A Generic Approach to Real-Time Freeway Network Traffic Surveillance

The presentation addresses a generic macroscopic model-based approach to real-time freeway network traffic surveillance as well as a software tool RENAISSANCE that has recently been developed to implement the approach for field applications. On the basis of stochastic macroscopic freeway network traffic flow modeling, extended Kalman filtering, and a limited amount of traffic measurements, RENAISSANCE enables a number of real-time freeway network traffic surveillance tasks, including traffic state estimation and prediction, travel time estimation and prediction, queue tail/head/length estimation and prediction (queue tracking), and incident alarms. This presentation introduces the utilized macroscopic freeway network traffic flow model and a real-time traffic measurement model, upon which a complete dynamic system model for freeway network traffic is established with special attention to the handling of some important model parameters. An outline is given of various algorithms and the functional architecture of RENAISSANCE. Simulation testing results of the major RENAISSACNE functions are presented with respect to a hypothetical freeway network example. A number of real-data testing results concerning a 7-km German freeway stretch are presented, focusing on the RENAISSANCE traffic state estimation function under various circumstances regarding congestion, weather conditions and traffic incidents. Some recent real-data testing results of the traffic state estimator for a large-scale freeway network in Southern Italy is also presented. Finally, an outlook of further issues and RENAISSANCE applications is given.