Application of fuzzy logic and neural networks for dynamic travel time estimation

The effectiveness of loop detectors as a data source for advanced traveler information systems has been researched recently. In urban traffic control schemes loop detectors provide on-line information on traffic conditions consisting of volume counts and occupancy levels. The need to convert loop detector data into travel times is recognized mostly in data fusion applications. Literature review indicates limited knowledge on the actual relationship between travel times and loop detector data under interrupted traffic conditions. Currently available statistical regression models cannot capture the dynamics of traffic conditions under signalized control and suffer from limited calibration and empirical validation. This paper presents a fuzzy reasoning model to convert loop detector data into link travel times obtained from empirical studies. This model incorporates flexible reasoning and captures non-linear relationship between link specific detector data and travel times.