A novel dynamic segmentation model for identification and prioritization of black spots based on the pattern of potential for safety improvement |
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| Institution: | 1. Roads and Transportation Department, Civil & Environmental Engineering School, Tarbiat Modares University, Iran;2. Transportation Planning Department, Civil & Environmental Engineering School, Tarbiat Modares University, Iran;1. Department of Civil and Environmental Engineering, University of Hawaii at Manoa, 2540 Dole Street Honolulu, HI 96822, United States;2. Metropia, Inc., 1790 E.River Rd., Suite 140, Tucson, AZ 85718, United States;3. Department of Civil and Environmental Engineering, University of Utah, 110 Central Campus Drive, 2137 MCE, Salt Lake City, UT 84112, United States;4. Department of Civil Engineering, University of New Mexico, 210 University Blvd NE Albuquerque, NM 87106, United States;1. Department of Civil Environmental Construction Engineering, University of Central Florida, Orlando 4000 Central Florida Blvd, Orlando, FL 32816, USA;2. Department of Civil and Environmental Engineering, University of Windsor, Windsor, 401 Sunset Ave., Windsor, ON N9B 3P4, Canada;3. Department of Crime Prevention and Corrections, Central Police University, No. 56, Shujen Rd., Takang Village, Kueishan Hsiang, Taoyuan County, 33304, Taiwan;1. Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao''an Road, Shanghai 201804, China;2. Department of Civil & Environmental Engineering, Colorado State University, Fort Collins, CO 80523, United States;1. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, China;2. School of Transportation Engineering, Tongji University, Shanghai, 201804, China;1. School of Automobile, Chang’an University, Xi’an, Shaanxi, China;2. Deppon Logistics Corporation, Shanghai, China;3. John A. Reif, Jr. Department of Civil and Environmental Engineering, New Jersey Institute of Technology, Newark, NJ, USA;4. Yunnan Transport Research Institute, Kunming, Yunnan, China;5. Center of Transportation Research, The University of Tennessee, Knoxville, TN, USA;1. Department of Civil Engineering, University of Costa Rica, Costa Rica;2. Department of Transportation and Logistics Management, National Chiao Tung University, Taiwan, ROC;3. Department of Civil and Environmental Engineering, The Pennsylvania State University, United States |
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| Abstract: | Road segmentation is one of the most important steps in identification of high accident-proneness segments of a road. Based on the ratio of the Potential to Safety Improvement (PSI) along the road, the objective of the paper is to propose a novel dynamic road segmentation model. According to the fundamental model assumption, the determined segments must have the same pattern of PSI. Experimental results obtained from implementation of the proposed method took four Performance Measures (PMs) into consideration; namely, Crash Frequency, Crash Rate, Equivalent Property Damage Only, and Expected Average Crash Frequency with Empirical Bayes adjustment into the accident data obtained from Highway 37 located between two cities in Iran. Results indicated the low sensitivity of the method to PMs. In comparison with the real high accident-proneness segments, identified High Crash Road Segments (HCRS) obtained from the model, demonstrated the potential of the method to recognize the position and length of high accident-proneness segments accurately. Based on the road repair and maintenance costs limitation index for safety improvement, in an attempt to compare the proposed method of road segmentation with conventional ones, results demonstrated the efficient performance of the proposed method. So as to identify 20 percent HCRS located on a read, the proposed method showed an improvement of 38 and 57 percent in comparison with the best and worst outcomes derived from conventional road segmentation methods. |
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| Keywords: | Road safety Black spots Road segmentation Prioritization |
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