A stochastic estimation approach to real-time prediction of incident effects on freeway traffic congestion

Abstract

Real-time prediction of the effects of freeway incidents on traffic congestion is urgently necessary for the development of advanced freeway incident management systems. This paper presents a stochastic estimation approach to real-time prediction of time-varying delays and queue lengths which are regarded as two significant variables in examining freeway incident congestion in this study. In addition to system specification utilizing four groups of proposed lane traffic variables, a stochastic estimation approach which involves a discrete-time nonlinear stochastic model and an algorithm based on Kalman filtering is developed to estimate real-time delays and queues in the presence of freeway incidents. The proposed method is tested employing simulated data generated via the CORSIM simulation model. The preliminary test results indicate that the proposed method is promising. Utilizing the estimates of delays and queue lengths generated by the proposed method in real time, our further research will aim at developing time-varying incident effect indexes for real-time prediction of the impact magnitude of freeway incidents either in the temporal domain or in the spatial domain. We therefore expect that this study can make available real-time incident-related traffic information with benefits not only for understanding the impact of freeway incidents on traffic congestion, but also for developing advanced incident-responsive traffic management technologies.