Submit Manuscript

Journal of Applied Sciences

Year: 2008 | Volume: 8 | Issue: 1 | Page No.: 146-151
Research Article

Neural Networks Based Modelling of Traffic Accidents in Interurban Rural Highways, Duzce Sampling

Ercan Ozgan
Department of Construction, Faculty of Technical Education, DÜzce Üniversity, Konuralp, DÜzce, Turkey
Recep Demirci
2Department of Electric, Faculty of Technical Education, DÜzce Üniversity, Konuralp, DÜzce, Turkey
In this study, alternatively, Artificial Neural Network (ANN) based modelling of traffic accidents on two line interurban rural highways in terms of number of accidents; injuries and dead have been presented. This study was conducted for D100/11 state highway section in Duzce. In this section of the highway, totally 783 traffic accidents occurred and 1396 vehicles involved in these accidents between 2002 and 2006 years. Using traffic accident reports data, ANN was applied for modelling of traffic accidents with respect to distance and months. As a result, it was observed that there was a perfect fit between the simulation results and actual data of accidents and the created neural network model of accidents resembles the actual data. Therefore, the developed model could be an alternative method for predictions of traffic accidents on interurban rural highways.
PDF Fulltext XML References Citation

Leave a Comment

Your email address will not be published. Required fields are marked *

Article Trend

Total views 3645

References

  1. Abdelwahab, H.T. and M.A. Abdel-Aty, 2001. Development of artificial neural network models to predict driver injury severity in traffic accidents at signalizes intersection. Transport. Res. Rec., 1746: 6-13.
  2. Abdelwahab, H.T. and M.A. Abdel-Aty, 2002. Artificial neural networks and logit models for traffic safety analysis of toll plazas. Transport. Res. Rec., 1784: 115-125.
    Direct Link
  3. Delen, D., R. Sharda and M. Bessonov, 2006. Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accid. Anal. Prev., 38: 434-444.
    Direct Link
  4. Qiao, F., H. Yang and W.H.K. Lam, 2001. Intelligent simulation and prediction of traffic flow dispersion. Transport. Res. Part B: Methodological., 35: 843-863.
    Direct Link
  5. Hauer, E., 1996. Identification of sites with promise. Transport. Res. Rec., 1542: 54-60.
  6. Hauer, E., B.K. Allery, J. Kononov and M.S. Griffith, 2004. How to best rank site with promise. Transport. Res. Rec., 1897: 48-54.
    Direct Link
  7. Himanen, V., 1998. Neural Network in Transport Application. Ashgate Pub. Co., USA., pp: 311-340.
  8. Yin, H., S. Wong, J. Xu and C. Wong, 2002. Urban traffic flow prediction using a fuzzy-neural approach. Transport. Res. Part C: Emerg. Technologies, 10: 85-98.
    Direct Link
  9. Kalyoncuoglu, S.F. and M. Tigdemir, 2004. An alternative approach for modelling and simulation of traffic data: Artificial neural networks. Simulat. Model. Practice Theory, 12: 351-362.
    Direct Link
  10. Lord, D., 2006. Modeling motor vehicle crashes using Poisson gamma models: Examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter. Accid. Anal. Prev., 38: 751-766.
    Direct Link
  11. Maren, C.H. and R. Pap, 1990. Handbook of Neural Computing Applications. Academic Press, London.
  12. McFadden, J., W.T. Yang and S.R. Durrans, 2001. Application of artificial neural networks to predict speeds on two-lane rural highways. Transport. Res. Rec., 1751: 9-17.
    Direct Link
  13. Miaou, S.P. and D. Lord, 2003. Modeling traffic crash-flow relationships for intersections: Dispersion parameter, functional form and Bayes versus empirical Bayes. Transport. Res. Rec., 1840: 31-40.
    Direct Link
  14. Miaou, S.P. and J.J. Song, 2005. Bayesian ranking of sites for engineering safety improvements: Decision parameter, treatability concept, statistical criterion and spatial dependence. Accid. Anal. Prev., 37: 699-720.
    Direct Link
  15. Mussone, L., A. Ferrari and M. Oneta, 1999. An analysis of urban collisions using an artificial intelligence model. Accid. Anal. Prev., 31: 705-718.
    Direct Link
  16. Narendra, K.S. and K. Parthasarathy, 1990. Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Networks, 1: 4-27.
    CrossRefPubMed
  17. Ozgan, E., 2003. Multi-variate clinical aspects of traffic accidents at Sivas environmental state highway. Ph.D. Thesis. Gazi University, Turkey.
  18. Qin, X., J.N. Ivan, N. Ravishanker and J. Liu, 2005. Hierarchical bayesian estimation of safety performance functions for two-lane highways using Markov chain Monte Carlo modeling. J. Transport. Eng., 131: 345-351.
    Direct Link
  19. Riviere, C., P. Lauret, J.F.M. Ramsamy and Y. Page, 2006. A Bayesian neural network approach to estimating the energy equivalent speed. Accid. Anal. Prev., 38: 248-259.
    Direct Link
  20. Rumelhart, D.E. and J.L. McClelland, 1986. Parallel Distributed Processing. Vol. 1, The MIT Press, Cambridge.
  21. Schluter, P.J., J.J. Deely and A.J. Nicholson, 1997. Ranking and selecting motor vehicle accident sites by using a hierarchical Bayesian model. Statistician, 46: 293-316.
    Direct Link
  22. Tunaru, R., 2002. Hierarchical Bayesian models for multiple count data. Aust. J. Stat., 31: 221-229.
    Direct Link
  23. Wael, H.A. and N.J. Bruce, 2007. Prediction models for truck accidents at freeway ramps in Washington State using regression and artificial intelligence. Transport. Res. Rec., 1635: 30-36.
  24. Yildirim, S., M.F. Sukkar, R. Demirci and V. Aslantas, 1996. Design of adaptive NNs-Robust-PID controller for a robot control. Proceedings of the International Symposium on Intelligent Control (ISIC). September 15-18, 1996, Dearborn, Michigan, USA., pp: 508-513.
    CrossRefDirect Link
  25. Yuanchang, X. D. Lord and Y. Zhang, 2007. Predicting motor vehicle collisions using Bayesian neural network models: An empirical analysis. Accid. Anal. Prev., 39: 922-933.
    Direct Link

Keywords