Wang Xuesong, Yang Junguang, Lee Chris, Ji Zhuoran, You Shikai
School of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, China.
School of Transportation Engineering, Tongji University, Shanghai 201804, China.
Accid Anal Prev. 2016 Nov;96:12-21. doi: 10.1016/j.aap.2016.07.028. Epub 2016 Jul 29.
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.
行人安全已成为交通安全领域最重要的问题之一。本研究旨在调查行人碰撞频率与各种预测变量之间的关联，这些变量包括道路、社会经济和土地利用特征。利用中国最大城市上海市区内263个交通分析区（TAZ）的数据对这些关系进行建模。由于区域层面的数据存在空间相关性，因此开发了具有七种不同空间权重特征的贝叶斯条件自回归（CAR）模型（即：(a) 基于邻接的0-1一阶；(b) 基于公共边界长度；(c) 基于几何质心距离；(d) 基于碰撞加权质心距离；(e) 基于邻接的土地利用类型；(f) 基于邻接的土地利用强度；(g) 几何质心距离顺序）来表征TAZ之间的空间相关性。模型结果表明，首次在宏观层面安全分析中引入的几何质心距离顺序空间权重特征优于所有其他空间权重特征。人口被用作行人暴露量的替代指标，对行人碰撞有积极影响。其他显著因素包括主要干道长度、次要干道长度、道路密度、平均交叉口间距、三岔路口百分比和TAZ面积。土地利用强度中等的TAZ中的行人碰撞事故比土地利用强度低和高的TAZ中的行人碰撞事故更多。因此，应给予土地利用强度中等的TAZ更高优先级以提高行人安全。总体而言，这些发现有助于交通规划者和管理者了解行人碰撞事故的特征并提高行人安全。
