Transferability of Freeway Incident Detection Algorithms

Abstract

This research seeks to evaluate the potential for transferability of new freeway incident detection algorithms that can be used in automatic incident detection. An incident detection logic is evaluated that distinguishes incidents from recurrent congestion and other traffic disturbances using exponential smoothing. The algorithm is tested with loop detector data from test sites on I-35W in Minnesota and I-880 in California with promising results. Test results indicate that, at the Minnesota test site, algorithm performance improves over earlier findings with a limited data set, without requiring recalibration. At a detection rate of approximately 60 percent, the algorithm produces one false alarm per hour in the total (southbound and northbound) 40-km (25-mi) test section, which includes 54 detector stations. When the algorithm is recalibrated and tested at the California site, its performance further improves. The performance of the algorithm is compared with that of major algorithms of comparative type and is found to be superior at all times. This superiority is not affected by the transport of the algorithm across sites; it is still observed in analyzing northbound and southbound, peak and off-peak, and accident versus nonaccident detection. The findings indicate a strong transferability potential of the new algorithm.

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References

Lindley

J. A.

Urban Freeway Congestion: Quantification of the Problem and Effectiveness of Potential Solutions. ITE Journal, Vol. 57, No. 1, pp. 27–32.

Stephanedes

Y. J.

, and Chassiakos

A. P.

. Application of Filtering Techniques for Incident Detection. Journal of Transportation Engineering, ASCE, Vol. 119, No. 1, Jan.–Feb. 1993, pp. 13–26.

Chassiakos

A. P.

, and Stephanedes

Y. J.

. Smoothing Algorithms for Incident Detection. In Transportation Research Record 1394, TRB, National Research Council, Washington, D.C., 1993, pp. 8–16.

Payne

H. J.

, and Tignor

S. C.

. Freeway Incident-Detection Algorithms Based on Decision Trees With States. In Transportation Research Record 682, TRB, National Research Council, Washington, D.C., 1978, pp. 30–37.

Levin

, and Krause

G. M.

. Incident Detection: A Bayesian Approach. In Transportation Research Record 682, TRB, National Research Council, Washington, D.C., 1978, pp. 52–58.

CORD. Incident Detection Review. Incident Detection Task Force, DRIVE, Brussels, Belgium, 1994.

Cohen

Comparative Assessment of Conventional and New Incident Detection Algorithms. Proc., 7th International Conference on Road Traffic Monitoring and Control, Institution of Electrical Engineers, London, England, April 1994, pp. 156–159.

Dudek

C. L.

, Messer

C. J.

, and Nuckles

N. B.

. Incident Detection on Urban Freeways. In Transportation Research Record 495, TRB, National Research Council, Washington, D.C., 1974, pp. 12–24.

Cook

A. R.

, and Cleveland

D. E.

. Detection of Freeway Capacity-Reducing Incidents by Traffic-Stream Measurements. In Transportation Research Record 495, TRB, National Research Council, Washington, D.C., 1974, pp. 1–11.

10.

Ahmed

S. A.

, and Cook

A. R.

. Discrete Dynamic Models for Freeway Incident Detection Systems. Transportation Planning and Technology, Vol. 7, 1982, pp. 231–242.

11.

Persaud

B. N.

, Hall

F. L.

, and Hall

L. M.

. Congestion Identification Aspects of the McMaster Incident Detection Algorithm. In Transportation Research Record 1287, TRB, National Research Council, Washington, D.C., 1990, pp. 167–175.

12.

Antoniades

, and Stephanedes

Y. J.

. Single Station Incident Detection for Sparsely Instrumented Freeway Sites. Proc., 4th International Conference on Applications of Advanced Technologies in Transportation Engineering, ASCE, Capri, Italy, 1995, pp. 228–221.