

1. First steps to set up an eqasim population for Île-de-France
    
2. Automatic data downloading
   
   
    
3. Configuration for Toulouse :)

feat: utility scripts for automatic data download #500

Next steps

Next steps for eqasim

Extension to other geographical contexts

• Following the approach for Cairo, Bavaria, ...
• Leading to a standardized library of synthetic populations?

Next steps for eqasim

Modularization of the framework

• Migrate form a rigid structure to multiple approaches
  • Machine learning, deep learning, LLM
  • For different steps (population, activity chains, location assignment, ...)
• Position as a benchmarking framework for integrated synthetic populations

Extension to other geographical contexts

• Following the approach for Cairo, Bavaria, ...
• Leading to a standardized library of synthetic populations?

Next steps for eqasim

Use cases beyond transport

• Energy systems
• Health systems, epidemics
• Climate change adaption, resilience, risk

Modularization of the framework

• Migrate form a rigid structure to multiple approaches
  • Machine learning, deep learning, LLM
  • For different steps (population, activity chains, location assignment, ...)
• Position as a benchmarking framework for integrated synthetic populations

Extension to other geographical contexts

• Following the approach for Cairo, Bavaria, ...
• Leading to a standardized library of synthetic populations?

Thank you!

sebastian.horl@irt-systemx.fr

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

https://sebhoerl.github.io/

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