On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level |
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| Affiliation: | 1. Parsons, Brinckerhoff, 400 SW Sixth Avenue, Suite 802, Portland, OR 97204, United States;2. Parsons Brinckerhoff, One Penn Plaza, Suite 200, New York, NY 10119, United States;3. The University of Texas at Austin, Dept. of Civil, Architectural and Environmental Engineering, 301 E. Dean Keeton St., Stop C1761, Austin, TX 78712, United States;4. King Abdulaziz University, Jeddah 21589, Saudi Arabia;1. Department of Civil Engineering & Applied Mechanics, McGill University, Suite 483, 817 Sherbrooke St. W., Montréal, Canada;2. Department of Civil and Environmental Engineering, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL 33620, United States;3. Faculty of Business, Economics and Law, La Trobe University, Melbourne, Victoria 3086, Australia;1. Graduate Research Assistant Department of Civil and Environmental Engineering University of Alberta, Edmonton, AB, Canada T6G 2W2;2. City of Edmonton Assistant Professor of Urban Traffic Safety Department of Civil and Environmental Engineering University of Alberta, Edmonton, AB, Canada T6G 2W2;3. Department of Civil and Environmental Engineering University of Alberta, 4-110 NREF, Edmonton, Alberta, Canada T6G 2W2;1. School of Transportation Engineering, Tongji University, Shanghai 201804, China;2. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, China;3. Department of Civil and Environmental Engineering, University of Windsor, Windsor, Ontario N9B 3P4, Canada;1. University of Connecticut, Department of Statistics, Storrs, CT, USA;2. University of Connecticut, Department of Civil and Environmental Engineering, Storrs, CT, USA |
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| Abstract: | This paper proposes a new spatial multivariate count model to jointly analyze the traffic crash-related counts of pedestrians and bicyclists by injury severity. The modeling framework is applied to predict injury counts at a Census tract level, based on crash data from Manhattan, New York. The results highlight the need to use a multivariate modeling system for the analysis of injury counts by road-user type and injury severity level, while also accommodating spatial dependence effects in injury counts. |
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| Keywords: | Multivariate count data Spatial econometrics Crash analysis Composite marginal likelihood |
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