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Title: APPLICATION OF ADAPTIVE AND NEURAL NETWORK COMPUTATIONAL TECHNIQUES TO TRAFFIC VOLUME AND CLASSIFICATION MONITORING
Accession Number: 00677633
Record Type: Component
Availability: Find a library where document is available Abstract: A traffic volume and classification monitoring (TVCM) system based on adaptive and neural network computational techniques is being developed. The value of neural networks in this application lies in their ability to learn from data and to form a mapping of arbitrary topology. The piezoelectric strip and magnetic loop sensors typically used for TVCM provide signals that are complicated and variable and that correspond in indirect ways with the desired FHWA 13-class classification system. Furthermore, the wide variety of vehicle configurations adds to the complexity of the classification task. The goal is to provide a TVCM system featuring high accuracy, adaptability to wide sensor and environmental variations, and continuous fault detection. The authors have instrumented an experimental TVCM site, developed personal computer-based on-line data acquisition software, collected a large data base of vehicles' signals together with accurate ground truth determination, and analyzed the data off-line with a neural net classification system that can distinguish between class 2 (automobiles) and class 3 (utility vehicles) vehicles with better than 90% accuracy. The neural network used, called the connectionist hyperprism classification network, features simple basis functions; rapid, linear training algorithms for basis function amplitudes and widths; and basis function elimination that enhances network speed and accuracy. Work is in progress to extend the system to other classes, to quantify the system's adaptability, and to develop automatic fault detection techniques.
Supplemental Notes: This paper appears in Transportation Research Record No. 1466, Issues in Land Use and Transportation Planning, Models, and Applications. Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the Transportation Research Board of the National Academy of Sciences. Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Copyright © National Academy of Sciences. All rights reserved
Monograph Accession #: 01401286
Language: English
Authors: Mead, W CFisher, H NJones, R DBisset, K RLee, L APagination: p. 116-123
Publication Date: 1994
Serial: ISBN: 0309060729
Features: Figures
(10)
; References
(14)
; Tables
(2)
TRT Terms: Uncontrolled Terms: Old TRIS Terms: Subject Areas: Bridges and other structures; Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning
Files: TRIS, TRB
Created Date: May 9 1995 12:00AM
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