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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, largely overlook public transit users as active, embodied participants, and are rarely designed with accessibility in mind for non-technical users. To address this gap, the following paper presents the detailed specifications of a multi-agent simulation platform designed to support real-time, human-centered, and immersive studies of all road users, accompanied by open-sourced 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 for pedestrians, a seating arrangement for public transit users, a smart trainer for cyclists, and an actuated cockpit for drivers. Additionally, the platform simultaneously collects multimodal physiological, neurological, and behavioral data through embedded sensing devices such as functional near-infrared spectroscopy (fNIRS) for brain activity, eye tracking, and wrist-based biosensors. To show the usability of this system, we present three use cases across various areas of road user research. Simulation for All aims to pave the way for lowering the barrier to entry for high-fidelity transportation simulation, support experimentation across disciplines, and advance our understanding of multimodal mobility in increasingly complex urban environments. The codebase for the platform is available at https://osf.io/6sa8q, enabling replication and adaptation for transportation research applications.
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BTE-Sim: Fast Simulation Environment For Public Transportation
The public commute is essential to all urban centers and is an efficient and environment-friendly way to travel. Transit systems must become more accessible and user-friendly. Since public transit is majorly designed statically, with very few improvements coming over time, it can get stagnated, unable to update itself with changing population trends. To better understand transportation demands and make them more usable, efficient, and demographic-focused, we propose a fast, multi-layered transit simulation that primarily focuses on public transit simulation (BTE-Sim). BTE-Sim is designed based on the population demand, existing traffic conditions, and the road networks that exist in a region. The system is versatile, with the ability to run different configurations of the existing transit routes, or inculcate any new changes that may seem necessary, or even in extreme cases, new transit network design as well. In all situations, it can compare multiple transit networks and provide evaluation metrics for them. It provides detailed data on each transit vehicle, the trips it performs, its on-time performance and other necessary factors. Its highlighting feature is the considerably low computation time it requires to perform all these tasks and provide consistently reliable results.
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- Award ID(s):
- 1952011
- PAR ID:
- 10466149
- Date Published:
- Journal Name:
- 2022 IEEE International Conference on Big Data (Big Data)
- Page Range / eLocation ID:
- 2886 to 2894
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1109/BigData55660.2022.10020973
