A method that evaluates the risk of traffic standstills on winter roads in real time using a state space model is proposed herein. In Japan, large-scale anomaly events such as traffic standstills that cause serious road disturbances occur frequently every year because of heavy snowfall. However, if the risk of anomaly events is known in advance, appropriate preparation and management can be undertaken to prevent such events and/or alleviate their impacts on road traffic. Therefore, this study attempts to evaluate the risk of standstills based on the degraded road performance estimated from probe vehicle speeds using sequential Bayesian filtering in a state space model (SSM). The SSM comprises a system model constructed by learning historical data and a measurement model using several exogenous variables such as snowfall amounts and temperature. The risk of anomaly events is then determined as the deviation of the filtered vehicle speed by the SSM from the statistically feasible speed distribution. The validation is performed by applying the proposed model to 58 traffic-standstill cases in northern Japan, and we confirm that the model successfully evaluates risks at a reasonable level that permits the practical use.

本文提出一种使用状态空间模型实时评估冬季道路上交通停滞风险的方法。在日本，由于降雪量大，每年经常发生引起道路严重干扰的大规模异常事件，例如交通停顿。但是，如果事先知道异常事件的风险，则可以进行适当的准备和管理以防止此类事件和/或减轻其对道路交通的影响。因此，本研究试图根据状态空间模型（SSM）中使用顺序贝叶斯滤波，根据探测车速估算出的退化道路性能来评估停车风险。SSM包括一个通过学习历史数据而构建的系统模型和一个使用多个外生变量（例如降雪量和温度）的测量模型。然后，将异常事件的风险确定为SSM过滤后的车速与统计上可行的速度分布之间的偏差。通过将建议的模型应用于日本北部的58个交通停滞案例中，进行了验证，并且我们确认该模型成功地在合理水平上评估了风险，并可以进行实际使用。