Department of Civil Engineering, Institute of Transport Studies, Monash University, Melbourne, VIC, Australia.
Institute of Smart City and Intelligent Transportation, Institute of Urban Rail Transportation, Southwest Jiaotong University, Chengdu 611730, China.
Accid Anal Prev. 2024 Jun;201:107561. doi: 10.1016/j.aap.2024.107561. Epub 2024 Apr 6.
While numerous studies have examined the factors that influence crash occurrence, there remains a gap in understanding the intricate relationship between built environment, traffic flow, and crash occurrences across different spatial units. This study explores how built environment attributes, and dynamic traffic flow characteristics affect crash frequency by focusing on proposed traffic density-based zones (TDZs). Utilizing a comprehensive dataset from Greater Melbourne, Australia, this research emphasizes on the dynamic traffic flow variables and insights from the Macroscopic Fundamental Diagram model, considering parameters such as shockwave velocity and congestion index. The association between the potential influencing factors and crash frequency is examined using a random parameter negative binomial regression model. Results indicate that the data segmentation based on TDZs is instrumental in establishing a more refined crash model compared to traditional planning-based zones, as demonstrated by improved goodness-of-fit measures. Factors including density (e.g., employment density), network design (e.g., road density and highway density), land use diversity (e.g., job-housing balance and land use mixture), and public transit accessibility (e.g., bus route density) are significantly associated with crash occurrence. Furthermore, the unobserved heterogeneity effects of the shockwave velocity and congestion index on crashes are revealed. The study highlights the significance of incorporating dynamic traffic flow variables in understanding crash frequency variations across different spatial units. These findings can inform optimal real-time traffic monitoring, environmental design, and road safety management strategies to mitigate crash risks.
虽然有许多研究考察了影响事故发生的因素，但在理解不同空间单元中建筑环境、交通流量与事故发生之间的复杂关系方面仍存在差距。本研究通过关注拟议的交通密度分区（TDZ），探讨了建筑环境属性和动态交通流特征如何通过影响交通密度来影响事故频率。本研究利用来自澳大利亚大墨尔本的综合数据集，强调了动态交通流变量和宏观基本图模型的见解，考虑了冲击波速度和拥堵指数等参数。使用随机参数负二项回归模型研究了潜在影响因素与事故频率之间的关联。结果表明，基于 TDZ 的数据分段有助于建立比传统规划分区更精细的事故模型，这一点通过改进的拟合优度度量得到了证明。密度（例如，就业密度）、网络设计（例如，道路密度和高速公路密度）、土地利用多样性（例如，职住平衡和土地利用混合）和公共交通可达性（例如，公交线路密度）等因素与事故发生显著相关。此外，还揭示了冲击波速度和拥堵指数对事故的未观察到的异质性效应。该研究强调了在不同空间单元中理解事故频率变化时纳入动态交通流变量的重要性。这些发现可以为优化实时交通监测、环境设计和道路安全管理策略提供信息，以降低事故风险。
