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Crash prediction based on traffic platoon characteristics using floating car trajectory data and the machine learning approach

机译：使用浮动汽车轨迹数据和机器学习方法基于交通排特征的碰撞预测

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摘要

Predicting crash propensity helps study safety on urban expressways in order to implement countermeasures and make improvements. It also helps identify and prevent crashes before they happen. However, collecting real-time wide-coverage traffic information for crash prediction has been challenging. More importantly, previous studies have failed to consider the characteristics of the traffic platoon (vehicle group) that the crash vehicle belongs to before the crash occurs. This paper aims to model crash propensity based on traffic platoon characteristics collected by the floating car method, which provides a time-efficient and reliable solution to collecting traffic information. Crash and floating car data are collected from the Middle Ring Expressway in Shanghai, China. Both the binary logistic model and the support vector machine are applied. A data preparation method, involving crash data filtering, floating car data filtering and data matching on the road network, is introduced for the safety analysis purpose. Results suggest that the traffic platoon information collected from floating cars accompanied works reasonably in predicting crashes on expressways. The support vector machine, with an overall accuracy of 85%, outperformed the binary logistic model which had an overall accuracy of 60%. Results further suggest the application of floating car technologies and the support vector machine in real-time crash prediction. Despite this, the study also concludes the merits of the binary logistic model over the support vector machine model in explaining the impact of different factors that contribute to crash occurrences.
机译：预测碰撞倾向有助于研究城市高速公路的安全性，以实施对策并加以改进。它还有助于在崩溃发生之前识别并防止崩溃。但是，收集实时的大范围交通信息以进行碰撞预测一直是一项挑战。更重要的是，先前的研究未能考虑碰撞车辆在碰撞发生之前所属的交通排（车辆组）的特征。本文旨在基于通过浮动汽车方法收集的交通排特征来建模碰撞倾向，从而为收集交通信息提供一种省时，可靠的解决方案。碰撞和浮动汽车数据是从中国上海的中环高速公路收集的。二进制逻辑模型和支持向量机都适用。为了安全分析的目的，介绍了一种数据准备方法，包括碰撞数据过滤，浮动汽车数据过滤和道路网络上的数据匹配。结果表明，从浮动汽车中收集的交通排信息可合理地预测高速公路的撞车事故。支持向量机的总体精度为85％，优于二进制逻辑模型的总体精度为60％。结果进一步表明，浮动汽车技术和支持向量机在实时碰撞预测中的应用。尽管如此，该研究还总结了二进制逻辑模型相对于支持向量机模型的优点，可以解释造成事故发生的不同因素的影响。

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