Abstract

Interchanges are important highway facilities by connecting two or more freeways/expressways; however, it has been considered more dangerous than basic segments because of drivers’ decision-making to stay or exit, weaving, variations in speeds, etc. In this study, we aim at contributing to the literature by using a quasi-induced exposure method and logistic regression modeling approach to identify contributing factors associated with the risk of causing an interchange crash. To this end, the 2014 traffic crash data, roadway and drivers’ characteristics were collected from Florida. The modeling results indicate that drivers’ age, gender, distraction, alcohol, and other factors have statistically significant effects. In addition, the finding suggests that drivers are more likely to cause crashes at cloverleaf and direct connection interchanges than at diamond interchanges. Furthermore, a support vector machine (SVM) model was applied to compare its predictability with the logistic regression model, and a sensitivity analysis was conducted. From the comparison of the areas under the receiver operating characteristic curve (AUC) of the two approaches, it shows that the SVM model outperforms the logistic regression model. It is expected that the findings would help establish effective strategies to reduce traffic crashes at interchanges by targeted education, engineering, and enforcement.

摘要

立交是连接两条或多条高速公路/高速公路的重要公路设施；然而，由于驾驶员决定停留或离开、编织、速度变化等，它被认为比基本部分更危险。在这项研究中，我们旨在通过使用准诱导暴露方法为文献做出贡献和逻辑回归建模方法，以确定与导致立交桥碰撞风险相关的影响因素。为此，从佛罗里达州收集了 2014 年的交通事故数据、道路和驾驶员特征。建模结果表明，驾驶员的年龄、性别、分心、酒精和其他因素具有统计显着性影响。此外，调查结果表明，司机在 Cloverleaf 立交桥和直接连接立交桥比钻石立交桥更容易造成车祸。此外，应用支持向量机（SVM）模型将其预测性与逻辑回归模型进行比较，并进行敏感性分析。从两种方法的受试者工作特征曲线下面积（AUC）的比较可以看出，SVM模型优于逻辑回归模型。预计研究结果将有助于制定有效的策略，通过有针对性的教育、工程和执法来减少立交桥的交通事故。并进行了敏感性分析。从两种方法的受试者工作特征曲线下面积（AUC）的比较可以看出，SVM模型优于逻辑回归模型。预计研究结果将有助于制定有效的策略，通过有针对性的教育、工程和执法来减少立交桥的交通事故。并进行了敏感性分析。从两种方法的受试者工作特征曲线下面积（AUC）的比较可以看出，SVM模型优于逻辑回归模型。预计研究结果将有助于制定有效的策略，通过有针对性的教育、工程和执法来减少立交桥的交通事故。