title: Predicting motor vehicle crashes using Support Vector Machine models.
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.
authors.affiliation: Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843-3136, USA. li_xiugang@tamu.edu
authors.forename: Xiugang
authors.initials: X
authors.lastname: Li
authors.forename: Dominique
authors.initials: D
authors.lastname: Lord
authors.forename: Yunlong
authors.initials: Y
authors.lastname: Zhang
authors.forename: Yuanchang
authors.initials: Y
authors.lastname: Xie
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000465:Algorithms; D004779:Environment Design; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012044:Regression Analysis; D013781:Texas