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| Main Authors: | , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2507.09367 |
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| _version_ | 1866918092502728704 |
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| author | Azimi, Shiva Tavakoli, Arash |
| author_facet | Azimi, Shiva Tavakoli, Arash |
| contents | As cities evolve toward more complex and multimodal transportation systems, the need for human-centered multi-agent simulation tools has never been more urgent. Yet most existing platforms remain limited - they often separate different types of road users, rely on scripted or pre-defined behaviors, overlook public transit users as active participants, and are rarely designed with accessibility in mind for non-technical users. To address this gap, this paper presents the specifications of a multi-agent simulation platform designed to support real-time, human-centered, and immersive studies of all road users, accompanied by open-source scripts for replication. Using high-fidelity immersive virtual environments, our platform enables interaction across public transit users, pedestrians, cyclists, automated vehicles, and drivers. The architecture is modular, extensible, and designed for accessibility. The system integrates hardware-specific modules - including an omnidirectional treadmill, a seating arrangement, a smart trainer, and an actuated cockpit. Additionally, the platform collects multimodal physiological, neurological, and behavioral data through embedded sensing devices such as functional near-infrared spectroscopy (fNIRS), eye tracking, and wrist-based biosensors. To show the usability of this system, we present three use cases. Simulation for All aims to lower the barrier to entry for high-fidelity transportation simulation, support experimentation across disciplines, and advance our understanding of multimodal mobility in complex urban environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09367 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Simulation for All: A Step-by-Step Cookbook for Developing Human-Centered Multi-Agent Transportation Simulators Azimi, Shiva Tavakoli, Arash Multiagent Systems As cities evolve toward more complex and multimodal transportation systems, the need for human-centered multi-agent simulation tools has never been more urgent. Yet most existing platforms remain limited - they often separate different types of road users, rely on scripted or pre-defined behaviors, overlook public transit users as active participants, and are rarely designed with accessibility in mind for non-technical users. To address this gap, this paper presents the specifications of a multi-agent simulation platform designed to support real-time, human-centered, and immersive studies of all road users, accompanied by open-source scripts for replication. Using high-fidelity immersive virtual environments, our platform enables interaction across public transit users, pedestrians, cyclists, automated vehicles, and drivers. The architecture is modular, extensible, and designed for accessibility. The system integrates hardware-specific modules - including an omnidirectional treadmill, a seating arrangement, a smart trainer, and an actuated cockpit. Additionally, the platform collects multimodal physiological, neurological, and behavioral data through embedded sensing devices such as functional near-infrared spectroscopy (fNIRS), eye tracking, and wrist-based biosensors. To show the usability of this system, we present three use cases. Simulation for All aims to lower the barrier to entry for high-fidelity transportation simulation, support experimentation across disciplines, and advance our understanding of multimodal mobility in complex urban environments. |
| title | Simulation for All: A Step-by-Step Cookbook for Developing Human-Centered Multi-Agent Transportation Simulators |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2507.09367 |