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 Jun 2;17(11):3937. doi: 10.3390/ijerph17113937.
Risky and aggressive driving maneuvers are considered a significant indicator for traffic accident occurrence as well as they aggravate their severity. Traffic violations caused by such uncivilized driving behavior is a global issue. Studies in existing literature have used statistical analysis methods to explore key contributing factors toward aggressive driving and traffic violations. However, such methods are unable to capture latent correlations among predictor variables, and they also suffer from low prediction accuracies. This study aimed to comprehensively investigate different traffic violations using spatial analysis and machine learning methods in the city of Luzhou, China. Violations committed by taxi drivers are the focus of the current study since they constitute a significant proportion of total violations reported in the city. Georeferenced violation data for the year 2016 was obtained from the traffic police department. Detailed descriptive analysis is presented to summarize key statistics about various violation types. Results revealed that over-speeding was the most prevalent violation type observed in the study area. Frequency-based nearest neighborhood cluster methods in Arc map Geographic Information System (GIS) were used to develop hotspot maps for different violation types that are vital for prioritizing and conducting treatment alternatives efficiently. Finally, different machine learning (ML) methods, including decision tree, AdaBoost with a base estimator decision tree, and stack model, were employed to predict and classify each violation type. The proposed methods were compared based on different evaluation metrics like accuracy, F-1 measure, specificity, and log loss. Prediction results demonstrated the adequacy and robustness of proposed machine learning (ML) methods. However, a detailed comparative analysis showed that the stack model outperformed other models in terms of proposed evaluation metrics.
冒险和激进的驾驶行为被认为是交通事故发生的一个重要指标，并且会使事故的严重程度加剧。这种不文明驾驶行为导致的交通违法行为是一个全球性问题。现有文献中的研究已经使用统计分析方法来探索激进驾驶和交通违法行为的关键影响因素。然而，这些方法无法捕捉预测变量之间的潜在相关性，并且预测精度也较低。本研究旨在利用空间分析和机器学习方法在中国泸州市全面调查不同的交通违法行为。目前的研究重点是出租车司机的违法行为，因为它们构成了该市报告的总违法行为中的很大一部分。从交通警察部门获得了 2016 年的违规地理参考数据。进行了详细的描述性分析，以总结各种违规类型的关键统计信息。结果表明，超速是研究区域中最常见的违规类型。Arc map 地理信息系统（GIS）中的基于频率的最近邻聚类方法用于为不同的违规类型开发热点图，这对于有效地确定优先级和实施处理替代方案至关重要。最后，使用不同的机器学习（ML）方法，包括决策树、基于决策树的 AdaBoost 和堆叠模型，来预测和分类每种违规类型。基于不同的评估指标，如准确性、F-1 度量、特异性和对数损失，对提出的方法进行了比较。预测结果证明了所提出的机器学习（ML）方法的充分性和稳健性。然而，详细的比较分析表明，堆叠模型在提出的评估指标方面优于其他模型。
