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

Abstract

The effectiveness of loop detectors as a data source for advanced traveler information systems has been researched recently [V. P. Sisiopiku (1993 Travel Time Estimation from Loop Detector Data for Advanced Traveler Information System Applications, Ph.D. Thesis, University of Illinois at Chicago]. 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 [P. Nelson and P. Palacharla (1993) A neural network model for data fusion in ADVANCE, Pacific Rim Transportation Technology Conference Proceedings, Vol. I, pp. 237-243, Seattle, WA, 1993]. Literature review indicates limited knowledge on the actual relationship between travel times and loop detector data under interrupted traffic conditions [V. P. Sisiopiku and N. M. Rouphail (1994) Towards the Use of Detector Output for Arterial Link Travel Time Estimation: a Literature Review. Transportation Research Record Series, Washington, DC]. 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. © 1999 Blackwell Publishing Ltd.