Analyzing angle crashes at unsignalized intersections using machine learning techniques

    Authors

    M. Abdel-Aty;K. Haleem

    Comments

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

    Abbreviated Journal Title

    Accid. Anal. Prev.

    Keywords

    Data mining; Machine learning; Multivariate adaptive regression splines; MARS; Angle crash; Random forest; Unsignalized intersections; Crash; prediction; ADAPTIVE REGRESSION SPLINES; PREDICTION; MODELS; MARS; Ergonomics; Public, Environmental & Occupational Health; Social; Sciences, Interdisciplinary; Transportation

    Abstract

    A recently developed machine learning technique, multivariate adaptive regression splines (MARS), is introduced in this study to predict vehicles' angle crashes. MARS has a promising prediction power, and does not suffer from interpretation complexity. Negative Binomial (NB) and MARS models were fitted and compared using extensive data collected on unsignalized intersections in Florida. Two models were estimated for angle crash frequency at 3- and 4-legged unsignalized intersections. Treating crash frequency as a continuous response variable for fitting a MARS model was also examined by considering the natural logarithm of the crash frequency. Finally, combining MARS with another machine learning technique (random forest) was explored and discussed. The fitted NB angle crash models showed several significant factors that contribute to angle crash occurrence at unsignalized intersections such as, traffic volume on the major road, the upstream distance to the nearest signalized intersection, the distance between successive unsignalized intersections, median type on the major approach, percentage of trucks on the major approach, size of the intersection and the geographic location within the state. Based on the mean square prediction error (MSPE) assessment criterion, MARS outperformed the corresponding NB models. Also, using MARS for predicting continuous response variables yielded more favorable results than predicting discrete response variables. The generated MARS models showed the most promising results after screening the covariates using random forest. Based on the results of this study. MARS is recommended as an efficient technique for predicting crashes at unsignalized intersections (angle crashes in this study). (C) 2010 Elsevier Ltd. All rights reserved.

    Journal Title

    Accident Analysis and Prevention

    Volume

    43

    Issue/Number

    1

    Publication Date

    1-1-2011

    Document Type

    Article

    Language

    English

    First Page

    461

    Last Page

    470

    WOS Identifier

    WOS:000285272400055

    ISSN

    0001-4575

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