A data fusion framework for real-time risk assessment on freeways

    Authors

    M. Ahmed;M. Abdel-Aty

    Comments

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    Abbreviated Journal Title

    Transp. Res. Pt. C-Emerg. Technol.

    Keywords

    Freeway safety; Automatic Vehicle Identification; Remote Traffic; Microwave Sensor; Data mining; Data fusion; Stochastic Gradient Boosting; CRASH PREDICTION; WEATHER; Transportation Science & Technology

    Abstract

    The increased deployment of non-intrusive detection systems such as Automatic Vehicle Identification (AVI) and Remote Traffic Microwave Sensors (RTMSs) provides access to real-time traffic data from multiple sources. The availability of such rich data enhances the reliability of travel time estimation and route guidance systems, however, utilization of these data is absent in the context of proactive safety management systems. This paper presents a framework for real-time risk assessment on a freeway in Colorado by fusing data from two different detection systems (AVI and RTMS), real-time weather and roadway geometry. Stochastic Gradient Boosting (SGB), a relatively recent and promising machine learning technique is used to calibrate the models. SGB's key strengths lie in its capability to fit complex nonlinear relationships, handling different types of predictors (nominal and categorical) and accommodating missing values with no need for prior transformation of the predictor variables or elimination of outliers. Boosting multiple simple trees together overcomes the drawback of single tree models of poor prediction accuracy and provides fast and superior predictive performance. The proposed framework is considered a good alternative for real-time risk assessment on freeways because of its high estimation accuracy, robustness and reliability. (C) 2012 Elsevier Ltd. All rights reserved.

    Journal Title

    Transportation Research Part C-Emerging Technologies

    Volume

    26

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    203

    Last Page

    213

    WOS Identifier

    WOS:000315421300014

    ISSN

    0968-090X

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