Classification analysis of driver's stop/go decision and red-light running violation

    Authors

    N. Elmitiny; X. D. Yan; E. Radwan; C. Russo;D. Nashar

    Comments

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    Abbreviated Journal Title

    Accid. Anal. Prev.

    Keywords

    Signalized intersections; Yellow signal change; Stop/go decisions; Red-light running; Classification tree; Video-based system; SIGNALIZED INTERSECTIONS; BEHAVIOR; SAFETY; Ergonomics; Public, Environmental & Occupational Health; Social; Sciences, Interdisciplinary; Transportation

    Abstract

    When the driver encounters a signal change from green to yellow, he is required to make a stop or go decision based on his speed and the distance to the stop bar making the wrong decision will lead to a red-light running violation or an abrupt stop at the intersection. In this study, a field data collection was conducted at a high-speed signalized intersection, where a video-based system with three cameras was used to record the drivers' behavior related to the onset of yellow. Observed data include drivers' stop/go decisions, red-light running violation. lane position in the highway. positions (leading/following) in the traffic flow, vehicle type, and vehicles' yellow-onset speeds and distances from the intersection. Further, classification tree models were applied to analyze how the probabilities of a stop or go decision and of red-light running are associated with the traffic parameters. The data analysis indicated that vehicle's distance from the intersection at the onset of yellow, operating speed, and position in the traffic flow are the most important predictors for both the stop/go decision and red-light running violation. This study illustrates that the tree models are helpful to recognize and predict how drivers make stop/go decisions and partake in red-light running violations corresponding to the traffic parameters. (C) 2009 Elsevier Ltd. All rights reserved.

    Journal Title

    Accident Analysis and Prevention

    Volume

    42

    Issue/Number

    1

    Publication Date

    1-1-2010

    Document Type

    Article

    Language

    English

    First Page

    101

    Last Page

    111

    WOS Identifier

    WOS:000272482100013

    ISSN

    0001-4575

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