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ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems

Author

Listed:
  • Georgios Fragkos

    (Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA)

  • Pavlos Athanasios Apostolopoulos

    (Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA)

  • Eirini Eleni Tsiropoulou

    (Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA)

Abstract

Natural disasters and terrorist attacks pose a significant threat to human society, and have stressed an urgent need for the development of comprehensive and efficient evacuation strategies. In this paper, a novel evacuation-planning mechanism is introduced to support the distributed and autonomous evacuation process within the operation of a public safety system, where the evacuees exploit the capabilities of the proposed ESCAPE service, towards making the most beneficial actions for themselves. The ESCAPE service was developed based on the principles of reinforcement learning and game theory, and is executed at two decision-making layers. Initially, evacuees are modeled as stochastic learning automata that select an evacuation route that they want to go based on its physical characteristics and past decisions during the current evacuation. Consequently, a cluster of evacuees is created per evacuation route, and the evacuees decide if they will finally evacuate through the specific evacuation route at the current time slot or not. The evacuees’ competitive behavior is modeled as a non-co-operative minority game per each specific evacuation route. A distributed and low-complexity evacuation-planning algorithm (i.e., ESCAPE) is introduced to implement both the aforementioned evacuee decision-making layers. Finally, the proposed framework is evaluated through modeling and simulation under several scenarios, and its superiority and benefits are revealed and demonstrated.

Suggested Citation

  • Georgios Fragkos & Pavlos Athanasios Apostolopoulos & Eirini Eleni Tsiropoulou, 2019. "ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems," Future Internet, MDPI, vol. 11(1), pages 1-17, January.
  • Handle: RePEc:gam:jftint:v:11:y:2019:i:1:p:20-:d:198556
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    References listed on IDEAS

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    1. Maurizio Pollino & Grazia Fattoruso & Luigi La Porta & Antonio Bruno Della Rocca & Valentina James, 2012. "Collaborative Open Source Geospatial Tools and Maps Supporting the Response Planning to Disastrous Earthquake Events," Future Internet, MDPI, vol. 4(2), pages 1-18, May.
    2. Huibo Bi, 2014. "Routing Diverse Evacuees with the Cognitive Packet Network Algorithm," Future Internet, MDPI, vol. 6(2), pages 1-20, April.
    3. Qing Han, 2013. "Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm," Future Internet, MDPI, vol. 5(4), pages 1-20, October.
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    Cited by:

    1. Kambombo Mtonga & Santhi Kumaran & Chomora Mikeka & Kayalvizhi Jayavel & Jimmy Nsenga, 2019. "Machine Learning-Based Patient Load Prediction and IoT Integrated Intelligent Patient Transfer Systems," Future Internet, MDPI, vol. 11(11), pages 1-24, November.
    2. Xinyu Huang & Dongming Chen & Dongqi Wang & Tao Ren, 2020. "MINE: Identifying Top- k Vital Nodes in Complex Networks via Maximum Influential Neighbors Expansion," Mathematics, MDPI, vol. 8(9), pages 1-25, August.

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