College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China.
College of Artificial Intelligence and Automation, Beijing University of Technology, Beijing 100124, China.
Int J Environ Res Public Health. 2020 Jul 18;17(14):5193. doi: 10.3390/ijerph17145193.
Traffic violations usually caused by aggressive driving behavior are often seen as a primary contributor to traffic crashes. Violations are either caused by an unintentional or deliberate act of drivers that jeopardize the lives of fellow drivers, pedestrians, and property. This study is aimed to investigate different traffic violations (overspeeding, wrong-way driving, illegal parking, non-compliance traffic control devices, etc.) using spatial analysis and different machine learning methods. Georeferenced violation data along two expressways (S308 and S219) for the year 2016 was obtained from the traffic police department, in the city of Luzhou, China. Detailed descriptive analysis of the data showed that wrong-way driving was the most common violation type observed. Inverse Distance Weighted (IDW) interpolation in the ArcMap Geographic Information System (GIS) was used to develop violation hotspots zones to guide on efficient use of limited resources during the treatment of high-risk sites. Lastly, a systematic Machine Learning (ML) framework, such as K Nearest Neighbors (KNN) models (using k = 3, 5, 7, 10, and 12), support vector machine (SVM), and CN2 Rule Inducer, was utilized for classification and prediction of each violation type as a function of several explanatory variables. The predictive performance of proposed ML models was examined using different evaluation metrics, such as Area Under the Curve (AUC), F-score, precision, recall, specificity, and run time. The results also showed that the KNN model with k = 7 using manhattan evaluation had an accuracy of 99% and outperformed the SVM and CN2 Rule Inducer. The outcome of this study could provide the practitioners and decision-makers with essential insights for appropriate engineering and traffic control measures to improve the safety of road-users.
交通违法行为通常是由攻击性驾驶行为引起的，往往被视为交通事故的主要原因。违规行为要么是驾驶员无意或故意的行为造成的，危及到其他驾驶员、行人和财产的生命安全。本研究旨在使用空间分析和不同的机器学习方法来调查不同的交通违法行为（超速、逆行驾驶、非法停车、不遵守交通控制装置等）。从中国泸州市交通警察部门获得了 2016 年两条高速公路（S308 和 S219）的违规地理参考数据。对数据的详细描述性分析表明，逆行驾驶是观察到的最常见的违规类型。在 ArcMap 地理信息系统（GIS）中使用反距离权重（IDW）插值来开发违规热点区域，以指导在处理高风险地点时有效利用有限的资源。最后，利用 K 最近邻（KNN）模型（k = 3、5、7、10 和 12）、支持向量机（SVM）和 CN2 规则诱导器等系统机器学习（ML）框架，根据几个解释变量对每种违规类型进行分类和预测。使用不同的评估指标，如曲线下面积（AUC）、F 分数、精度、召回率、特异性和运行时间，检查了所提出的 ML 模型的预测性能。结果还表明，曼哈顿评估中 k = 7 的 KNN 模型的准确率为 99%，优于 SVM 和 CN2 规则诱导器。这项研究的结果可以为从业者和决策者提供必要的见解，以采取适当的工程和交通控制措施，提高道路使用者的安全性。
