Digital Twin-based Driver Risk-Aware Intelligent Mobility Analytics for Urban Transportation Management
<|reference_start|>Digital Twin-based Driver Risk-Aware Intelligent Mobility Analytics for Urban Transportation Management: Traditional mobility management strategies emphasize macro-level mobility oversight from traffic-sensing infrastructures, often overlooking safety risks that directly affect road users. To address this, we propose a Digital Twin-based Driver Risk-Aware Intelligent Mobility Analytics (DT-DIMA) system. The DT-DIMA system integrates real-time traffic information from pan-tilt-cameras (PTCs), synchronizes this data into a digital twin to accurately replicate the physical world, and predicts network-wide mobility and safety risks in real time. The system's innovation lies in its integration of spatial-temporal modeling, simulation, and online control modules. Tested and evaluated under normal traffic conditions and incidental situations (e.g., unexpected accidents, pre-planned work zones) in a simulated testbed in Brooklyn, New York, DT-DIMA demonstrated mean absolute percentage errors (MAPEs) ranging from 8.40% to 15.11% in estimating network-level traffic volume and MAPEs from 0.85% to 12.97% in network-level safety risk prediction. In addition, the highly accurate safety risk prediction enables PTCs to preemptively monitor road segments with high driving risks before incidents take place. Such proactive PTC surveillance creates around a 5-minute lead time in capturing traffic incidents. The DT-DIMA system enables transportation managers to understand mobility not only in terms of traffic patterns but also driver-experienced safety risks, allowing for proactive resource allocation in response to various traffic situations. To the authors' best knowledge, DT-DIMA is the first urban mobility management system that considers both mobility and safety risks based on digital twin architecture.<|reference_end|>
@article{li2024digital,
    title={Digital Twin-based Driver Risk-Aware Intelligent Mobility Analytics for
  Urban Transportation Management},
    author={Tao Li, Zilin Bian, Haozhe Lei, Fan Zuo, Ya-Ting Yang, Quanyan Zhu,
  Zhenning Li, Zhibin Chen, Kaan Ozbay},
    journal={arXiv preprint arXiv:2407.15025},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15025},
    primaryClass={physics.soc-ph cs.LG}
}