Geographical risk analysis based path selection for automatic, speedy, and reliable evacuation guiding using evacuees’ mobile devices

  • Takanori HaraEmail author
  • Masahiro Sasabe
  • Shoji Kasahara
Original Research


It has been highly expected to achieve speedy and reliable evacuation guiding under large scale disasters. As for the speedy evacuation, an automatic evacuation guiding scheme has been proposed, which is a reactive approach based on implicit interactions among evacuees, their mobile devices, and networks. In this scheme, an evacuation route is given by the shortest path, which may not be safe. In this paper, we propose a speedy and reliable path selection based on the geographical risk map for the existing automatic evacuation guiding, which is a proactive approach that allows evacuees to evacuate speedily while avoiding encounters with blocked road segments as much as possible. First, the proposed scheme enumerates candidates of short paths from the evacuee’s current location to the refuge. Then, it selects the most reliable one from the candidates by taking into account road blockage probabilities, each of which is an estimated probability that the corresponding road is blocked under a certain disaster. Through simulation experiments, we show that the proposed scheme can improve the safety of evacuation in terms of the number of encounters with blocked road segments while keeping both the average and maximum evacuation times unchanged, compared with the shortest path selection. We further demonstrate how the proactive function, i.e., geographical risk analysis, and the reactive function, i.e., information sharing, contribute to the system performance.


Geographical risk analysis Automatic evacuation guiding Path selection Path reliability 



This research was partly supported by JSPS KAKENHI Grant Number 15H04008 and 15K00126, Japan.


  1. Ahuja RK, Magnanti TL, Orlin JB (1993) Network flows: theory, algorithms, and applications. Prentice-Hall Inc, Upper Saddle River, NJ, USAzbMATHGoogle Scholar
  2. Akiba T, Hayashi T, Nori N, Iwata Y, Yoshida Y (2015) Efficient top-k shortest-path distance queries on large networks by Pruned Landmark Labeling. In: Proc. of the twenty-ninth AAAI conference on artificial intelligence, AAAI Press, AAAI’15, pp 2–8Google Scholar
  3. Chen A, Yang H, Lo HK, Tang WH (2002) Capacity reliability of a road network: an assessment methodology and numerical results. Transp Res Part B Methodol 36(3):225–252CrossRefGoogle Scholar
  4. Chen X, Kwan MP, Li Q, Chen J (2012) A model for evacuation risk assessment with consideration of pre- and post-disaster factors. Comput Environ Urban Syst 36(3):207–217CrossRefGoogle Scholar
  5. Church RL, Cova TJ (2000) Mapping evacuation risk on transportation networks using a spatial optimization model. Transp Res Part C Emerg Technol 8(16):321–336CrossRefGoogle Scholar
  6. City of Nagoya (2015) Earthquake-resistance city development policy (in Japanese). Accessed 26 Sept 2017
  7. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39CrossRefGoogle Scholar
  8. Fall K (2003) A delay-tolerant network architecture for challenged internets. In: Proc. of SIGCOMM’03, pp 27–34Google Scholar
  9. Fujihara A, Miwa H (2014) Disaster evacuation guidance using opportunistic communication: the potential for opportunity-based service. Big data and internet of things: a roadmap for smart environments studies in computational intelligence. Springer, Cham, pp 425–446Google Scholar
  10. Hara T, Sasabe M, Kasahara S (2017) Short and reliable path selection for automatic evacuation guiding based on interactions between evacuees and their mobile devices. In: Proc. of mobile web and intelligent information systems (MobiWIS 2017), Lecture notes in computer science, vol 10486. Springer, Cham, pp 33–44Google Scholar
  11. Iida Y (1999) Basic concepts and future directions of road network reliability analysis. J Adv Transp 33(2):125–134CrossRefGoogle Scholar
  12. Iizuka Y, Yoshida K, Iizuka K (2011) An effective disaster evacuation assist system utilized by an ad-hoc network. Proc HCI Int 2011:31–35Google Scholar
  13. Kasai Y, Sasabe M, Kasahara S (2017) Congestion-aware route selection in automatic evacuation guiding based on cooperation between evacuees and their mobile nodes. EURASIP J Wirel Commun Netw 1:1–11Google Scholar
  14. Keränen A, Ott J, Kärkkäinen T (2009) The ONE simulator for DTN protocol evaluation. In: Proc. of the 2nd international conference on simulation tools and techniques, pp 55:1–55:10Google Scholar
  15. Komatsu N, Sasabe M, Kawahara J, Kasahara S (2018) Automatic evacuation guiding scheme based on implicit interactions between evacuees and their mobile nodes. GeoInformatica 22(1):127–141CrossRefGoogle Scholar
  16. Lu Q, George B, Shekhar S (2005) Capacity constrained routing algorithms for evacuation planning: a summary of results. In: Proc. of the 9th international conference on advances in spatial and temporal databases, pp 291–307Google Scholar
  17. Ministry of Internal Affairs and Communications (2011) 2011 WHITE PAPER information and communications in Japan. Accessed 26 Sept 2017
  18. Mohammad S, Ali M, Mohammad T (2009) Evacuation planning using multiobjective evolutionary optimization approach. Eur J Oper Res 198(1):305–314CrossRefzbMATHGoogle Scholar
  19. Silva V, Crowley H, Pagani M, Monelli D, Pinho R (2014) Development of the OpenQuake engine, the global earthquake model’s open-source software for seismic risk assessment. Nat Hazards 72(3):1409–1427CrossRefGoogle Scholar
  20. Yen JY (1971) Finding the K shortest loopless paths in a network. Manag Sci 17(11):712–716MathSciNetCrossRefzbMATHGoogle Scholar
  21. Yuan Y, Wang D (2009) Path selection model and algorithm for emergency logistics management. Comput Ind Eng 56(3):1081–1094CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Takanori Hara
    • 1
    Email author
  • Masahiro Sasabe
    • 1
  • Shoji Kasahara
    • 1
  1. 1.Graduate School of Science and TechnologyNara Institute of Science and TechnologyIkomaJapan

Personalised recommendations