Application of Fuzzy Logic and Neural Networks to Automatically Detect Freeway Traffic Incidents
Publication: Journal of Transportation Engineering
Volume 120, Issue 5
Abstract
To date, efforts to manage freeway congestion have been seriously impeded by the inability to promptly and reliably detect the presence of traffic incidents. Traditional incident‐detection algorithms distinguish between congested and uncongested operation by comparing measured traffic‐stream parameters with predefined threshold values. Given the range of possible operating conditions in the traffic stream, selecting a single threshold value, and the suitability of that selected threshold, is full of uncertainty. This inherent uncertainty makes fuzzy logic a promising approach to incident detection. A Fuzzy Logic Incident Patrol System (FLIPS) is proposed to solve many of the problems inherent in traditional incident‐detection algorithms. The FLIPS combines fuzzy logic with the learning capabilities of neural networks to form a connectionist model. The system can be constructed automatically from training examples to find the optimal input/output membership functions. Threshold values, implicitly obtained by fuzzy‐logic rules and membership functions, are treated as dependent variables, which change according to prevailing traffic‐stream parameters measured by detectors. The FLIPS avoids the rule‐matching time of the inference engine in the traditional fuzzy‐logic system. The potential effectiveness of the FLIPS is evaluated using an empirical database collected in Toronto, Canada. Future refinement to the FLIPS are also discussed in this paper.
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References
1.
Ahmed, S. A. (1983). “Stochastic processes in freeway traffic part II: incident detection algorithms.” Traffic Engrg. and Control, 24(6–7), 309–310.
2.
Braae, M., and Rutherford, D. A. (1978). “Fuzzy relations in a control setting.” Kybernetes, 7(3), 185–188.
3.
Chen, F. C. (1989). “Back‐propagation neural network for nonlinear self‐tuning adaptive control.” Proc., IEEE Intelligent Machine, 274–279.
4.
Cook, A. R., and Cleveland, D. E. (1974). “Detection of freeway capacity‐reducing incidents by traffic‐stream measurements.” Transp. Res. Record No. 495, TRB, National Research Council, Washington, D.C., 1–11.
5.
Courage, K. G., and Levin, M. (1968). “A freeway corridor surveillance, information, and control system.” Texas Transp. Inst., Texas A&M University, College Station, Res. Rep. 488‐8, December, 349.
6.
Dudek, C. L., and Messer, C. J. (1974). “Incident detection on urban freeways.” Transp. Res. Record No. 495, TRB, National Research Council, Washington, D.C., 12–24.
7.
Fambro, D. B., and Ritch, G. P. (1980). “Evaluation of an algorithm for detecting urban freeway incidents during low‐volume conditions.” Transp. Res. Record No. 773, TRB, National Research Council, Washington, D.C., 31–39.
8.
Gall, A. I., and Hall, F. L. (1989). “Distinguishing between incident congestion and recurrent congestion: a proposed logic.” Transp. Res. Record No. 1232, TRB, National Research Council, Washington, D.C., 1–8.
9.
Gallant, S. I. (1988). “Connectionist expert systems.” Commun. ACM, 31(2), 152–169.
10.
Hall, F. L., Yong Shi, and George, A. (1993). “On‐line testing of the McMaster incident detection algorithm under recurrent congestion.” 72nd Annu. Meeting, Transportation Research Board, Washington, D.C.
11.
Hinton, G. E.,McClelland, J. L., and Rumelhart, D. E. (1986). “Distributed representations.” Parallel distributed processing, Vol. 1. M.I.T. Press, Cambridge, Mass., 77–109.
12.
Hinton, G. E. (1989). “Connectionist learning procedures.” Artificial Intelligence, 40(1), 143–150.
13.
Kosko, B. (1992). Neural networks and fuzzy systems. Prentice‐Hall Inc., Englewood Cliffs, N.J.
14.
Lee, C. C. (1990). “Fuzzy logic in control systems: fuzzy logic controller part I & II.” IEEE Transp. Systems Man, Cybern., SMC‐20(2), 404–435.
15.
Li, Y. F., and Lan, C. C. (1989). “Development of fuzzy algorithms for servo ystems.” IEEE Contr. Syst. Mag., April, 65–72.
16.
Lin, Chin‐Teng, and Lee, C. S. G. (1991). “Neural‐network‐based fuzzy logic control and decision system.” IEEE Trans. on Comp., 40(12), 1320–1336.
17.
Lotan, T., and Koutsopoulos, H. N. (1992). “Fuzzy control and approximate reasoning models for route choice in the presence of information.” 71st Annu. Meeting of Transp. Res. Board, Washington, D.C.
18.
Moody, J., and Darken, C. J. (1989). “Fast learning in networks of locally‐tuned processing units.” Neural Computation, Vol. 1, 281–294.
19.
Payne, H. J., and Tignor, S. C. (1978). “Freeway incident‐detection algorithms based on decision trees with states.” Transp. Res. Record No. 682, TRB, National Research Council, Washington, D.C., 30–37.
20.
Persaud, B. N., Hall, F. L., and Hall, L. M. (1990). “Congestion identification aspects of the McMaster incident detection algorithm.” Transp. Res. Record No. 1287, TRB, National Research Council, Washington, D.C., 167–175.
21.
Scharf, E. M., and Mandic, N. J. (1985). “The application of a fuzzy controller to the control of a multi‐degree‐freedom robot arm.” Industrial Applications of Fuzzy Control, M. Sugeno, Ed., Amsterdam, North‐Holland, 41–62.
22.
Self, K. L. (1990). “Fuzzy logic design.” IEEE Spectrum, Vol. 27, 42–44, 105.
23.
Stephanedes, Y. J., and Chassiakos, A. P. (1991a). “Application of filtering techniques for incident detection.” Dept. of Civ. and Min. Engrg. University of Minnesota, Minneapolis, Minnesota.
24.
Stephanedes, Y. J., and Chassiakos, A. P. (1991b). “A low pass filter for incident detection.” Proc., 2nd Int. Conf. Application on Advanced Technologies in Transp. Engrg., Minneapolis, Minn., 378–382.
25.
Sugeno, M. (1985). Industrial Applications of Fuzzy Control. Amsterdam, North‐Holland.
26.
Terano, T., Asai, K., and Sugeno, M. Fuzzy Systems Theory and Its Applications. Academic Press Inc., San Diego, Calif.
27.
Touretzky, D. S., and Hinton, G. E. (1986). “A distributed connectionist production system.” CMU‐CS‐86‐172, Tech. Rep., Carnegie Mellon University, Pittsburgh, Pa.
28.
Turksen, I. B. (1986). “Measurement of membership functions.” Applications of Fuzzy Set Theory in Human Factors. W. Karwowski and A. Mital, Eds.
29.
West, J. T. (1971). “California makes its move.” Traffic Engrg., 41(4), 12–18.
30.
Zadeh, L. A. (1988). “Fuzzy logic.” IEEE Computing Mag., April, 83–93.
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Copyright © 1994 American Society of Civil Engineers.
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Received: Aug 31, 1992
Published online: Sep 1, 1994
Published in print: Sep 1994
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