Title

Fuzzy ART neural network model for automated detection of freeway incidents

Authors

Authors

S. S. Ishak; H. M. Al-Deek;Nrc

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Keywords

PATTERNS; Engineering, Civil; Transportation

Abstract

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-s 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-s 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.

Journal Title

Managing Urban Traffic Systems: Freeway Operations, High-Occupancy Vehicle Systems, and Traffic Signal Systems

Issue/Number

1634

Publication Date

1-1-1998

Document Type

Article

Language

English

First Page

56

Last Page

63

WOS Identifier

WOS:000081985000007

ISSN

0361-1981; 0-309-06506-2

Share

COinS