Identifying the crash characteristics on freeway segments based on different ramp influence areas

<b>Objective:</b> This study aimed to explore the relationship between crash types and different freeway segments and identify the factors contributing to crashes on different freeway segments. Unlike most of the previous studies on freeway segments, this study separately investigates basic freeway segments, single ramp influence segments, and multiple ramp influence segments. <b>Methods:</b> Nonlinear canonical correlation analysis (NLCCA) and proportionality test were used to identify the relationship between crash types and different freeway segments. The data sets for the different freeway segments accumulated for this study consist of 9,867 crash samples with complete information on all 22 chosen variables. A multinomial logit model (MNL) was used to estimate the influence of crash factors on different freeway segments. <b>Results:</b> The results show that weaving and diverge overlap influence segments (WD) are more likely to have injury or fatal crashes; diverge and diverge overlap influence segments (DD) are more likely to have property damage–only (PDO) crashes; merge and merge overlap influence segments (MM) are more likely to have sideswipe crashes; and WD have non-sideswipe crashes; WD and weaving overlap influence segments (MW) are more likely to have rear end crashes; and MM segments are less likely to have hit object crashes. The contributing factors are identified by MNL and the results show that different traffic variables, environmental variables, vehicle variables, driver variables, and geometric variables significantly affected the likelihood of crashes on different freeway segments. <b>Conclusions:</b> Investigation of crash types and factors contributing to crashes on different freeway segments is based on multiple ramp influence segments, which can promote a better understanding of the safety performance of various freeway segments.

研究目的：本研究旨在探究事故类型与不同高速公路路段之间的关联，并识别不同高速公路路段上的事故诱因。与以往多数针对高速公路路段的研究不同，本研究分别对基本高速公路路段、单匝道影响路段及多匝道影响路段展开了分析。
研究方法：本研究采用非线性典型相关分析（Nonlinear Canonical Correlation Analysis, NLCCA）与比例检验，以识别事故类型与不同高速公路路段之间的关联。本研究收集的不同高速公路路段数据集共包含9867起事故样本，所有22个选定变量的信息均完整。本研究采用多项logit模型（Multinomial Logit Model, MNL），以估算事故诱因对不同高速公路路段的影响程度。
研究结果：结果显示，交织与分流重叠影响路段（WD）更易发生伤人或致死事故；分流与分流重叠影响路段（DD）更易发生仅财产损失事故（PDO）；合流与合流重叠影响路段（MM）更易发生擦撞事故；WD路段更易发生非擦撞类事故；合流与交织重叠影响路段（MW）更易发生追尾事故；而MM路段发生撞物事故的概率更低。本研究通过多项logit模型识别了事故诱因，结果表明，不同的交通变量、环境变量、车辆变量、驾驶员变量及几何变量，均会显著影响不同高速公路路段的事故发生概率。
研究结论：本研究基于多匝道影响路段，针对不同高速公路路段的事故类型及事故诱因展开调研，有助于更深入地理解各类高速公路路段的安全性能。