在运输和道路安全研究中,了解车辆行驶的公里数作为暴露和机动性的指标至关重要。它以分类的方式在确定用户风险指数中的应用引起了科学界和负责确保高速公路道路安全的当局的极大兴趣。这项研究使用了在车辆技术检查站进行乘用车检查期间记录的数据样本,并将其存储在由西班牙交通总局管理的数据仓库中。本研究有三个显著特点:(1)探索了新的数据源,(2)所开发的方法适用于其他类型的车辆,根据数据允许的分解水平,(3)模式提取和流动性估计有助于道路安全指标的持续和必要改进,并与2030年议程联合国可持续发展目标的目标3(良好的健康和福祉:具体目标3.6)保持一致。从收到的样本创建了一个操作数据仓库,这有助于获得西班牙车队车辆行驶公里数的推断值,根据作者的知识,使用先进的统计模型无法到达。三种机器学习方法,CART,随机森林,和梯度增强,根据模型的性能指标进行了优化和比较。这三种方法确定了年龄,发动机尺寸,乘用车的皮重是对其出行方式影响最大的因素。
Knowledge of the kilometers traveled by vehicles is essential in transport and road safety studies as an indicator of exposure and mobility. Its application in the determination of user risk indices in a disaggregated manner is of great interest to the scientific community and the authorities in charge of ensuring road safety on highways. This
study used a sample of the data recorded during passenger vehicle inspections at Vehicle Technical Inspection stations and housed in a data warehouse managed by the General Directorate for Traffic of Spain. This
study has three notable characteristics: (1) a novel data source is explored, (2) the methodology developed applies to other types of vehicles, with the level of disaggregation the data allows, and (3) pattern extraction and the estimate of mobility contribute to the continuous and necessary improvement of road safety indicators and are aligned with goal 3 (Good Health and Well-Being: Target 3.6) of The United Nations Sustainable Development Goals of the 2030 Agenda. An Operational Data Warehouse was created from the sample received, which helped in obtaining inference values for the kilometers traveled by Spanish fleet vehicles with a level of disaggregation that, to the knowledge of the authors, was unreachable with advanced statistical models. Three machine learning methods, CART, random forest, and gradient boosting, were optimized and compared based on the performance metrics of the models. The three methods identified the age, engine size, and tare weight of passenger vehicles as the factors with greatest influence on their travel patterns.