# An Overview of Agent-based Traffic Simulators

arXiv: https://arxiv.org/abs/2102.07505 · HTML: https://mlsanity.com/paper/2102.07505/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2021.

## Built on by (most cited first, 11 of 11)

- [ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObility](https://mlsanity.com/paper/2409.09040/) (2024)
- [Building Transportation Foundation Model via Generative Graph Transformer](https://mlsanity.com/paper/2305.14826/) (2023)
- [TransWorldNG: Traffic Simulation via Foundation Model](https://mlsanity.com/paper/2305.15743/) (2023)
- [TravelAgent: Generative Agents in the Built Environment](https://mlsanity.com/paper/2412.18985/) (2024)
- [Return-Aligned Decision Transformer](https://mlsanity.com/paper/2402.03923/) (2024)
- [Beyond Words: Evaluating Large Language Models in Transportation Planning](https://mlsanity.com/paper/2409.14516/) (2024)
- [Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning](https://mlsanity.com/paper/2412.03925/) (2024)
- [AL-SPCE - Reliability analysis for nondeterministic models using stochastic polynomial chaos expansions and active learning](https://mlsanity.com/paper/2507.04553/) (2025)
- [GTA: Generative Traffic Agents for Simulating Realistic Mobility Behavior](https://mlsanity.com/paper/2601.16778/) (2026)
- [A methodological framework for Resilience as a Service (RaaS) in multimodal urban transportation networks](https://mlsanity.com/paper/2408.17233/) (2024)
- [A Resilience-as-a-Service assessment framework for coordinated disruption response in interdependent urban transit systems](https://mlsanity.com/paper/2606.08849/) (2026)

## Abstract (arXiv)

Individual traffic significantly contributes to climate change and environmental degradation. Therefore, innovation in sustainable mobility is gaining importance as it helps to reduce environmental pollution. However, effects of new ideas in mobility are difficult to estimate in advance and strongly depend on the individual traffic participants. The application of agent technology is particularly promising as it focuses on modelling heterogeneous individual preferences and behaviours. In this paper, we show how agent-based models are particularly suitable to address three pressing research topics in mobility: 1. Social dilemmas in resource utilisation; 2. Digital connectivity; and 3. New forms of mobility. We then explain how the features of several agent-based simulators are suitable for addressing these topics. We assess the capability of simulators to model individual travel behaviour, discussing implemented features and identifying gaps in functionality that we consider important.