Title

Analyzing angle crashes at unsignalized intersections using machine learning techniques

Authors

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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