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http://dx.doi.org/10.15683/kosdi.2021.9.30.655

Analysis Method for Speeding Risk Exposure using Mobility Trajectory Big Data  

Lee, Soongbong (Department of Big Data Platform and Data Economy, Korea Transport Institute)
Chang, Hyunho (Urban Science Institute, Incheon National University)
Kang, Taeseok (Urban Science Institute, Incheon National University)
Publication Information
Journal of the Society of Disaster Information / v.17, no.3, 2021 , pp. 655-666 More about this Journal
Abstract
Purpose: This study is to develop a method for measuring dynamic speeding risks using vehicle trajectory big data and to demonstrate the feasibility of the devised speeding index. Method: The speed behaviors of vehicles were analysed in microscopic space and time using individual vehicle trajectories, and then the boundary condition of speeding (i.e., boundary speed) was determined from the standpoint of crash risk. A novel index for measuring the risk exposure of speeding was developed in microscopic space and time with the boundary speed. Result: A validation study was conducted with vehicle-GPS trajectory big data and ground-truth vehicle crash data. As a result of the analysis, it turned out that the index of speeding-risk exposure has a strong explanatory power (R2=0.7) for motorway traffic accidents. This directly indicates that speeding behaviors should be analysed at a microscopic spatiotemporal dimension. Conclusion: The spatial and temporal evolution of vehicle velocity is very variable. It is, hence, expected that the method presented in this study could be efficaciously employed to analyse the causal factors of traffic accidents and the crash risk exposure in microscopic space using mobility trajectory data.
Keywords
Mobility Big Data; Vehicle Trajectory; Dynamic Speed Behavior; Microscopic Space and Time; Speeding Risk Exposure;
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