Practical approach to evacuation planning via network flow and deep learning

Akira Tanaka, Nozomi Hata, Nariaki Tateiwa, Katsuki Fujisawa

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    Abstract

    In this paper, we propose a practical approach to evacuation planning by utilizing network flow and deep learning algorithms. In recent years, large amounts of data are rapidly being stored in the cloud system, and effective data utilization for solving real-world problems is required more than ever. Hierarchical Data Analysis and Optimization System (HDAOS) enables us to select appropriate algorithms according to the degree of difficulty in solving problems and a given time for the decision-making process, and such selection helps address real-world problems. In the field of emergency evacuation planning, however, the Lexicographically Quickest Flow (LQF) algorithm has an extremely long computation time on a large-scale network, and is therefore not a practical solution. For Osaka city, which is the second-largest city in Japan, we must solve the maximum flow problems on a large-scale network with over 8.3M nodes and 32.8M arcs for obtaining an optimal plan. Consequently, we can feed back nothing to make an evacuation plan. To solve the problem, we utilize the optimal solution as training data of a deep Convolutional Neural Network (CNN). We train a CNN by using the results of the LQF algorithm in normal time, and in emergencies predict the evacuation completion time (ECT) immediately by the well-learned CNN. Our approach provides almost precise ECT, achieving an average regression error of about 2%. We provide several techniques for combining LQF with CNN and addressing numerous movements as CNN's input, which has rarely been considered in previous studies. Hodge decomposition also demonstrates that LQF is efficient from the standpoint of the total distance traveled by all evacuees, which reinforces the validity of the method of utilizing the LQF algorithm for deep learning.

    Original languageEnglish
    Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
    EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages3368-3377
    Number of pages10
    ISBN (Electronic)9781538627143
    DOIs
    Publication statusPublished - Jul 1 2017
    Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
    Duration: Dec 11 2017Dec 14 2017

    Publication series

    NameProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
    Volume2018-January

    Other

    Other5th IEEE International Conference on Big Data, Big Data 2017
    CountryUnited States
    CityBoston
    Period12/11/1712/14/17

    All Science Journal Classification (ASJC) codes

    • Computer Networks and Communications
    • Hardware and Architecture
    • Information Systems
    • Information Systems and Management
    • Control and Optimization

    Fingerprint Dive into the research topics of 'Practical approach to evacuation planning via network flow and deep learning'. Together they form a unique fingerprint.

  • Cite this

    Tanaka, A., Hata, N., Tateiwa, N., & Fujisawa, K. (2017). Practical approach to evacuation planning via network flow and deep learning. In J-Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, H. Zang, R. Baeza-Yates, R. Baeza-Yates, X. Hu, J. Kepner, A. Cuzzocrea, J. Tang, & M. Toyoda (Eds.), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (pp. 3368-3377). (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2017.8258322