DETECTION AND ANALYSIS OF DETOURS OF COMMERCIAL VEHICLES DURING HEAVY RAINS IN WESTERN JAPAN USING MACHINE LEARNING TECHNOLOGY

• KAWASAKI Yosuke

  Graduate School of Information Sciences, Tohoku University

• UMEDA Shogo

  New Industry Creation Hatchery Center

• KUWAHARA Masao

  New Industry Creation Hatchery Center

Abstract

 In this study, we detect the detours of commercial vehicles during heavy rains in western Japan using machine learning technology and then analyze the cause of these detours. Due to heavy rains in 2018 in western Japan, road regulation was implemented over a wide area. GPS-generated probe trajectories revealed the detour routes taken. The necessity of taking detours is one of the traffic failures caused by disasters. To identify these detours, a road administrator must visually check and analyze the probe vehicle trajectory, which requires considerable labor. Therefore, in this study, we detected detours during a disaster by learning the probe vehicle trajectory under normal circumstances using a one-class support vector machine (OCSVM). Results of detour detection for Shikoku revealed that vehicles were using distant detour routes even when nearer detour routes were accessible. An analysis of the cause of these detours showed that the “risk” of the traffic failure was one factor.

Journal

• Journal of JSCE

  Journal of JSCE 9 (1), 8-19, 2021

  Japan Society of Civil Engineers

Keywords

• one-class support vector machine (SVM)
• commercial probe vehicle
• the heavy rain event
• detour route
• risk of traffic failure

Details

• CRID: 1391975831237073664
• NII Article ID: 130007971150
• DOI: 10.2208/journalofjsce.9.1_8
• ISSN: 21875103
• Web Site: https://www.jstage.jst.go.jp/article/journalofjsce/9/1/9_8/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed