title: Macro-level safety analysis of pedestrian crashes in Shanghai, China.
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.
authors.affiliation: School of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, China. Electronic address: wangxs@tongji.edu.cn.
authors.forename: Xuesong
authors.initials: X
authors.lastname: Wang
authors.affiliation: School of Transportation Engineering, Tongji University, Shanghai 201804, China.
authors.forename: Junguang
authors.initials: J
authors.lastname: Yang
authors.affiliation: Department of Civil and Environmental Engineering, University of Windsor, Windsor, Ontario N9B 3P4, Canada.
authors.forename: Chris
authors.initials: C
authors.lastname: Lee
authors.affiliation: School of Transportation Engineering, Tongji University, Shanghai 201804, China.
authors.forename: Zhuoran
authors.initials: Z
authors.lastname: Ji
authors.affiliation: School of Transportation Engineering, Tongji University, Shanghai 201804, China.
authors.forename: Shikai
authors.initials: S
authors.lastname: You
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D002681:China; D004779:Environment Design; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D018986:Motor Vehicles / Q000706:statistics & numerical data*; D000069636:Pedestrians / Q000706:statistics & numerical data*; D018570:Risk Assessment / Q000706:statistics & numerical data; D012449:Safety / Q000706:statistics & numerical data*; D014505:Urban Population / Q000706:statistics & numerical data
keywords: Bayesian Conditional Autoregressive Model; Pedestrian crashes; Spatial weight features; TAZ-level safety analysis; Transportation safety planning