title: The effect of road network patterns on pedestrian safety: A zone-based Bayesian spatial modeling approach.
abstract: Pedestrian safety is increasingly recognized as a major public health concern. Extensive safety studies have been conducted to examine the influence of multiple variables on the occurrence of pedestrian-vehicle crashes. However, the explicit relationship between pedestrian safety and road network characteristics remains unknown. This study particularly focused on the role of different road network patterns on the occurrence of crashes involving pedestrians. A global integration index via space syntax was introduced to quantify the topological structures of road networks. The Bayesian Poisson-lognormal (PLN) models with conditional autoregressive (CAR) prior were then developed via three different proximity structures: contiguity, geometry-centroid distance, and road network connectivity. The models were also compared with the PLN counterpart without spatial correlation effects. The analysis was based on a comprehensive crash dataset from 131 selected traffic analysis zones in Hong Kong. The results indicated that higher global integration was associated with more pedestrian-vehicle crashes; the irregular pattern network was proved to be safest in terms of pedestrian crash occurrences, whereas the grid pattern was the least safe; the CAR model with a neighborhood structure based on road network connectivity was found to outperform in model goodness-of-fit, implying the importance of accurately accounting for spatial correlation when modeling spatially aggregated crash data.
authors.affiliation: Department of Automation, Tsinghua University, Beijing, 100084, China. Electronic address: guoqiang_620@126.com.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Guo
authors.affiliation: Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong. Electronic address: pengpengxu@yeah.net.
authors.forename: Pengpeng
authors.initials: P
authors.lastname: Xu
authors.affiliation: Department of Automation, Tsinghua University, Beijing, 100084, China. Electronic address: peixin@mail.tsinghua.edu.cn.
authors.forename: Xin
authors.initials: X
authors.lastname: Pei
authors.affiliation: Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong. Electronic address: hhecwsc@hku.hk.
authors.forename: S C
authors.initials: SC
authors.lastname: Wong
authors.affiliation: Department of Automation, Tsinghua University, Beijing, 100084, China. Electronic address: yaody@mail.tsinghua.edu.cn.
authors.forename: Danya
authors.initials: D
authors.lastname: Yao
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control*; D001499:Bayes Theorem; D004779:Environment Design; D006723:Hong Kong; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D000069636:Pedestrians / Q000706:statistics & numerical data*; D011156:Population Density; D012111:Residence Characteristics; D018570:Risk Assessment / Q000706:statistics & numerical data; D012449:Safety / Q000592:standards*; D014505:Urban Population / Q000706:statistics & numerical data*
keywords: Bayesian spatial model; Global integration; Pedestrian safety; Road network connectivity; Road network patterns; Zone-based approach