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． 例えば，2022年と2023年に国内で被害を伴った地震は11件あり，全国の45.6%の市区町村では10年間に10回以上の水害，令和3年度と4年度には17件の重大な電気通信事故が国内で発生していることが報告されています． 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 occurrence of large-scale natural and man-made disasters cannot be ignored, necessitating lifeline design premised on the possibility of major disasters. This research aims to establish crowd evacuation guidance utilizing nudging information by designing disaster lifelines from the perspectives of people flow, logistics, and information flow. Specifically, it 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. ↩︎ ↩︎