title: Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects.
abstract: In zone-level crash prediction, accounting for spatial dependence has become an extensively studied topic. This study proposes Support Vector Machine (SVM) model to address complex, large and multi-dimensional spatial data in crash prediction. Correlation-based Feature Selector (CFS) was applied to evaluate candidate factors possibly related to zonal crash frequency in handling high-dimension spatial data. To demonstrate the proposed approaches and to compare them with the Bayesian spatial model with conditional autoregressive prior (i.e., CAR), a dataset in Hillsborough county of Florida was employed. The results showed that SVM models accounting for spatial proximity outperform the non-spatial model in terms of model fitting and predictive performance, which indicates the reasonableness of considering cross-zonal spatial correlations. The best model predictive capability, relatively, is associated with the model considering proximity of the centroid distance by choosing the RBF kernel and setting the 10% of the whole dataset as the testing data, which further exhibits SVM models' capacity for addressing comparatively complex spatial data in regional crash prediction modeling. Moreover, SVM models exhibit the better goodness-of-fit compared with CAR models when utilizing the whole dataset as the samples. A sensitivity analysis of the centroid-distance-based spatial SVM models was conducted to capture the impacts of explanatory variables on the mean predicted probabilities for crash occurrence. While the results conform to the coefficient estimation in the CAR models, which supports the employment of the SVM model as an alternative in regional safety modeling.
authors.affiliation: Urban Transport Research Center, School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, 410075 PR China. Electronic address: dongni722@foxmail.com.
authors.forename: Ni
authors.initials: N
authors.lastname: Dong
authors.affiliation: Urban Transport Research Center, School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, 410075 PR China. Electronic address: huanghelai@csu.edu.cn.
authors.forename: Helai
authors.initials: H
authors.lastname: Huang
authors.affiliation: Urban Transport Research Center, School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, 410075 PR China. Electronic address: zhengliang@csu.edu.cn.
authors.forename: Liang
authors.initials: L
authors.lastname: Zheng
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D005431:Florida; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis; D012449:Safety; D062206:Spatial Analysis; D060388:Support Vector Machine
keywords: CAR model; Correlation-based feature selector; Spatial weight features; Support vector machine