Macro-level safety analysis of pedestrian crashes in Shanghai,China |
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| Affiliation: | 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. Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, MD, USA;2. NASA Goddard Space Flight Center, Greenbelt, MD, USA;1. Postdoctoral Fellow, Center for Transportation Research (CTR), University of Texas at Austin, United States;2. Beaman Professor, Department of Civil & Environmental Engineering, The University of Tennessee, United States;3. Graduate Research Assistant, Department of Civil & Environmental Engineering, The University of Tennessee, United States;1. Centre for Accident Research and Road Safety – Queensland (CARRS-Q), Queensland University of Technology, Victoria Park Road, Kelvin Grove 4059, Brisbane, QLD 4059, Australia;2. Civil Engineering and Built Environment, Science and Engineering Faculty and Centre for Accident Research and Road Safety (CARRS-Q), Faculty of Health, Queensland University of Technology, George St GPO Box 2434, Brisbane, QLD 4001, Australia;3. Centre for Accident Research and Road Safety (CARRS-Q), Faculty of Health and Civil Engineering and Built Environment, Science and Engineering Faculty, Queensland University of Technology, 130 Victoria Park Rd, Kelvin Grove, QLD 4059, Australia;4. Centre for Accident Research and Road Safety-Queensland (CARRS-Q), Queensland University of Technology (QUT), K Block K433, Victoria Park Road, Kelvin Grove 4059, Brisbane, QLD, Australia;1. Chalmers University of Technology, Department of Architecture and Civil Engineering, Chalmersplatsen 1, 41296 Gothenburg, Sweden;2. Institute of Transport Economics, Gaustadalleen 21, NO-0349 Oslo, Norway;3. University of Utah, Department of Geography, 260 S. Central Campus Drive, Salt Lake City, 84112 UT, United States;1. Department of Urban Design and Planning, University of Washington, Seattle, USA;2. Department of Civil and Environmental Engineering, University of Washington, Seattle, USA;3. Department of Urban Planning and Design, University of Hong Kong, Hong Kong, China |
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| Abstract: | Pedestrian safety has become one of the most important issues in the field of traffic safety. This study aims at investigating the association between pedestrian crash frequency and various predictor variables including roadway, socio-economic, and land-use features. The relationships were modeled using the data from 263 Traffic Analysis Zones (TAZs) within the urban area of Shanghai – the largest city in China. Since spatial correlation exists among the zonal-level data, Bayesian Conditional Autoregressive (CAR) models with seven different spatial weight features (i.e. (a) 0–1 first order, adjacency-based, (b) common boundary-length-based, (c) geometric centroid-distance-based, (d) crash-weighted centroid-distance-based, (e) land use type, adjacency-based, (f) land use intensity, adjacency-based, and (g) geometric centroid-distance-order) were developed to characterize the spatial correlations among TAZs. Model results indicated that the geometric centroid-distance-order spatial weight feature, which was introduced in macro-level safety analysis for the first time, outperformed all the other spatial weight features. Population was used as the surrogate for pedestrian exposure, and had a positive effect on pedestrian crashes. Other significant factors included length of major arterials, length of minor arterials, road density, average intersection spacing, percentage of 3-legged intersections, and area of TAZ. Pedestrian crashes were higher in TAZs with medium land use intensity than in TAZs with low and high land use intensity. Thus, higher priority should be given to TAZs with medium land use intensity to improve pedestrian safety. Overall, these findings can help transportation planners and managers understand the characteristics of pedestrian crashes and improve pedestrian safety. |
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| Keywords: | TAZ-level safety analysis Pedestrian crashes Spatial weight features Bayesian Conditional Autoregressive Model Transportation safety planning |
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