Exploring pedestrian injury severities at pedestrian-vehicle crash hotspots with an annual upward trend: A spatiotemporal analysis with latent class random parameter approach |
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| Institution: | 1. USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, Room 3366, 9201 University City Boulevard, Charlotte, NC 28223-0001, United States;2. School of Transportation Science and Engineering, Harbin Institute of Technology, No. 73, Huanghe Road, Nangang District, Harbin, China;1. USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, Room 3366, 9201 University City Boulevard, Charlotte, NC 28223-0001, United States;2. School of Transportation Science and Engineering, Harbin Institute of Technology, No. 73, Huanghe Road, Nangang District, Harbin, China;1. Glenn Department of Civil Engineering, Clemson University, Clemson, SC 29634, USA;2. Applied Statistics, School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA;3. Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29208 USA;1. College of Engineering, Zhejiang Normal University, Zhejiang 321005, China;2. Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Province, Zhejiang Normal University, Zhejiang 321005, China;3. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 611756, China;4. Sichuan Vocational and Technical College of Communications, Chengdu, Sichuan 611130, China;1. Connecticut Transportation Safety Research Center, Connecticut Transportation Institute, University of Connecticut, United States;2. Department of Civil, Environmental & Construction Engineering, University of Central Florida, United States;1. Center for Connected and Automated Transportation, Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907-2051, USA;2. Center for Road Safety, Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907-2051, USA;3. Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN 37212, USA;4. Department of Civil and Environmental Engineering, Manhattan College, Riverdale, NY 10471, USA |
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| Abstract: | Introduction: With the increasing trend of pedestrian deaths among all traffic fatalities in the past decade, there is an urgent need for identifying and investigating hotspots of pedestrian-vehicle crashes with an upward trend. Method: To identify pedestrian-vehicle crash locations with aggregated spatial pattern and upward temporal pattern (i.e., hotspots with an upward trend), this paper first uses the average nearest neighbor and the spatial autocorrelation tests to determine the grid distance and the neighborhood distance for hotspots, respectively. Then, the spatiotemporal analyses with the Getis-Ord Gi* index and the Mann-Kendall trend test are utilized to identify the pedestrian-vehicle crash hotspots with an annual upward trend in North Carolina from 2007 to 2018. Considering the unobserved heterogeneity of the crash data, a latent class model with random parameters within class is proposed to identify specific contributing factors for each class and explore the heterogeneity within classes. Significant factors of the pedestrian, vehicle, crash type, locality, roadway, environment, time, and traffic control characteristics are detected and analyzed based on the marginal effects. Results: The heterogeneous results between classes and the random parameter variables detected within classes further indicate the superiority of latent class random parameter model. Practical Applications: This paper provides a framework for researchers and engineers to identify crash hotspots considering spatiotemporal patterns and contribution factors to crashes considering unobserved heterogeneity. Also, the result provides specific guidance to developing countermeasures for mitigating pedestrian-injury at pedestrian-vehicle crash hotspots with an upward trend. |
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| Keywords: | Pedestrian Injury severity Spatiotemporal analysis Latent class clustering Random parameter logit model |
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