DYNAMIC MODEL-BASED TECHNIQUES FOR THE DETECTION OF INCIDENTS ON FREEWAYS*

In this paper we discuss an approach to the detection of incidents on freeways. Our techniques are based on the use of a macroscopic dynamic model describing the evolution of spatial-average traffic variables (velocities, flows, and densities) over sections of the freeway. With such a model as a starting point we develop two incident detection algorithms based on the multiple model and generalized likelihood ratio techniques. We also describe a new and very simple system for processing raw data from presence-type vehicle detectors to produce estimates of the aggregate variables, which are then in turn used as the input variables to the incident detection algorithms. Simulation results using a microscopic simulation of a two-lane freeway indicate that: (1) our algorithms are robust to the differences between the dynamics of actual traffic and the aggregated dynamics used to design the detection systems; and (2) our methods appear to work as well as existing algorithms in heavy traffic conditions and work better in moderate to light traffic. Areas for future work are outlined at the end of the paper