Stochastic modeling and real-time estimation of incident effects on surface street traffic congestion

Real-time prediction of the effects of arterial incidents on traffic congestion is a significant factor in the development of advanced incident management systems. This paper explores a stochastic modeling approach to real-time prediction of incident effects on surface traffic congestion. To formulate the incident-induced traffic congestion problems for surface street arterial incident cases, inter-lane and intra-lane traffic variables are specified, followed by the development of a discrete-time, nonlinear stochastic model and a recursive estimation algorithm for the application of real-time prediction. The proposed method is tested with simulated data generated using the Paramics traffic simulator The preliminary tests indicated the capability of the proposed method in estimating incident effects on surface street traffic congestion in real time. We expect that this study can provide realtime incident-related traffic information with benefits not only for understanding the impact of incidents on non-recurrent traffic congestion of surface streets, but also for developing advanced incident-responsive traffic control and management technologies