An Overview of Agent-based Traffic Simulators
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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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Feb 2021.
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Citing papers per year: 2023: 2; 2024: 6; 2025: 1; 2026: 2.
11 papers.
- 2024ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObilitycited by 34
- 2023Building Transportation Foundation Model via Generative Graph Transformercited by 19
- 2023TransWorldNG: Traffic Simulation via Foundation Modelcited by 9
- 2024TravelAgent: Generative Agents in the Built Environmentcited by 5
- 2024Return-Aligned Decision Transformercited by 3
- 2024Beyond Words: Evaluating Large Language Models in Transportation Planningcited by 3
- 2024Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learningcited by 1
- 2025AL-SPCE - Reliability analysis for nondeterministic models using stochastic polynomial chaos expansions and active learningcited by 1
- 2026GTA: Generative Traffic Agents for Simulating Realistic Mobility Behaviorcited by 1
- 2024A methodological framework for Resilience as a Service (RaaS) in multimodal urban transportation networkscited by 0
- 2026A Resilience-as-a-Service assessment framework for coordinated disruption response in interdependent urban transit systemscited by 0
- 2026A Resilience-as-a-Service assessment framework for coordinated disruption response in interdependent urban transit systems7 Jun 2026
- 2026GTA: Generative Traffic Agents for Simulating Realistic Mobility Behavior23 Jan 2026
- 2025AL-SPCE - Reliability analysis for nondeterministic models using stochastic polynomial chaos expansions and active learning6 Jul 2025
- 2024TravelAgent: Generative Agents in the Built Environment25 Dec 2024
- 2024Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning5 Dec 2024
- 2024Beyond Words: Evaluating Large Language Models in Transportation Planning22 Sep 2024
- 2024A methodological framework for Resilience as a Service (RaaS) in multimodal urban transportation networks30 Aug 2024
- 2024ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObility29 Aug 2024
- 2024Return-Aligned Decision Transformer6 Feb 2024
- 2023TransWorldNG: Traffic Simulation via Foundation Model25 May 2023
- 2023Building Transportation Foundation Model via Generative Graph Transformer24 May 2023