Abstract

Objective: Rear-end crashes are a type of road traffic accident that occurs frequently. Currently, the application of advanced statistical models to predict the frequency of accident numbers has increased because such models enable accuracy in predictions. The study focuses on the application of these statistical models to determine the relationship between explanatory variables and the frequency of rear-end crashes. Method: Data used are rear-end collisions occurring on highways throughout Thailand for the years 2011–2018. The number of rear-end collisions was distributed according to road segments with similar physical characteristics. Spatial correlation was utilized by varying according to the jurisdiction of the Department of Highways. Four models, namely, Poisson regression model, negative binomial model, zero-inflated negative binomial model, and spatial zero-inflated negative binomial (SZINB) model were developed. Results: When compared with the conditional Akaike Information Criterion (cAIC), SIZNB was found to be most suitable for data. Regarding random effect results, the effect of the significance was constant for the variables conditional state and zero state, which covered segment length, number of lanes, and traffic volume. Conclusion: This study can serve as a starting point for researchers interested in applying the spatial model to the analysis of rear-end crashes.

摘要

目的：追尾事故是一种多发的道路交通事故。目前，高级统计模型在预测事故数量频率方面的应用有所增加，因为此类模型能够实现预测的准确性。该研究侧重于应用这些统计模型来确定解释变量与追尾事故频率之间的关系。方法：使用的数据是 2011-2018 年泰国各地高速公路上发生的追尾事故。追尾事故的数量根据具有相似物理特征的路段分布。空间相关性根据公路部的管辖范围而有所不同。建立了泊松回归模型、负二项式模型、零膨胀负二项式模型和空间零膨胀负二项式（SZINB）模型四种模型。结果：与条件赤池信息准则 (cAIC) 相比，发现 SIZNB 最适合数据。关于随机效应结果，变量条件状态和零状态的显着性影响是恒定的，包括路段长度、车道数和交通量。结论：这项研究可以作为有兴趣将空间模型应用于追尾事故分析的研究人员的起点。