00757447

Component

https://doi.org/10.3141/1634-07

http://scholar.google.com...Deek&publication_year=1998

Pattern recognition techniques such as artificial neural networks continue to offer potential solutions to many of the existing problems associated with freeway incident-detection algorithms. This study focuses on the application of Fuzzy ART neural networks to incident detection on freeways. Unlike back-propagation models, Fuzzy ART is capable of fast, stable learning of recognition categories. It is an incremental approach that has the potential for on-line implementation. Fuzzy ART is trained with traffic patterns that are represented by 30-sec loop-detector data of occupancy, speed, or a combination of both. Traffic patterns observed at the incident time and location are mapped to a group of categories. Each incident category maps incidents with similar traffic pattern characteristics, which are affected by the type and severity of the incident and the prevailing traffic conditions. Detection rate and false alarm rate are used to measure the performance of the Fuzzy ART algorithm. To reduce the false alarm rate that results from occasional misclassification of traffic patterns, a persistence time period of 3 min was arbitrarily selected. The algorithm performance improves when the temporal size of traffic patterns increases from one to two 30-sec periods for all traffic parameters. An interesting finding is that the speed patterns produced better results than did the occupancy patterns. However, when combined, occupancy-speed patterns produced the best results. When compared with California algorithms 7 and 8, the Fuzzy ART model produced better performance.

This paper appears in Transportation Research Record No. 1634, Managing Urban Traffic Systems: Freeway Operations, High-Occupancy Vehicle Systems, and Traffic Signal Systems.

English

p. 56-63

1998

Transportation Research Record

0309065062

Figures (4) ; References (18) ; Tables (3)

Algorithms; False alarms (Security); Freeways; Incident detection; Neural networks; Pattern recognition systems; Performance; Speed; Urban areas

Occupancy

Highways; Operations and Traffic Management; Security and Emergencies; I73: Traffic Control

TRIS, TRB, ATRI

Dec 2 1998 12:00AM

• A NEW APPROACH TO HOV ENTRY REQUIREMENTS: MASSHIGHWAY'S 3+/LIMITED 2+ STICKER PROGRAM
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• VEHICULAR FLOW PAST INCIDENTS INVOLVING LANE BLOCKAGE ON URBAN ROADS: A PRELIMINARY EXPLORATION