Real-time crash prediction helps identify and prevent the occurrence of traffic crash. For years, various real-time crash prediction models have been investigated to provide effective information for proactive traffic management. When building real-time crash prediction model, a suitable variable space together with a specific time interval for traffic data aggregation and an appropriate modelling algorithm should be applied. Regarding the intercorrelation problem with variable space, comprehensive real-time crash prediction model considering available traffic data characteristics in applicable circumstances needs to be explored. Taking Xi’an G3001 Expressway as study area, real road traffic and accident data during the period from January 2014 to January 2019 on this expressway are applied for real-time crash prediction. To better capture traffic flow characteristics on expressway and improve the practicality of real-time crash prediction model, two new variables (segment difference coefficient and lane difference coefficient) describing the smoothness and continuity of traffic flow in spatial dimension are developed and incorporated in building the crash prediction model to solve the intercorrelation problem with variable space. Random forest (RF) is then adopted to specify the quantitative relationship between specific variable and crash risk. Real-time crash prediction model based on support vector machine (SVM) using new composed variable space is built. The results show that simplified variable space could contribute to the same classification power in currently used real-time crash prediction models compared with traditional variable space. Moreover, the prediction model based on SVM reaches an accuracy level of 0.9, which performs better than other currently used prediction models.

中文翻译：

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基于机器学习方法的融合交通流连续性参数的高速公路车祸预测

实时崩溃预测有助于识别并防止流量崩溃的发生。多年来，已经研究了各种实时崩溃预测模型，以为主动的流量管理提供有效的信息。在建立实时碰撞预测模型时，应使用适当的可变空间以及用于交通数据聚合的特定时间间隔和适当的建模算法。对于空间可变的互相关问题，需要探索在适用情况下考虑可用交通数据特征的综合实时碰撞预测模型。以西安G3001高速公路为研究区域，对该高速公路2014年1月至2019年1月的实际道路交通和事故数据进行实时碰撞预测。为了更好地捕捉高速公路上的交通流特征并提高实时碰撞预测模型的实用性，开发了两个新变量（段差系数和车道差系数），这些变量描述了交通流在空间维度上的平滑性和连续性，并将其纳入到构建中。碰撞预测模型，用于解决可变空间的互相关问题。然后采用随机森林（RF）来指定特定变量与崩溃风险之间的定量关系。建立了基于支持向量机（SVM）的实时碰撞预测模型，并使用了新的组合变量空间。结果表明，与传统变量空间相比，简化的变量空间可以在当前使用的实时碰撞预测模型中贡献相同的分类能力。而且，