Nonlinear Influence of Built Environment on Pedestrian Traffic Accident Severity

被引:0
|
作者
Ji X.-F. [1 ]
Qiao X. [1 ]
机构
[1] Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming
基金
中国国家自然科学基金;
关键词
built environment; light gradient boosting machine (Light GBM); severity of pedestrian traffic accident; SHAP attribution analysis; urban traffic;
D O I
10.16097/j.cnki.1009-6744.2023.01.033
中图分类号
学科分类号
摘要
The study on the impact of the built environment on pedestrian traffic accidents could provide a theoretical basis for accident prevention. This paper constructed a day-night built environment index system, based on three dimensions of land use, urban design, and transportation system in the "5D" elements. Using the light gradient boosting machine, a day-night pedestrian traffic accident severity model was constructed to explore the influence mechanism of the urban built environment on the severity of pedestrian traffic accidents. Combined with the SHAP attribution analysis method, the nonlinear relationship was revealed. Taking Shenzhen City as an example, the results show that there is significant temporal heterogeneity in the impact of the built environment on pedestrian traffic accidents. The severity of daytime pedestrian traffic accidents is mainly affected by factors such as sidewalk accessibility, subway station accessibility, and school proximity. At night, it is mainly affected by sidewalk accessibility, entertainment point of interest (POI) indicators, road lighting conditions, and other factors. The built environment has a conspicuous nonlinear effect on the severity of pedestrian traffic accidents. When the proximity of schools is between zero and three kilometers during the daytime and the accessibility of subway stations is less than three kilometers, it has a great effect on the severity of pedestrian accidents. When the accessibility of entertainment POI is less than 0.5 kilometers at night, it has a significant effect on the severity of pedestrian accidents. The accessibility of sidewalks can reduce the severity of pedestrian accidents both day and night, and the area with a low density of courtyard gates on the street has higher-degree accidents. Lastly, the model shows excellent results, with classification accuracies of 96.38% and 92.08%. © 2023 Science Press. All rights reserved.
引用
收藏
页码:314 / 323
页数:9
相关论文
共 14 条
  • [1] HE Y Q, DUAN Y Y, WANG C., Analysis of the pedestrian traffic accidents and countermeasures for heightening such safety based on the cumulative Logistic model, Journal of Safety and Environment, 21, 3, pp. 1165-1172, (2021)
  • [2] CHEN J, LIU K L, DI J, Et al., Nonlinear model of impact of built environment on urban parking demand, Journal of Transportation Systems Engineering and Information Technology, 21, 4, pp. 197-203, (2021)
  • [3] MEHRNAZ A, MEHMET B U, KARST T G, Et al., A comprehensive analysis of the relationships between the built environment and traffic safety in the Dutch urban areas, Accident Analysis & Prevention, 172, 7, pp. 1-17, (2022)
  • [4] MARTA R O, ALAN R S, CHRISTINE T N, Et al., A new zone system to analyze the spatial relationships between the built environment and traffic safety, Journal of Transport Geography, 84, 4, pp. 1-12, (2020)
  • [5] JI X F, ZHANG Q., Risk assessment and influencing factors of pupils' school commuting accident risk in school district scale, Journal of Transportation Systems Engineering and Information Technology, 21, 1, pp. 221-226, (2021)
  • [6] RUI A, ZHAO M T, YI M D, Et al., Examining non-linear built environment effects on injurious traffic collisions: A gradient boosting decision tree analysis, Journal of Transport & Health, 24, 3, pp. 1-14, (2022)
  • [7] YUAN Z Z, GUO M Z, PENG Y X, Et al., Risk recognition of older pedestrian traffic crashes based on XGB-Apriori algorithm, Journal of Transportation Systems Engineering and Information Technology, 22, 1, pp. 195-208, (2022)
  • [8] DING C, CHEN P, JIAO J F., Non-linear effects of the built environment on automobile-involved pedestrian crash frequency: A machine learning approach, Accident Analysis & Prevention, 112, pp. 116-126, (2018)
  • [9] CHEN P, ZHOU J P., Effects of the built environment on automobile-involved pedestrian crash frequency and risk, Journal of Transport & Health, 3, 4, pp. 448-456, (2016)
  • [10] ZAMANI A, BEHNOOD A, RASOUL S, Et al., Temporal stability of pedestrian injury severity in pedestrian-vehicle crashes: New insights from random parameter Logit model with heterogeneity in means and variances [J], Analytic Methods in Accident Research, 32, 12, pp. 1-29, (2021)