Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China.
Beijing Key Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture, Beijing, China.
Int J Inj Contr Saf Promot. 2024 Jun;31(2):273-293. doi: 10.1080/17457300.2023.2300479. Epub 2024 Jan 29.
Traffic violation is one of the leading causes of traffic crashes. In the context of global aging, it is important to study traffic violations by elderly drivers for improving traffic safety in preparation for a worldwide aging population. In this study, a hybrid approach of Latent Class Analysis (LCA) and XGBoost based SHAP is proposed to identify hidden clusters and to understand the key contributing factors on the severity of traffic violations by elderly drivers, based on the police-reported traffic violation dataset of Beijing (China). First, LCA is applied to segment the dataset into several latent homogeneous clusters, then XGBoost based SHAP is established on each cluster to identify feature contributions and the interaction effects of the key contributing factors on the severity of traffic violations by elderly drivers. Two comparison groups were set up to analyze factors, which are responsible for the different severities of traffic violations. The results show that elderly drivers can be classified into four groups by age, urban or not, license, and season; factors such as less annual number of traffic violations, national & provincial highway, night and winter are key contributing factors for higher severity of traffic violations, which are consistent with common cognition; key contributing factors for all clusters are similar but not identical, for example, more annual number of traffic violations contribute to more severe violation for all clusters except for Cluster 2; some factors which are not key contributing factors may affect the severity of traffic violations when they are combined with other factors, for example, the combination of lower annual number of traffic violations and county & township highway contributes to more severe violation for Cluster 1. These findings can help government to formulate targeted countermeasures to decrease the severity of traffic violations by specific elderly groups and improve road service for the driving population.
交通违法是交通事故的主要原因之一。在全球老龄化的背景下，研究老年驾驶员的交通违法行为对于提高交通安全水平、为全球人口老龄化做好准备具有重要意义。本研究提出了一种基于潜在类别分析（LCA）和 XGBoost 的 SHAP 的混合方法，用于识别隐藏的聚类，并基于北京市（中国）的警方报告交通违法数据集，了解导致老年驾驶员交通违法行为严重程度的关键因素。首先，应用 LCA 将数据集分为几个潜在同质聚类，然后在每个聚类上建立基于 XGBoost 的 SHAP，以识别特征贡献和关键因素对老年驾驶员交通违法行为严重程度的交互作用。设置了两个比较组来分析导致交通违法严重程度不同的因素。结果表明，老年驾驶员可以按年龄、城乡、驾照和季节分为四类；每年交通违法次数较少、国道和省道、夜间和冬季等因素是导致交通违法严重程度较高的关键因素，这与普遍认知一致；所有聚类的关键因素相似但不完全相同，例如，除了聚类 2 之外，每年交通违法次数越多，所有聚类的违法越严重；当某些因素与其他因素结合时，即使它们不是关键因素，也可能会影响交通违法的严重程度，例如，每年交通违法次数较少且在县道和乡道行驶，会使聚类 1 的违法更严重。这些发现有助于政府针对特定老年群体制定有针对性的措施，以降低交通违法严重程度，并改善驾驶人群的道路服务。
