Modeling realistic human behavior using generative agents in a multimodal transport system: Software architecture and Application to Toulouse

• URL: http://arxiv.org/abs/2510.19497v1
• Date: Wed, 22 Oct 2025 11:45:44 GMT
• Title: Modeling realistic human behavior using generative agents in a multimodal transport system: Software architecture and Application to Toulouse
• Authors: Trung-Dung Vu, Benoit Gaudou, Kamaldeep Singh Oberoi,
• Abstract summary: This paper presents an architecture for modeling realistic human mobility behavior in complex multimodal transport systems.<n>We apply Large Language Models (LLMs) within an agent-based simulation to capture decision-making in a real urban setting.<n>Result show that agents not only make context-aware transport decisions but also form habits over time.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modeling realistic human behaviour to understand people's mode choices in order to propose personalised mobility solutions remains challenging. This paper presents an architecture for modeling realistic human mobility behavior in complex multimodal transport systems, demonstrated through a case study in Toulouse, France. We apply Large Language Models (LLMs) within an agent-based simulation to capture decision-making in a real urban setting. The framework integrates the GAMA simulation platform with an LLM-based generative agent, along with General Transit Feed Specification (GTFS) data for public transport, and OpenTripPlanner for multimodal routing. GAMA platform models the interactive transport environment, providing visualization and dynamic agent interactions while eliminating the need to construct the simulation environment from scratch. This design enables a stronger focus on developing generative agents and evaluating their performance in transport decision-making processes. Over a simulated month, results show that agents not only make context-aware transport decisions but also form habits over time. We conclude that combining LLMs with agent-based simulation offers a promising direction for advancing intelligent transportation systems and personalised multimodal mobility solutions. We also discuss some limitations of this approach and outline future work on scaling to larger regions, integrating real-time data, and refining memory models.

Related papers

• MR-LDM -- The Merge-Reactive Longitudinal Decision Model: Game Theoretic Human Decision Modeling for Interactive Sim Agents [0.9883562565157391]
  We aim to improve the simulation of the highway merge scenario by targeting a game theoretic model for tactical decision-making.<n>We couple this with an underlying dynamics model to have a unified decision and dynamics model that can capture more realistic interactions.
  arXiv  Detail & Related papers  (2025-07-15T20:41:00Z)
• The Sound of Simulation: Learning Multimodal Sim-to-Real Robot Policies with Generative Audio [138.07247714782412]
  MultiGen is a framework that integrates large-scale generative models into traditional physics simulators.<n>We demonstrate effective zero-shot transfer to real-world pouring with novel containers and liquids.
  arXiv  Detail & Related papers  (2025-07-03T17:59:58Z)
• MobiVerse: Scaling Urban Mobility Simulation with Hybrid Lightweight Domain-Specific Generator and Large Language Models [11.90100976089832]
  We propose MobiVerse, a hybrid framework to bridge the gap in mobility simulation.<n>A case study was conducted in Westwood, Los Angeles, where we efficiently generated and dynamically adjusted schedules.<n>Results show our approach maintains computational efficiency while enhancing behavioral realism.
  arXiv  Detail & Related papers  (2025-06-26T21:46:18Z)
• CAMS: A CityGPT-Powered Agentic Framework for Urban Human Mobility Simulation [9.907406552578607]
  textbfCAMS is an agentic framework that leverages the language based urban foundation model to simulate human mobility in urban space.<n>textbfCAMS achieves superior performance without relying on externally provided geospatial information.
  arXiv  Detail & Related papers  (2025-06-16T15:24:07Z)
• MobileCity: An Efficient Framework for Large-Scale Urban Behavior Simulation [24.339278159507035]
  We present MobileCity, a lightweight simulation platform designed to model realistic urban mobility with high computational efficiency.<n>We introduce a comprehensive transportation system with multiple transport modes, and collect questionnaire data from respondents to construct agent profiles.<n>To enable scalable simulation, agents perform action selection within a pre-generated action space and uses local models for efficient agent memory generation.
  arXiv  Detail & Related papers  (2025-04-18T07:01:05Z)
• TrajLLM: A Modular LLM-Enhanced Agent-Based Framework for Realistic Human Trajectory Simulation [3.8106509573548286]
  This work leverages Large Language Models (LLMs) to simulate human mobility, addressing challenges like high costs and privacy concerns in traditional models.<n>Our hierarchical framework integrates persona generation, activity selection, and destination prediction, using real-world demographic and psychological data.
  arXiv  Detail & Related papers  (2025-02-26T00:13:26Z)
• A Survey of World Models for Autonomous Driving [55.520179689933904]
  Recent breakthroughs in autonomous driving have been propelled by advances in robust world modeling.<n>World models offer high-fidelity representations of the driving environment that integrate multi-sensor data, semantic cues, and temporal dynamics.<n>Future research must address key challenges in self-supervised representation learning, multimodal fusion, and advanced simulation.
  arXiv  Detail & Related papers  (2025-01-20T04:00:02Z)
• Probing Multimodal LLMs as World Models for Driving [72.18727651074563]
  We look at the application of Multimodal Large Language Models (MLLMs) in autonomous driving. Despite advances in models like GPT-4o, their performance in complex driving environments remains largely unexplored.
  arXiv  Detail & Related papers  (2024-05-09T17:52:42Z)
• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [149.5716746789134]
  We show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
  arXiv  Detail & Related papers  (2023-03-07T18:28:41Z)
• TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
  We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation. In particular, we leverage an implicit latent variable model to parameterize a joint actor policy. We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
  arXiv  Detail & Related papers  (2021-01-17T00:29:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.