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Application of fuzzy logic and neural networks for dynamic travel time estimation
Author(s) -
Palacharla P.V.,
Nelson P.C.
Publication year - 1999
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/j.1475-3995.1999.tb00148.x
Subject(s) - fuzzy logic , computer science , estimation , artificial neural network , artificial intelligence , economics , management
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.