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Movement Recommendation System Based on Multi-Spot Congestion Analytics

Author

Listed:
  • Keita Nakayama

    (Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan)

  • Akira Onoue

    (Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan)

  • Maiya Hori

    (Platform of Inter/Transdisciplinary Energy Research, Kyusyu University, Fukuoka 819-0395, Japan)

  • Atsushi Shimada

    (Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan)

  • Rin-ichiro Taniguchi

    (Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan)

Abstract

A method is proposed for resolving human congestion at a specific time at key spots in an area. Sensing data on real-world human flows are analyzed, and important information for changing movement behavior is accordingly provided. By using conventional approaches, this was a difficult task, whereas in the proposed approach, the targets and timing of providing information for congestion mitigation are determined based on spot importance. A congestion transition model is constructed from actual data and the results of a questionnaire survey. Finally, congestion mitigation in key spots is simulated after movement recommendation has been provided.

Suggested Citation

  • Keita Nakayama & Akira Onoue & Maiya Hori & Atsushi Shimada & Rin-ichiro Taniguchi, 2020. "Movement Recommendation System Based on Multi-Spot Congestion Analytics," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2417-:d:334407
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    References listed on IDEAS

    as
    1. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
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