As car ownership and urbanization continue to rise worldwide, traffic crashes have become growing concerns globally. Measuring crash risk provides insight into understanding crash patterns, which can eventually support proactive transport planning and improve road safety. However, traditional spatial analysis methods for crash risk assessment, such as the hotspot detection method, are mainly focused on identifying areas with higher crash frequency. These methods are subject to critical issues in risk analysis due to ignoring crash impacts and background traffic volume information. Aside from the two issues, current crash risk assessment methods, especially those aiming for cluster detection, are subject to the modified temporal unit problem, referring to the temporal effects (i.e., aggregation, segmentation, and boundary) in cluster detection. To alleviate these issues, this paper applies an emerging hot spot detection method, called the prospective space-time scan statistic (STSS) method, for assessing the crash risk at a refined network scale and over multiple years in a case study of Hartford, Connecticut. By identifying the spatial and temporal clusters of the crash risk, the study can provide evidence for tailoring road safety management strategies in neighborhoods characterized by high crash risk.

中文翻译：

---

前瞻性时空扫描统计方法评估网络交通事故风险


随着全球汽车保有量和城市化进程持续上升，交通事故已成为全球日益关注的问题。测量碰撞风险可以深入了解碰撞模式，最终可以支持主动交通规划并提高道路安全。然而，传统的碰撞风险评估空间分析方法，例如热点检测方法，主要集中于识别碰撞频率较高的区域。由于忽略了碰撞影响和背景交通量信息，这些方法在风险分析中容易遇到关键问题。除了这两个问题之外，当前的碰撞风险评估方法，特别是那些针对聚类检测的方法，还受到修改时间单元问题的影响，即聚类检测中的时间效应（即聚合、分割和边界）。为了缓解这些问题，本文应用了一种新兴的热点检测方法，称为前瞻性时空扫描统计（STSS）方法，在康涅狄格州哈特福德的案例研究中评估细化网络规模和多年的崩溃风险。通过识别碰撞风险的空间和时间集群，该研究可以为在碰撞风险高的社区制定道路安全管理策略提供证据。