An improved vehicle-pedestrian near-crash identification method with a roadside LiDAR sensor |
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| Institution: | 1. School of Qilu Transportation, Shandong University, China;2. University of Nevada, Reno, Reno, NV 89557, United States;3. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;1. Virginia Tech Transportation Institute, 3500 Transportation Research Plaza, Blacksburg, VA 24060, United States;2. Virginia Polytechnic Institute and State University, 750 Drillfield Drive, 200 Patton Hall, Blacksburg, VA 24061, United States;1. Lyles School of Civil Engineering, Purdue University, 3000 Kent Avenue, Suite C2-103, West Lafayette, IN 47907, United States;2. Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, United States;1. Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada;2. Department of Civil Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario, M5B 2K3 Canada;1. Zachry Department of Civil & Environmental Engineering, Texas A&M University, 3136 TAMU, College Station, TX, 77843-3136, United States;2. Texas A&M Transportation Institute, 3500 NW Loop 410, San Antonio, TX, 78229, United States |
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| Abstract: | Problem: Potential conflicts between pedestrians and vehicles represent a challenge to pedestrian safety. Near-crash is used as a surrogate metric for pedestrian safety evaluations when historical vehicle–pedestrian crash data are not available. One challenge of using near-crash data for pedestrian safety evaluation is the identification of near-crash events. Method: This paper introduces a novel method for pedestrian-vehicle near-crash identification that uses a roadside LiDAR sensor. The trajectory of each road user can be extracted from roadside LiDAR data via several data processing algorithms: background filtering, lane identification, object clustering, object classification, and object tracking. Three indicators, namely, the post encroachment time (PET), the proportion of the stopping distance (PSD), and the crash potential index (CPI) are applied for conflict risk classification. Results: The performance of the developed method was evaluated with field-collected data at four sites in Reno, Nevada, United States. The results of case studies demonstrate that pedestrian-vehicle near-crash events could be identified successfully via the proposed method. Practical applications: The proposed method is especially suitable for pedestrian-vehicle near-crash identification at individual sites. The extracted near-crash events can serve as supplementary material to naturalistic driving study (NDS) data for safety evaluation. |
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| Keywords: | Near-crash identification Pedestrian safety Roadside LiDAR |
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