| 摘要: |
| 建成环境与行人交通事故的关系是
城市规划和交通管理领域的重要研究议题。然
而，现有研究多局限于线性关系的探讨，缺乏
对非线性影响及阈值效应的深入分析，难以支
撑精细化规划与治理实践。为此，本文以重庆
市渝中区为例，整合行人交通事故数据、路网
数据、土地利用数据、手机信令数据、POI数
据等多源空间大数据，运用梯度提升决策树模
型（GBDT），从道路设施、土地利用、设施
临近性、空间结构、社会经济5 个维度系统
解析建成环境要素对行人交通事故频率的非
线性关系和阈值效应。研究发现：第一，各
建成环境要素与行人交通事故存在非线性关
系和阈值效应；第二，控制度对行人交通事
故的相对重要性最高，其次是人口密度、路
网密度和土地利用混合度。研究结论为精细
化建成环境规划与交通治理提供了科学依
据，对提升行人步行安全具有重要的实践指
导意义。 |
| 关键词: 建成环境 行人安全 交通安全 非线性 机器学习 |
| DOI：10.13791/j.cnki.hsfwest.20240112002 |
| 分类号: |
| 基金项目:国家自然科学基金项目（42071218） |
|
| Research on the nonlinear relationship and threshold effects between the built environmentand pedestrian traffic accidents: A case study of Yuzhong District, Chongqing |
|
CHEN Chun,LIU Shuangqi,ZHOU Shuhong,KUANG Xinhui
|
| Abstract: |
| Pedestrians are widely recognized as the most vulnerable group among road users and are
more likely to be involved in severe traffic accidents. Therefore, creating a safer environment for
pedestrians is crucial for protecting residents and promoting high-quality urban development.
Traditionally, the factors influencing pedestrian traffic accidents have been analyzed from a micro
perspective, focusing primarily on intersections and crosswalks. However, the effects of medium- and
macro-level built environment factors, such as urban spatial structure, land use, and density, on
pedestrian traffic accidents have not been comprehensively and systematically addressed. Recently,
scholars in the fields of urban planning and geography have begun to explore the relationship between
the built environment and pedestrian traffic accidents, often employing statistical models such as
negative binomial regression and logistic regression. In recent years, machine learning methods have
been increasingly applied to the studies of walking behavior. These studies have revealed a nonlinear
relationship between the built environment and walking activities, and since pedestrian traffic safety
is closely related to walking behavior, it is likely that similar nonlinear and threshold effects exist
between built environment factors and pedestrian traffic safety. Investigating these nonlinear
relationships and threshold effects challenges the traditional assumption of a linear connection
between the built environment and pedestrian traffic accidents. It also provides valuable insights into
the local characteristics of how built environment variables influence pedestrian traffic accidents,
particularly from the perspective of marginal effects. In practical applications, empirical findings on
nonlinear and threshold effects, especially regarding the relative importance and threshold ranges of
various built environment elements, can offer more refined strategies and effective solutions for traffic
management and urban planning.Yuzhong District in Chongqing, located at the confluence of the
Yangtze River and Jialing River, covers an area of approximately 23.24 square kilometers. With its
dense urban road network and compact land use, Yuzhong District has developed a unique and
complex transportation system that integrates both underground and surface transportation. However,
the increasing number of vehicles and relatively underdeveloped pedestrian infrastructure have
exacerbated conflicts between pedestrians and vehicles. For these reasons, Yuzhong District serves as
an ideal and representative reseach area. This study collects spatial big data from multiple sources,
including pedestrian traffic accident data, road network data, land use data, mobile phone signaling
data, and Points of Interest (POI) data. Using the Gradient Boosting Decision Tree (GBDT) model,the study explores the nonlinear relationships and threshold effects of built environment factors across five dimensions: road facilities, land use, proximity to
facilities, spatial structure, and socioeconomic factors, on the frequency of pedestrian traffic accidents.The findings of the study are as follows: First, there are
significant differences in the degree to which built environment factors influence pedestrian traffic accidents. Spatial structure, with the highest relative
importance, has the greatest impact on pedestrian traffic accidents, followed by population density, road network density, and land use mix. Second, the
relationship between each built environment factor and pedestrian traffic accidents exhibits nonlinear and threshold effects. For example, pedestrian overpasses
contribute more significantly to pedestrian safety than zebra crossings. When the number of pedestrian overpasses exceeds four per square kilometer, pedestrian
traffic accidents can be effectively reduced. Similarly, in areas where the land use mix exceeds 0.37, traffic management measures such as speed limits should
be implemented to mitigate pedestrian traffic accidents. |
| Key words: built environment pedestrian safety traffic safety nonlinearity machine learning |
