Utilizing the eigenvectors of freeway loop data spatiotemporal schematic for real time crash prediction |
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| Affiliation: | 1. School of Transportation Engineering, Tongji University, 4800, Cao’an Highway, Shanghai, China;2. School of Public Health, UC Berkeley, Safe Transportation Research and Education Center (SafeTREC), University of California, Berkeley, United States;1. Department of Civil and Environmental Engineering, University of Windsor, ON N9B 3P4, Canada;2. Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, USA;1. Green Transport and Logistics Institute, Korea Railroad Research Institute, Uiwang, Republic of Korea;2. Department of Civil and Environmental Engineering, Seoul National University, Seoul, Republic of Korea;1. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China;2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China;3. College of Transportation Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, China;4. Department of Civil, Environmental and Construction Engineering, University of Central Florida Orlando, FL 32826-2450, United States;1. Department of Computational Science & Engineering, North Carolina Agricultural & Technical State University, United States;2. Department of Civil & Environmental Engineering, University of Maryland, College Park, United States;3. Department of Mechanical & Industrial Engineering, University of Massachusetts, Amherst, United States;4. Department of Civil & Environmental Engineering, University of Massachusetts, Amherst, United States |
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| Abstract: | The concept of crash precursor identification is gaining more practicality due to the recent advancements in Advanced Transportation Management and Information Systems. Investigating the shortcomings of the existing models, this paper proposes a new method to model the real time crash likelihood based on loop data through schematic eigenvectors. Firstly, traffic volume, occupancy and density spatiotemporal schematics in certain duration before an accident occurrence were constructed to describe the traffic flow status. Secondly, eigenvectors and eigenvalues of the spatiotemporal schematics were extracted to represent traffic volume, occupancy and density situation before the crash occurrence. Thirdly, by setting the vectors in crash time as case and those at crash free time as control, a logistic model is constructed to identify the crash precursors. Results show that both the eigenvectors and eigenvalues can significantly impact the accident likelihood compared to the previous study, the proposed model has the advantage of avoiding multicollinearity, better reflection of the overall traffic flow status before the crash, and improving missing data problem of loop detectors. |
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| Keywords: | Real time accident model Crash precursor Spatiotemporal schematics Road safety |
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