matsim-org/matsim-libs

Multi-Agent Transport Simulation

What it solves

MATSim is designed to simulate large-scale agent-based transport systems. It provides a framework for modeling how individuals (agents) move through a transport network, allowing researchers and planners to test different transport scenarios and analyze the output of these simulations.

How it works

The project is organized as a modular toolbox. It includes specific modules for demand-modeling, traffic flow simulation, and re-planning. A controller manages the iterative process of running these simulations, while separate analysis tools are used to process the results. Users can replace standard modules with their own custom implementations to test specific hypotheses.

Who it’s for

This project is intended for transport planners, urban researchers, and developers who need to perform large-scale mobility simulations.

Highlights

  • Large-scale agent-based simulation capabilities
  • Modular architecture allowing forC custom module replacement
  • Integrated tools for demand-modeling and traffic flow
  • Iterative simulation controller for refining results

Related

  • Project
    101dotxyz/GPTeam

    GPTeam is a multi-agent simulation where AI agents collaborate, communicate, and move through a simulated world to achieve common goals.

  • Project
    Farama-Foundation/HighwayEnv

    A collection of Gymnasium-compatible environments for autonomous driving and tactical decision-making tasks.

  • Project
    Zihao-Felix-Zhou/UavNetSim

    A Python-based simulation platform for UAV communication networks that models physical, MAC, and network layers to test routing protocols and mobility models.

  • Project
    agent-sh/agentsys

    A modular runtime and orchestration system for AI agents that automates software development lifecycles through structured pipelines, quality gates, and specialized agent roles.

  • Project
    OpenDriveLab/SimScale

    A scalable simulation pipeline and co-training strategy for end-to-end autonomous driving that synthesizes high-fidelity reactive scenarios to improve model robustness.