Browse > Article
http://dx.doi.org/10.7470/jkst.2014.32.3.266

A Comparative Study On Accident Prediction Model Using Nonlinear Regression And Artificial Neural Network, Structural Equation for Rural 4-Legged Intersection  

Oh, Ju Taek (Department of Urban Engineering, Korea National University of Transportation)
Yun, Ilsoo (Department of Transportation Systems Engineering, Ajou University)
Hwang, Jeong Won (Department of Urban Engineering, Korea National University of Transportation)
Han, Eum (Department of Transportation Systems Engineering, Ajou University)
Publication Information
Journal of Korean Society of Transportation / v.32, no.3, 2014 , pp. 266-279 More about this Journal
Abstract
For the evaluation of roadway safety, diverse methods, including before-after studies, simple comparison using historic traffic accident data, methods based on experts' opinion or literature, have been applied. Especially, many research efforts have developed traffic accident prediction models in order to identify critical elements causing accidents and evaluate the level of safety. A traffic accident prediction model must secure predictability and transferability. By acquiring the predictability, the model can increase the accuracy in predicting the frequency of accidents qualitatively and quantitatively. By guaranteeing the transferability, the model can be used for other locations with acceptable accuracy. To this end, traffic accident prediction models using non-linear regression, artificial neural network, and structural equation were developed in this study. The predictability and transferability of three models were compared using a model development data set collected from 90 signalized intersections and a model validation data set from other 33 signalized intersections based on mean absolute deviation and mean squared prediction error. As a result of the comparison using the model development data set, the artificial neural network showed the highest predictability. However, the non-linear regression model was found out to be most appropriate in the comparison using the model validation data set. Conclusively, the artificial neural network has a strong ability in representing the relationship between the frequency of traffic accidents and traffic and road design elements. However, the predictability of the artificial neural network significantly decreased when the artificial neural network was applied to a new data which was not used in the model developing.
Keywords
artificial neural network; nonlinear regression; intersections; structural equation; traffic accident;
Citations & Related Records
Times Cited By KSCI : 11  (Citation Analysis)
연도 인용수 순위
1 Lee J. Y., Chung J. H., Son B. S. (2008), Analysis of Traffic Accident Severity for Korean Highway Using Structural Equations Model, J. Korean Soc. Transp., 26(2), Korean Society of Transportation, 17-24.   과학기술학회마을
2 McCoy P. T., Malone M. S. (1989), Safety Effects of Left-Turn Lanes on Urban Four-Lane Roadways, Transportation Research Board, TRB, 1239, 17-22.
3 Neter J., Kutner M., Wasserman W., Nachtsheim C. (1996), Applied Linear Statistical Models-four edition, A Division of the McGraw-Hill Companies, USA.
4 Oh C., Kang Y. S., Kim B. I. (2006), Development of Pedestrian Fatality Model using Bayesian-Based Neural Network, J. Korean Soc. Transp., 24(2), Korean Society of Transportation, 139-145.   과학기술학회마을
5 Park J. T., Kim J. W., Lee S. B., Lee D. M. (2008), Development of a Traffic Accident Prediction Model for Urban Signalized Intersections, J. Korean Soc. Transp., 26(4), Korean Society of Transportation, 99-110.   과학기술학회마을
6 Oh I. S., Kim S. S., Shin C. H. (2007), Development of Traffic Accident Forecasting Model for Signalized Intersections -Focusing National Highway in Kyonggi Province-, The 57th Conference of Korean Society of Transportation, Korean Society of Transportation, 315-322.
7 Oh J. T., Kim D. H., Lee D. M. (2012), Development of the Expected Safety Performance Models for Rural Highway Segments, J. Korea Soc. Road Eng., 14(2), 131-143.   과학기술학회마을   DOI   ScienceOn
8 Park B. H., Han S. W., Kim T. Y., Kim W. H. (2008), Traffic Accident Models of Cheongju Four-Legged Signalized Intersections by Accident Type, J. Korean Soc. Transp., 26(5), Korean Society of Transportation, 153-162.   과학기술학회마을
9 Poch M., Mannering F. L. (1996), Nagative Binomial Analysis of Intersection Accident Frequencies, Journal of Transportation Engineering, ASCE, 122(2), 105-113.   DOI   ScienceOn
10 Shim J. H., Cho C. H., Lee S. H. (2007), The Industrial Land Price Appraisal based on Artificial Neural Network, Journal of korean Planner Association, 42(5), Korean Planner Association, 223-232.   과학기술학회마을
11 Vogt A. (1999), Crash Models for Rural Intersection: Four-Lane by Two-Lane Stop Controlled and Two- Lane Signalized, Federal Highway Administration, FHWA-RD-99-128.
12 Harwood D. W., Bauer K. M., Potts I. B., Torbic D. J., Richard K. R., Kohlman Rabbani E. R. et al. (2002), Safety Effectiveness of intersection Left and Right-Turn Lanes, Federal Highway Administration, FHWA-RD-02-089.
13 Ha O. G. (2005), Development of Accident Prediction Models and Accident Injury Severity for Rural Signalized Intersections, Hanyang University graduate school, A master Dissertation.
14 Hong S. H. (2000), The Criteria for Selecting Appropriate Fit Indices in Structural Equation Modeling and Their Rationales, The Korean Journal of Clinical Psychological, 19(1), 161-177.
15 Ha T. J., Kang J. K., Park J. J. (2001), Development and Application of Traffic Accident Forecasting Model for Signalized Intersections (Four-Legged Signalized Intersections in Kwang-Ju), J. Korean Soc. Transp., 19(6), Korean Society of Transportation, 207-218.
16 Hong J. Y., Doh C. W. (2002), Development of a Traffic Accident Prediction Model and Determination of the Risk Level at Signalized Intersection, J. Korean Soc. Transp., 20(7), Korean Society of Transportation, 155-166.   과학기술학회마을
17 Bauer K. M., Harwood D. W. (1997), Statistical Models of At-grade Intersection Accidents, Federal Highway Administration, FHWA-RD-96-125.
18 Choi J. W., Kim S. H., Cho J. H., Kim W. C. (2004), A Study to Predict the Traffic Accident Severity Level Applying Neural Network at the Signalized Intersections, J. Korean Soc. Transp., 22(3), Korean Society of Transportation, 127-135.   과학기술학회마을
19 Jo J. I. (2008), Analysis of factors affecting pedestrian injury severity, Hanyang University graduate school, A master Dissertation.
20 Kang Y. K., Kim J. W., Lee S. I., Lee S. B. (2011), Development of Traffic Accident Frequency Prediction Model in Urban Signalized Intersections with Fuzzy Reasoning and Neural Network Theories, J. Korea Soc. Road Eng., 13(1), 69-77.   과학기술학회마을   DOI   ScienceOn
21 Kim D. H., Lee D. M., Sung N. M. (2010), A Development of Traffic Crash Frequency Prediction Models for Rural 3-Lagged Intersections, Journal of transport Research, 17(1), 37-48.
22 Kim E. C., Lee D. M., Kim D. H. (2008), Development of Traffic Accident Frequency Model for Evaluating Safety at Rural Signalized Intersections, J. Korea Soc. Road Eng., 10(4), 53-63.   과학기술학회마을
23 Lee H. S., Lim J. H. (2011), SPSS 18.0 Manual, Korea.
24 Kim K. S. (2010), AMOS 18.0, Hannarae Academy, Korea.
25 Kim S. R., Bae Y. K., Chung J. H., Kim H. J. (2011), Factor Analysis of Accident Types on Urban Street using Structural Equation Modeling(SEM), J. Korean Soc. Transp., 29(3), Korean Society of Transportation, 93-101.   과학기술학회마을
26 Kim W. C., Lee S. B., Namgung M., Hirofumi I. (2001), Constructing Method of Traffic Accidents Prediction Model for Safety Evaluation at Intersections, KSCE Journal of Civil Engineering, 21(4-D), korean society of civil engineers, 427-435.   과학기술학회마을
27 Lee J. K. (2009), A Study on Prediction of the Early-Age Strength of Concrete using Artificial Neural Network Theory, Joongbu University graduate school, A master Dissertation.
28 Lee J. P. (2001), Application of Artificial Neural Network to Predict Speed on Two-lane Rural Highway, Hanyang University graduate school, A master Dissertation.
29 Lee S. W. (2002), The Study on the Selecting Optimal Artificial Neural Networks Model Prior to Forecasting Stock. Inje University graduate school, A master Dissertation.