中文核心期刊
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中国科技核心期刊
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重庆交通大学学报（自然科学版）

重庆交通大学学报（自然科学版） ›› 2023, Vol. 42 ›› Issue (3): 105-111.DOI: 10.3969/j.issn.1674-0696.2023.03.15

• 交通基础设施工程 • 上一篇    

建成环境对城市交通事故严重程度影响研究

陈坚1，邱智宣1，彭涛1，刘柯良1，傅志妍2，庹永恒3   

  1. (1. 重庆交通大学 交通运输学院，重庆 400074；2. 重庆第二师范学院 经济与工商管理学院，重庆 400067； 3. 重庆市公安局 大渡口区分局交巡警支队，重庆 400084)
  • 收稿日期:2021-09-09 修回日期:2021-12-31 发布日期:2023-05-11
  • 作者简介:陈 坚（1985—），男，江西赣州人，教授，博士，博士生导师，主要从事交通大数据理论方面的研究。E-mail：chenjian525@126.com
  • 基金资助:
    重庆教委科技研究计划项目(KJQN202001611)；重庆市教委”成渝地区双城经济圈建设”科技创新项目(KJCXZD2020029)

Influence of Built Environment on the Severity of Urban Traffic Accidents

CHEN Jian1, QIU Zhixuan1, PENG Tao1, LIU Keliang1, FU Zhiyan2, TUO Yongheng3   

  1. (1. School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China; 2. College of Economics & Business Administration, Chongqing University of Education, Chongqing 400067, China; 3. Traffic Patrol Detachment of Dadukou Public Security Sub-bureau, Chongqing Public Security Bureau, Chongqing 400084, China)
  • Received:2021-09-09 Revised:2021-12-31 Published:2023-05-11

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摘要： 针对城市交通事故分析中缺少建成环境因素的系统考虑，以密度、多样性、交通设计、可达性及公共交通临近度等5个维度表征建成环境，同时考虑个体行为、道路情况、事故类型及自然环境4个方面，运用机器学习算法建立融入建成环境因素对城市道路交通事故严重程度影响分析模型；并以重庆市某区的事故数据进行实证分析。研究结果表明：建成环境变量对事故严重程度有较大影响；从变量重要度排序来看，土地利用混合度(14.29%)、快速路及主干路密度(12.43%)、次干路及支路密度(11.54%)、人口密度(11.35%)与可达性(10.96%)的影响程度较高，累计重要度达60.57%；同时各变量与事故严重程度呈现出非线性关系。

关键词: 交通工程；城市交通安全；非线性；梯度提升决策树(GBDT)模型；建成环境；事故严重程度

Abstract: In view of the lack of systematic consideration of built environment factors in the analysis of urban traffic accidents, five dimensions including density, diversity, traffic design, accessibility and public transport proximity were selected to characterize the built environment, and four aspects including individual behavior, road conditions, accident types and natural environment were considered at the same time. The machine learning algorithm was used to establish an analysis model that incorporated built environment factors on the severity of urban road traffic accidents. Finally, an empirical analysis was carried out by use of the accident information in a certain district of Chongqing. The research results show that the built environment variables have a greater impact on the severity of accidents. From the perspective of variable importance ranking, the land use mixing degree (14.29%), the density of expressways and trunk roads (12.43%), the density of secondary roads and branch roads (11.54%), population density (11.35%) and accessibility (10.96%) have a higher impact and the cumulative importance reaches 60.57%. At the same time, each variable has a non-linear relationship with the severity of the accident.

Key words: traffic engineering; urban traffic safety; non-linearity; gradient boosting decision tree (GBDT) model; built environment; accident severity

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