Department of Civil Environmental and Architectural Engineering, University of Padua, Via Marzolo 9, 35131 Padua, Italy.
Atraki s.r.l., via Diaz 4, 37015 S. Ambrogio di Valpolicella (Verona), Italy.
Accid Anal Prev. 2021 Oct;161:106382. doi: 10.1016/j.aap.2021.106382. Epub 2021 Aug 31.
An innovative approach for real-time road safety analysis is presented in this work. Unlike traditional real-time crash prediction models (RTCPMs), in which crash data are used in the training phase, a real-time conflict prediction model (RTConfPM) is proposed. This model can be trained using surrogate measures of safety, and can therefore be applied even in situations in which highly spatial/temporal-accurate crash data are unavailable or unreliable. The application of an RTConfPM consists of using a set of input variables recorded during a given time interval, to predict whether there will be an increased risk of unsafe situations in the following interval. This paper presents an RTConfPM to predict rear-end crashes, using time-to-collision values recorded with radar sensors on multiple motorway cross-sections to define unsafe situations, and traffic conditions recorded on the same sections as input to the model. The RTConfPM is compared to a traditional RTCPM, trained with a dataset of crashes located on the same motorway, and using the same traffic data as input. In both approaches, variable selection is performed with Pearson's correlation test and random forest; synthetic minority oversampling technique (SMOTE) is used to balance the classes in the training dataset, support vector machine (SVM) is used as classifier, and Monte Carlo cross-validation is adopted for robustness. The two approaches are evaluated considering accuracy, recall, specificity/false alarm rate, and area under the curve (AUC). As shown by the results of this paper, the conflict-based approach appears promising, and is able to predict the occurrence of unsafe situations within 5 min with more than 93% accuracy, recall and specificity, significantly outperforming the RTCPM.
本文提出了一种创新的实时道路安全分析方法。与传统的实时碰撞预测模型（RTCPM）不同，该模型在训练阶段使用碰撞数据，而是提出了一种实时冲突预测模型（RTConfPM）。该模型可以使用安全的替代指标进行训练，因此即使在没有或不可靠的高度时空精确碰撞数据的情况下，也可以应用。应用 RTConfPM 包括使用在给定时间间隔内记录的一组输入变量，来预测在下一个间隔内是否会增加不安全情况的风险。本文提出了一种用于预测追尾碰撞的 RTConfPM，使用在多个高速公路横断面的雷达传感器上记录的碰撞时间值来定义不安全情况，并将在同一部分记录的交通条件作为模型的输入。将 RTConfPM 与使用位于同一条高速公路上的碰撞数据集进行训练的传统 RTCPM 进行了比较，并将相同的交通数据作为输入。在这两种方法中，使用 Pearson 相关测试和随机森林进行变量选择；使用合成少数过采样技术（SMOTE）来平衡训练数据集中的类别，使用支持向量机（SVM）作为分类器，并采用蒙特卡罗交叉验证来提高稳健性。通过本文的结果评估，基于冲突的方法具有很大的发展潜力，能够以超过 93%的准确性、召回率和特异性，在 5 分钟内预测不安全情况的发生，显著优于 RTCPM。
