Application of GLASSO in Variable Selection and Crash Prediction at Unsignalized Intersections

    Authors

    K. Haleem;M. Abdel-Aty

    Comments

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    Abbreviated Journal Title

    J. Transp. Eng.

    Keywords

    Group least absolute shrinkage and selection operator; GLASSO; Negative; binomial; Random forest; 3-Legged unsignalized intersection; 4-Legged; unsignalized intersection; Crash prediction; ADAPTIVE REGRESSION SPLINES; REAR-END CRASHES; SIGNALIZED INTERSECTIONS; MODELS; LASSO; Engineering, Civil; Transportation Science & Technology

    Abstract

    In this paper, a new promising variable screening technique is proposed to select important covariates and to improve crash prediction; the group least absolute shrinkage and selection operator (GLASSO). The GLASSO's main power lies in its ability to deal with data sets havinga large number of categorical variables, the case in this study. Identifying the significant factors affecting the safety of unsignalized intersections was also an essential objective. Two applications of GLASSO were investigated: before fitting the negative binomial (NB) model, and before fitting the promising multivariate adaptive regression splines (MARS) technique using extensive data representing 2,475 unsignalized intersections. Regarding the NB models, GLASSO yielded close prediction capability to the backward deletion and random forest techniques. Also, MARS model fitting after using GLASSO relatively outperformed that after using random forest, with similar prediction performance. Because of its outstanding performance with categorical variables and its simplicity, GLASSO is recommended as a promising variable selection technique. Some significant predictors affecting unsignalized intersections' safety were traffic volume on the major road, upstream and downstream distances to the nearest signalized intersection, and median type on major and minor approaches. DOI: 10.1061/(ASCE)TE.1943-5436.0000398. (C) 2012 American Society of Civil Engineers.

    Journal Title

    Journal of Transportation Engineering

    Volume

    138

    Issue/Number

    7

    Publication Date

    1-1-2012

    Document Type

    Article

    Language

    English

    First Page

    949

    Last Page

    960

    WOS Identifier

    WOS:000311388200013

    ISSN

    0733-947X

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