Smart Transportation Key Laboratory of Hunan Province, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China.
Sensors (Basel). 2021 Dec 21;22(1):5. doi: 10.3390/s22010005.
Spatial autocorrelation and skewed distribution are the most frequent issues in crash rate modelling analysis. Previous studies commonly focus on the spatial autocorrelation between adjacent regions or the relationships between crash rate and potentially risky factors across different quantiles of crash rate distribution, but rarely both. To overcome the research gap, this study utilizes the spatial autoregressive quantile (SARQ) model to estimate how contributing factors influence the total and fatal-plus-injury crash rates and how modelling relationships change across the distribution of crash rates considering the effects of spatial autocorrelation. Three types of explanatory variables, i.e., demographic, traffic networks and volumes, and land-use patterns, were considered. Using data collected in New York City from 2017 to 2019, the results show that: (1) the SARQ model outperforms the traditional quantile regression model in prediction and fitting performance; (2) the effects of variables vary with the quantiles, mainly classifying three types: increasing, unchanged, and U-shaped; (3) at the high tail of crash rate distribution, the effects commonly have sudden increases/decrease. The findings are expected to provide strategies for reducing the crash rate and improving road traffic safety.
空间自相关和偏态分布是碰撞率建模分析中最常见的问题。先前的研究通常集中在相邻区域之间的空间自相关，或者在不同碰撞率分布分位数下碰撞率与潜在风险因素之间的关系，但很少同时考虑这两个方面。为了克服研究空白，本研究利用空间自回归分位数 (SARQ) 模型来估计影响因素如何影响总碰撞率和致命加伤害碰撞率，以及考虑空间自相关影响时，模型关系如何随碰撞率分布而变化。考虑了三种解释变量，即人口统计学、交通网络和交通量以及土地利用模式。利用 2017 年至 2019 年在纽约市收集的数据，结果表明：（1）SARQ 模型在预测和拟合性能方面优于传统的分位数回归模型；（2）变量的影响随分位数而变化，主要分为三种类型：增加、不变和 U 型；（3）在碰撞率分布的长尾部分，影响通常会突然增加/减少。研究结果有望为降低碰撞率和提高道路交通安全提供策略。
