情報通信機器とナッジを活用した群衆誘導

大規模な自然災害や人的災害の発生は無視できないものであり，大規模災害の発生を前提としたライフライン設計が求められます．

本研究テーマでは，人流・物流・情報流の観点から災害ライフラインを設計することで，防災ナッジを活用した群衆避難誘導を確立します． 具体的には，以下の研究課題群に取り組んでいます．

Nudge-based Crowd Navigation Using ICT

The increasing occurrence of large-scale natural and man-made disasters underscores the need to design disaster lifelines under the assumption that major disasters can occur. This research aims to establish crowd evacuation guidance by leveraging nudging information and designing disaster lifelines from the perspectives of pedestrian flow, logistics, and information flow. Specifically, this study addresses the following research topics:

  1. T. Hara, M. Sasabe, and S. Kasahara, “Selfish Yet Optimal Routing by Adjusting Perceived Traffic Information of Road Networks,” IEEE Open J. Intell. Transp. Syst., vol. 1, pp. 120–133, 2020, doi: 10.1109/OJITS.2020.3019935. ↩︎ ↩︎

  2. T. Hara, M. Sasabe, and S. Kasahara, “Geographical Risk Analysis Based Path Selection for Automatic, Speedy, and Reliable Evacuation Guiding Using Evacuees’ Mobile Devices,” J Ambient Intell Human Comput, vol. 10, no. 6, pp. 2291–2300, June 2019, doi: 10.1007/s12652-018-0826-z. ↩︎ ↩︎

  3. T. Hara, M. Sasabe, T. Matsuda, and S. Kasahara, “Capacitated Refuge Assignment for Speedy and Reliable Evacuation,” IJGI, vol. 9, no. 7, p. 442, July 2020, doi: 10.3390/ijgi9070442. ↩︎ ↩︎

  4. J. Kawahara, T. Hara, and M. Sasabe, “On Robustness against Evacuees’ Unexpected Movement in Automatic Evacuation Guiding,” Computers and Electrical Engineering, vol. 105, p. 108531, Jan. 2023, doi: 10.1016/j.compeleceng.2022.108531. ↩︎ ↩︎

  5. T. Hara, M. Sasabe, and S. Kasahara, “Multi-Agent Distributed Route Selection under Consideration of Time Dependency among Agents’ Road Usage for Vehicular Networks,” IEICE Trans. Commun., vol. E105.B, no. 2, pp. 140–150, Feb. 2022, doi: 10.1587/transcom.2021CET0001. ↩︎ ↩︎

  6. M. Sasabe, M. Otani, T. Hara, and S. Kasahara, “Path Reachability Including Distance-Constrained Detours,” Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, vol. 238, no. 1, pp. 79–92, Feb. 2024, doi: 10.1177/1748006X221133600. ↩︎ ↩︎

  7. H. Shimizu, T. Hara, and T. Iwata, “Deep Reinforcement Learning for Pedestrian Guidance,” in Proc. of PRIMA 2020: Principles and Practice of Multi-Agent Systems, T. Uchiya, Q. Bai, and I. Marsá Maestre, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2021, pp. 334–342. doi: 10.1007/978-3-030-69322-0_22. ↩︎ ↩︎