Incorporating multiple congestion levels into spatiotemporal analysis for the impact of a traffic incident.

Traffic incidents occurring on the road interrupt the smooth mobility of traffic flow and lead to traffic congestion. Although there has been a proliferation of studies that attempt to estimate the spatiotemporal impact of a traffic incident, most, if not all, of them focus exclusively on the differentiation of bi-level traffic status. In this research, we propose to incorporate multiple congestion levels in the spatiotemporal analysis for the impact of a traffic incident, which is new to the literature. The input to our model includes the historical speed on a given road and the occurrence time and location of the incident. The model then outputs the spatiotemporal impact region with multiple congestion levels. We first use a discriminant indicator to initially indicate the traffic status according to the travelling speed of probe vehicles. We then develop an integer programming model with a set of novel constraints to estimate the spatiotemporal region impacted by the incident. Unlike existing studies that only distinguish between uncongested and congested status, our model allows us to determine the impact region with diverse congestion levels. We validate our model using both simulation and real data. Results demonstrate that our approach can not only ensure the consistency of the propagation of shockwaves even when multiple congestion levels are incorporated, but also produce more accurate estimation of the delay caused by the incident when compared to the current state-of-the-art approach.