使用机器学习模型对新泽西州工作区崩溃进行严重程度建模

IF 2.4 3区 工程技术 Q3 TRANSPORTATION
A. S. Hasan, Md. Asif Bin Kabir, M. Jalayer, Subasish Das
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引用次数: 6

摘要

在美国,由于交通基础设施改造工程的增加,工作区撞车事故的概率有所增加。工作区域碰撞的严重程度与多种因素有关,如道路的几何设计特征、时间变量、环境条件、车辆类型和驾驶员行为。在这项研究中,我们从新泽西州获取并分析了三年(2016-2018年)的工作区撞车数据。我们研究了几种机器学习方法的性能,包括支持向量机、随机森林、Catboost、Light GBM和XGBoost,以预测工作区域碰撞导致的伤害严重程度。为了评价模型的性能,计算了一些统计评价参数,如准确率、精密度和召回分数。此外,还进行了敏感性分析,以评估与工作区有关的事故中最具影响因素的影响。随机森林和Catboost在预测致命、严重和轻微伤害方面优于其他模型。根据敏感性分析,碰撞类型和限速是与碰撞严重程度最显著相关的变量。预计这项研究的结果将有助于确定适当的对策,以减少工作区域碰撞的严重程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Severity modeling of work zone crashes in New Jersey using machine learning models
Abstract In the United States, the probability of work zone crashes has increased due to an increase in renovation works by transportation infrastructures. The severity of work zone crashes is associated with multiple contributing factors such as the roadway’s geometric design features, temporal variables, environmental conditions, types of vehicles, and driver behaviors. For this study, we acquired and analyzed three years (2016–2018) of work zone crash data from the state of New Jersey. We investigated the performance of several machine learning methods, including Support Vector Machine, Random Forest, Catboost, Light GBM, and XGBoost to predict the type of injury severity resulting from work zone crashes. To evaluate models’ performances, some statistical evaluation parameters such as accuracy, precision, and recall scores were calculated. In addition, a sensitivity analysis was conducted to assess the impact of the most influential factors in work zone-related crashes. Random Forest and Catboost outperformed the other models in terms of predicting fatal, major, and minor injuries. According to the sensitivity analysis, crash type and speed limit were the most significantly associated variables with crash severity. The findings of this study are expected to facilitate the identification of appropriate countermeasures for reducing the severity of work zone crashes.
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来源期刊
CiteScore
6.00
自引率
15.40%
发文量
38
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