In this study, the traffic state of a commercial vehicle was analyzed from a macroscopic viewpoint by using the probe data of a commercial vehicle in the Shikoku region during a period of heavy rain that occurred in western Japan in July, 2018. A method is proposed to calculate indexes, such as the detour rate and reduction in the number of trips, through an analysis of a trip at each origin-destination (OD) and extracting the route of a detouring vehicle during a disaster by using the results of the calculation. Finally, a method for the early detection of abnormalities, which involves paying attention to U-turn action during traffic disturbances is proposed. The influence of heavy rain on a commercial vehicle was evaluated quantitatively by analyzing the probe data of the vehicle during a disaster period caused by heavy rain. Specifically, analysis was performed on the number of passing commercial vehicles before and after the occurrence of a disaster, changes in running speed, route changes at each OD, and the vehicle trajectory around a regulated area. From the results of the analysis, it was possible to grasp the macroscopic traffic state, OD influenced by the traffic restriction, route in use for the OD during a normal time period, and an alternate route (detour action) during the disaster time period. With the method for the early detection of abnormalities at the time of a traffic disturbance, which pays close attention to U-turn action, a U-turn after the traffic regulation can be detected; however, it was confirmed that there is a problem in detecting timing and the application range.

1. [1] L. Bengtsson, X. Lu, A. Thorson, R. Garfield, and J. von Schreeb, “Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: A post-earthquake geospatial study in Haiti,” PLoS Medicine, Vol.8, No.8, e1001083, 2011.
2. [2] Y. Hara and M. Kuwahara, “Traffic Monitoring immediately after a major natural disaster as revealed by probe data,” Transportation Research Part A: Policy and Practice, Vol.75, pp. 1-15, 2015.
3. [3] Y. Hara, “Behaviour analysis using tweet data and geo-tag data in a natural disaster,” Transportation Research Procedia, Vol.11, pp. 399-412, 2015.
4. [4] Y. Kawasaki, M. Kuwahara, Y. Hara, T. Mitani, A. Takenouchi, T. Iryo, and J. Urata, “Investigation of Traffic and Evacuation Aspects at Kumamoto Earthquake and the Future Issues,” J. Disaster Res., Vol.12, No.2, pp. 272-286, 2017.
5. [5] M. Borowska-Stefańska, A. Domagalski, and S. Wiśniewski, “Changes concerning commute traffic distribution on a road network following the occurrence of a natural disaster,” Transportation Research Part D: Transport and Environment, Vol.65, pp. 116-137, 2018.
6. [6] https://truck-probe.douro-net.jp.fujitsu.com/promotion/page/2016042201/slider.html [accessed January 7, 2019]
7. [7] M. Yoshida, Y. Kawasaki, S, Umeda, and M. Kuwahara, “Incident Alert by an Anomaly Indicator of Probe Trajectories,” Transportation Research Procedia, Vol.34, pp. 179-186, 2018.
8. [8] M. M. Ahmed abd A. Ghasemzadeh, “The impacts of heavy rain on speed and headway Behaviors An investigation using the SHRP2 naturalistic driving study data,” Transportation Research Part C: Emerging Technologies, Vol.91, pp. 371-384, 2018.
9. [9] S. Guo, R. Wu, Q. Tong, G. Zeng, J. Yang, L. Chen, T. Zhu, W. Lv, and D. Li, “Is city traffic damaged by torrential rain?,” Physica A: Statistical Mechanics and its Applications, Vol.503, pp. 1073-1080, 2018.
10. [10] Y. Wang and J. Luo, “Study of Rainfall Impacts on Freeway Traffic Flow Characteristics,” Transportation Research Procedia, Vol.25, pp. 1533-1543, 2017.
11. [11] D. Sathiaraj, T. Punkasem, F. Wang, and D. P. K. Seedah, “Data-driven analysis on the effects of extreme weather elements on traffic volume in Atlanta, GA, USA,” Computers, Environment and Urban Systems, Vol.72, pp. 212-220, 2018.
12. [12] X. Cai, J. J. Lu, Y. Xing, C. Jiang, and W. Lu, “Analyzing Driving Risks of Roadway Traffic under Adverse Weather Conditions: In Case of Rain Day,” Procedia – Social and Behavioral Sciences, Vol.96, pp. 2563-2571, 2013.
13. [13] M. Aron, R. Billot, N.-E. EL Faouzi, and R. Seidowsky, “Traffic indicators, accidents and rain some relationships calibrated on a French urban motorway network,” Transportation Research Procedia, Vol.10, pp. 31-40, 2015.
14. [14] D. Jaroszweski and T. McNamara, “The influence of rainfall on road accidents in urban areas A weather radar approach,” Travel Behaviour and Society, Vol.1, Issue 1, pp. 15-21, 2014.
15. [15] https://www.stat.go.jp/data/mesh/gaiyou.html [accessed Octber 30, 2018]
16. [16] K. Sekizuka, T. Mitani, Y. Kawasaki, T. Masuda, S. Nagai, and M. Kuwahara, “Travel time prediction under incident using only vehicle trajectories,” JSCE, Vol.53, CD-ROM, 2016 (in Japanese).
17. [17] Y. Asakura, T. Kusakabe, L. X. Nguyen, and T. Ushiki, “Incident Detection Methods using Probe Vehicles with on-board GPS Equipment,” Transportation Research Part C: Emerging Technologies, Vol.81, pp. 330-341, 2017.