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http://dx.doi.org/10.7470/jkst.2014.32.4.369

A Crash Prediction Model for Expressways Using Genetic Programming  

Kwak, Ho-Chan (Integrated Research Institute of Construction and Environmental Engineering, Seoul National University)
Kim, Dong-Kyu (Integrated Research Institute of Construction and Environmental Engineering, Seoul National University)
Kho, Seung-Young (Department of Civil and Environmental Engineering, Seoul National University)
Lee, Chungwon (Department of Civil and Environmental Engineering, Seoul National University)
Publication Information
Journal of Korean Society of Transportation / v.32, no.4, 2014 , pp. 369-379 More about this Journal
Abstract
The Statistical regression model has been used to construct crash prediction models, despite its limitations in assuming data distribution and functional form. In response to the limitations associated with the statistical regression models, a few studies based on non-parametric methods such as neural networks have been proposed to develop crash prediction models. However, these models have a major limitation in that they work as black boxes, and therefore cannot be directly used to identify the relationships between crash frequency and crash factors. A genetic programming model can find a solution to a problem without any specified assumptions and remove the black box effect. Hence, this paper investigates the application of the genetic programming technique to develope the crash prediction model. The data collected from the Gyeongbu expressway during the past three years (2010-2012), were separated into straight and curve sections. The random forest technique was applied to select the important variables that affect crash occurrence. The genetic programming model was developed based on the variables that were selected by the random forest. To test the goodness of fit of the genetic programming model, the RMSE of each model was compared to that of the negative binomial regression model. The test results indicate that the goodness of fit of the genetic programming models is superior to that of the negative binomial models.
Keywords
crash prediction; expressway; genetic programming; random forest; traffic safety;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 Kang J. G., Lee S. H. (2002), Traffic Accident Prediction Model by Freeway Geometric Types, J. Korean Soc. Transp., 20(4), Korean Society of Transportation, 163-175.   과학기술학회마을
2 Kang M. W., Doh T. W., Son B. S. (2002), Fitting Distribution of Accident Frequency of Freeway Horizontal Curve Sections & Development of Negative Binomial Regression Models, J. Korean Soc. Transp., 20(7), Korean Society of Transportation, 197-204.   과학기술학회마을
3 Kononov J., Bailey B., Allery B. K. (2008), Relationships Between Safety and Both Congestion and Number of Lanes on Urban Freeways, TRR, 2083, TRB, 26-39.
4 Kononov J., Lyon C., Allery B. K. (2011), Relation of Flow, Speed, and Density of Urban Freeways to Functional Form of a Safety Performance Function, TRR, 2236, TRB, 11-19.
5 Koza J. R. (1992), Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press (Cambridge, MA, USA), 73.
6 Li X., Lord D., Zhang Y., Xie Y. (2008), Predicting Motor Vehicle Crashes Using Support Vector Machine Models, Accid. Anal. Prev., 40(4), 1611-1618.   DOI   ScienceOn
7 Liaw A., Wiener M. (2002), Classification and Regression by random Forest, R news, 2(3), 18-22.
8 Lord D., Washington S. P., Ivan J. N. (2005), Poisson, Poisson-gamma and Zero-inflated Regression Models of Motor Vehicle Crashes: Balancing Statistical Fit and Theory, Accid. Anal. Prev., 37(1), 35-46.   DOI   ScienceOn
9 Park H. S., Son B. S., Kim H. J. (2007), Development of Accident Prediction Models for Freeway Interchange Ramps, J. Korean Soc. Transp., 25(3), Korean Society of Transportation, 123-135.   과학기술학회마을
10 Silva S. (2007), GPLAB: A Genetic Programming Toolbox for MATLAB, Mathworks (Natick, MA, USA), 10.
11 Zhong L., Sun X., Yulong H., Zhong X., Chen Y. (2009), Safety Performance Function for Freeway in China, 88th Annual Meeting of the TRB, Washington D.C.
12 Abdel-Aty M. A., Radwan A. E. (2000), Modeling Traffic Accident Occurrence and Involvement, Accid. Anal. Prev., 32(5), 633-642.   DOI   ScienceOn
13 Breiman L. (2001), Random Forests, Mach. Learn., 45(1), 5-32.   DOI   ScienceOn
14 Han S., Kim K., Oh S. (2008), What Goes Problematic in the Existing Accident Prediction Models and How to Make It Better. J. Korean Soc. Road Eng., 10(1), 19-29.   과학기술학회마을
15 Hauer E. (2004), Statistical Road Safety Modeling, TRR, 1987, TRB, 81-87.