Predicting motor vehicle crashes using Support Vector Machine models

  • Li X
  • Lord D
  • Zhang Y
 et al. 
  • 58

    Readers

    Mendeley users who have this article in their library.
  • 112

    Citations

    Citations of this article.

Abstract

Crash prediction models have been very popular in highway safety analyses. However, in highway safety research, the prediction of outcomes is seldom, if ever, the only research objective when estimating crash prediction models. Only very few existing methods can be used to efficiently predict motor vehicle crashes. Thus, there is a need to examine new methods for better predicting motor vehicle crashes. The objective of this study is to evaluate the application of Support Vector Machine (SVM) models for predicting motor vehicle crashes. SVM models, which are based on the statistical learning theory, are a new class of models that can be used for predicting values. To accomplish the objective of this study, Negative Binomial (NB) regression and SVM models were developed and compared using data collected on rural frontage roads in Texas. Several models were estimated using different sample sizes. The study shows that SVM models predict crash data more effectively and accurately than traditional NB models. In addition, SVM models do not over-fit the data and offer similar, if not better, performance than Back-Propagation Neural Network (BPNN) models documented in previous research. Given this characteristic and the fact that SVM models are faster to implement than BPNN models, it is suggested to use these models if the sole purpose of the study consists of predicting motor vehicle crashes. © 2008 Elsevier Ltd. All rights reserved.

Author-supplied keywords

  • Crash
  • Highway
  • Negative binomial model
  • Neural network
  • Support Vector Machine

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Xiugang Li

  • Dominique Lord

  • Yunlong Zhang

  • Yuanchang Xie

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free