Stochastic modeling of the dynamics of incident-induced lane traffic states for incident-responsive local ramp control

Incident-induced traffic congestion has been recognized as a critical issue to solve in the development of advanced freeway incident management systems. This paper investigates the applicability of a stochastic optimal control approach to real-time incident-responsive local ramp control on freeways. The architecture of the proposed ramp control system embeds two primary functions including (1) real-time estimation of incident-induced lane traffic states and (2) dynamic prediction of ramp-metering rates in response to the changes of incident impacts. To accomplish the above two goals, a discrete-time nonlinear stochastic optimal control model is proposed, followed by the development of a recursive prediction algorithm. Based on the simulation data, the numerical results of model tests indicate that the proposed method permits relieving incident impacts particularly under low-volume and medium-volume conditions, relative to high-volume lane-blocking conditions. Particularly, the incident-induced queue lengths can be improved by 50.1% and 67.9%, compared to the existing ramp control and control-free strategies, respectively.