Movement Recommendation System Based on Multi-Spot Congestion Analytics

Keita Nakayama, Akira Onoue, Maiya Hori, Atsushi Shimada and Rin-ichiro Taniguchi
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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

Sustainability, 2020, vol. 12, issue 6, 1-14

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.

Keywords: data visualization; predictive analytics; smart city (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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