Performance of automatic ANN-based incident detection on freeways

    Authors

    S. Ishak;H. Al-Deek

    Comments

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    Abbreviated Journal Title

    J. Transp. Eng.-ASCE

    Keywords

    FUZZY ART; PATTERNS; Engineering, Civil; Transportation Science & Technology

    Abstract

    Automatic incident detection on freeways is an essential ingredient for the successful deployment of Intelligent Transportation Systems. Several incident detection algorithms have been developed in the past three decades; however, most of them have not shown the anticipated performance in terms of detection rate and false alarm rate. Recently, the artificial neural networks (ANN) have been introduced to incident detection and shown success over the traditional algorithms. This study explores the application of two neural network models, namely, the Multi-Layer Feed-Forward and the Fuzzy ART algorithm. This study was conducted on the central corridor of 1-4 in Orlando using real-world data collected via the traffic surveillance system. Different scenarios were considered to improve the performance and to capture the sensitivity of the developed algorithms to some factors. The study results showed that the Fuzzy ART algorithm has generally outperformed the Multi-Layer Feed-Forward network and California algorithms #7 and #8.

    Journal Title

    Journal of Transportation Engineering-Asce

    Volume

    125

    Issue/Number

    4

    Publication Date

    1-1-1999

    Document Type

    Article

    Language

    English

    First Page

    281

    Last Page

    290

    WOS Identifier

    WOS:000080938100002

    ISSN

    0733-947X

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