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http://dx.doi.org/10.12815/kits.2017.16.1.26

An Analysis on Vehicle Accident Factors of Intersections using Random Effects Tobit Regression Model  

Lee, Sang Hyuk (Highway and Transportation Research Institute, Korea Institute of Civil Engineering and Building Tech.)
Lee, Jung-Beom (Urban Infrastructure Division, Daejeon Sejong Research Institute)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.16, no.1, 2017 , pp. 26-37 More about this Journal
Abstract
The study is to develop safety performance functions(SPFs) for urban intersections using random effects Tobit regression model and to analyze correlations between crashes and factors. Also fixed effects Tobit regression model was estimated to compare and analyze model validation with random effects model. As a result, AADT, speed limits, number of lanes, land usage, exclusive right turn lanes and front traffic signal were found to be significant. For comparing statistical significance between random and fixed effects model, random effects Tobit regression model of total crash rate could be better statistical significance with $R^2_p$ : 0.418, log-likelihood at convergence: -3210.103, ${\rho}^2$: 0.056, MAD: 19.533, MAPE: 75.725, RMSE: 26.886 comparing with $R^2_p$ : 0.298, log-likelihood at convergence: -3276.138, ${\rho}^2$: 0.037, MAD: 20.725, MAPE: 82.473, RMSE: 27.267 for the fixed model. Also random effects Tobit regression model of injury crash rate has similar results of model statistical significant with random effects Tobit regression model.
Keywords
Safety Performance Functions; Random Effects; Fixed Effects; Tobit Regression; Urban Intersections;
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Times Cited By KSCI : 1  (Citation Analysis)
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