Department of Civil Engineering, Tsinghua University, Beijing 100084, China; Department of Civil and Environment Engineering, University of California, Berkeley, CA 94720, United States.
Department of Civil Engineering, Tsinghua University, Beijing 100084, China.
Accid Anal Prev. 2020 Sep;145:105696. doi: 10.1016/j.aap.2020.105696. Epub 2020 Jul 21.
Traffic accident management is a critical issue for advanced intelligent traffic management. The increasingly abundant crowdsourcing data and floating car data provide new support for improving traffic accident management. This paper investigates the methods to predict the complicated behavior of traffic flow evolution after traffic accidents using crowdsourcing data. Based on the available data source, the traffic condition is divided into four levels by congestion delay index: severely congested, congested, slow moving and uncongested. Four types of accidents are consequently defined based on the occurrence of each level. A hierarchical scheme is designed for identifying the most congested level and sequentially predicting duration of each level. The proposed model is validated using traffic accident data in 2017 from an anonymous source in Beijing, China by embedding three machine learning algorithms, random forest (RF), support vector machine (SVM) and neural network (NN), in the scheme. The results show NN outperforms the other two models when the assessment is conducted in absolute differences. Meanwhile, RF has a slightly better performance than SVM, especially when predicting the short-period congestion of severely congested level at the first time. By continuously updating the traffic condition information, significant improvement in accuracy can be acquired regardless of the exact model used. This study shows that emerging crowdsourcing data can be used in a real-time analysis of traffic accidents and the proposed model is effective to analyze such data.
交通事故管理是先进智能交通管理的一个关键问题。日益丰富的众包数据和浮动车数据为改进交通事故管理提供了新的支持。本文研究了利用众包数据预测交通事故后交通流演化复杂行为的方法。基于可用数据源，通过拥挤延迟指数将交通状况分为四级：严重拥挤、拥挤、缓慢移动和不拥挤。然后根据每个级别发生的情况定义了四种类型的事故。设计了一个分层方案来识别最拥挤的级别，并依次预测每个级别的持续时间。该模型使用中国北京一个匿名来源的 2017 年交通事故数据进行验证，通过在方案中嵌入三种机器学习算法，即随机森林（RF）、支持向量机（SVM）和神经网络（NN）。结果表明，在绝对差异评估中，NN 优于其他两种模型。同时，RF 比 SVM 的性能略好，特别是在第一次预测严重拥挤级别短时间内的拥挤时。通过不断更新交通状况信息，无论使用的确切模型如何，都可以获得显著的准确性提高。本研究表明，新兴的众包数据可用于实时分析交通事故，所提出的模型可有效分析此类数据。
