Fundamental to the operation of most intelligent vehicle-highwaysystems (ivhs) projects are advanced systems for surveillance, control, and management of integrated freeway and arterial networks. A major concern in the development of so-called "smart roads" is the provision of decision support for traffic management center personnel, particularly for addressing nonrecurring congestion in large or complex networks. Decision support for control room staff is necessary to detect, verify, and develop effective response strategies for traffic incidents. These incidents are events that disrupt the orderly flow of traffic and cause nonrecurring congestion and motorist delay. Nonrecurring congestion can be caused by accidents, spilled loads, stalled or broken-down vehicles, maintenance and construction activities, signal and detector malfunctions, and special or unusual events. An attempt was made to implement a novel, artificial intelligence-based approach to the problem of providing operator decision support in integrated freeway and arterial traffic management systems, aspart of a more general ivhs. The development of the freeway real-time expert system demonstration (fred), a component prototype real-time expert system for managing nonrecurring congestion on urban freeways in southern california, is discussed. The application of fred toa section of the riverside freeway (sr-91) in orange county is presented as a case study and illustrates the current capabilities of the system. This paper appears in transportation research record no. 1320, Freeway operations, highway capacity, and traffic flow 1991.
Abstract