School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China; Beijing Municipal Institute of City Planning & Design, Beijing 100045, China.
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
Accid Anal Prev. 2024 Nov;207:107741. doi: 10.1016/j.aap.2024.107741. Epub 2024 Aug 12.
Statistical analysis of traffic crash frequency is significant for figuring out the distribution pattern of crashes, predicting the development trend of crashes, formulating traffic crash prevention measures, and improving traffic safety planning systems. In recent years, the theory and practice for traffic safety management have shown that road crash data have characteristics such as spatial correlation, temporal correlation, and excess zeros. If these characteristics are ignored in the modeling process, it may seriously affect the fitting performance and prediction accuracy of traffic crash frequency models and even lead to incorrect conclusions. In this research, traffic crash data from rural two-way two-lane from four counties in Pennsylvania, USA was modeled considering the spatiotemporal effects of crashes. First, a negative binomial Lindley spatiotemporal effect model of crash frequency was constructed at the micro level; Simultaneously, the characteristics and problems of excess zeros and potential heterogeneity of the crash data were resolved; Finally, the effects of road characteristics on crash frequency were analyzed. The results indicate a significant spatial correlation between the crash frequency of adjacent road sections. Compared with the negative binomial model, the negative binomial Lindley model can better handle the excess zeros characteristics in traffic crash data. The model that considers both spatial correlation and temporal conditional autoregressive effects has the best fit for the observed data. In addition, for road sections that allow passing and have a speed limitation of not less than 50 miles per hour, the crash frequency corresponding to these sections is lower due to their good visibility and road conditions. The increase in average turning angle and intersection density on the horizontal curve of the road section corresponds to an increase in crash frequency.
交通碰撞频率的统计分析对于找出碰撞的分布模式、预测碰撞的发展趋势、制定交通碰撞预防措施以及改进交通安全规划系统具有重要意义。近年来，交通安全管理的理论和实践表明，道路碰撞数据具有空间相关性、时间相关性和过零等特征。如果在建模过程中忽略这些特征，可能会严重影响交通碰撞频率模型的拟合性能和预测精度，甚至导致错误的结论。本研究考虑了碰撞的时空效应，对美国宾夕法尼亚州四个县的农村双向两车道的交通碰撞数据进行建模。首先，在微观层面上构建了一个负二项林德利时空效应的碰撞频率模型；同时，解决了碰撞数据过零和潜在异质性的特征和问题；最后，分析了道路特征对碰撞频率的影响。结果表明，相邻路段的碰撞频率存在显著的空间相关性。与负二项式模型相比，负二项林德利模型可以更好地处理交通碰撞数据中的过零特征。同时考虑空间相关性和时间条件自回归效应的模型对观测数据的拟合效果最好。此外，对于允许超车且限速不低于 50 英里/小时的路段，由于其良好的可视性和道路条件，这些路段对应的碰撞频率较低。道路路段水平曲线上平均转弯角度和交叉口密度的增加对应着碰撞频率的增加。
