Estimation Model of Two-Lane Rural Roads Safety Index According to Characteristics of the Road and Drivers’ Behavior

Document Type : Research Paper


1 Department of Civil and Environmental Engineering, Tarbiat Modarres University, Tehran, Iran

2 Department of Civil Engineering, Islamic Azad University of South Tehran, Tehran, Iran

3 Department Of Electrical Engineering, Sharif University of Technology, Tehran, Iran


Vehicle crashes are amongst the major causes of mortality and results in losses of lives and properties. A large number of the vehicle crashes occur on rural roads. Accidents become more noteworthy in two-lane roads due to going and coming traffic. Therefore, prediction of crashes and their causes are considerably important to reduce the number and severity of the accidents. The safety index is a suitable quantity for determination of road safety degree. It informs us to study the number of accidents in a specific road and time. In this study, safety index of two-lane rural roads is predicted by Artificial Neural Network (ANN), Radial Basis Function Neural Networks (RBFNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithms using MATLAB software. The number of causes which ends to an accident is related to some parameters. We chose seven new parameters as inputs to the ANN, RBFNN and ANFIS methods that are geometric and statistical values of the roads and one output variable that is the safety index of segments of two-lane rural roads. 5 roads in Ilam Province, Iran, were selected for the case study to train, validate and test the proposed estimation models. Finally, the results show that, it is possible to predict the safety index of two-lane rural roads with a high correlation coefficient and a low mean square error (MSE) in relation to real values. The ANN method has a higher correlation coefficient and lower MSE in comparison to RBFNN and ANFIS methods. The achieved correlation coefficient and MSE for validation of the ANN approach are 0.94 and 0.0086 respectively, and correlation coefficient of 0.845 and MSE of 0.019 for all data.


- Abdel-At, M. A and Pemmanaboina, R. (2006) "Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data", IEEE Transportations on Intelligent Transportation Systems, Vol. 7, No. 2, pp. 167- 174.
- Abd-ol-manafi, Seyed Ibrahim, Ahmadi Nejad, Mahmood and Afandi Zade, Shahriyar. (2007) "Designing a model for predicting the number of accidents in intra-city intersections according to statistical models and neural network" M.S Dissertation, Iran University of Science and Technology, Tehran, Iran (In Farsi language)
- Akgüngor, A. P. and Dogan, E. (2008) "Estimating road accidents of Turkey based on regression analysis and artificial neural network approach", Advances in Transportation Studies, an International Journal, Vol. 4, No.9, pp. 906- 913.
- Ayati, Ismaeel (2002) "Vehicle crashes costs in Iran", Publication of University of Ferdousi Mashhad. (In Farsi language)
- Ayati, Ismaeel (2000) "Comprehensive study on vehicle crashes in Mashhad", Publication of University of Ferdousi, Mashhad. (In Farsi language)
- Bayata, H. F., Hattatoglu, F. and Karsli, N. (2011) "Modeling of monthly traffic accidents with the artificial neural network method", International Journal of the Physical Sciences Vol. 6, No.2, pp. 244-254.
- Chen, S., Cowan, C.F.N. and Grant, P.M. (1991) "Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks", IEEE Transactions on Neural Networks, Vol. 2, No. 2, March, pp. 302–309.
- Dougherty, M. (1995) "A review of neural networks applied to transport", Transportation Research Part C, Vol. 3, No. 4, pp.247–260. 
- Han, J. and Kamber, M. (2006) "Data mining: concepts and techniques", Morgan Kaufmann.
- Haykin, S. (2009) "Neural networks and learning machines", London: Prentice Hall.
- Fetanat, M., Shamshiry, R. and Kazemi, M. H. (2013) "Mid-term prediction of wind turbine power generation using Artificial Neural Networks", 5th Conference on Electric Power Generation (EPGC 2013).
- Fielding, Gordon J., Mary, E. and Brenner, Katherine Faust (1985) "Typology for bus transit",Transportation Research, Part A, Vol 40, No. 4, pp. 1257-1266.
- Hagan, M. and Menhaj, M. (1994) "Training feed-forward networks with the Marquardt algorithm", IEEE Transactions on Neural Networks, Vol.5, No.6, 989–993.
- Haleem, K. and Abdel-Aty, M. (2010) "Examining traffic crash injury severity at unsignalized intersections”, Journal of Safety Research, Vol. 41, No. 4, pp. 347-357.
- Iran Road Maintenance and Transportation Organization [RMTO] (2008) “Annual Report”, Tehran, RMTO
- Jang, J. (1993) "ANFIS: adaptive-network-based fuzzy interference system", IEEE Transaction on Systems, Man and Cybernetics, Vol. 23(3), pp. 665–685.
- Kashani, T.A. and Mohaymany, S.A. (2011) "Analysis of the traffic injury severity on twolane two-way rural roads based on classification tree models", Safety Science, Vol. 49, pp. 1314– 1320.
- Kaveh, Ali and Servati, Homayoon (2001) "Artificial neural networks in analyzing and designing the structures", Publication of BHRC.
- Knuiman, M.W., Council, F.M. and Reinfurt, D.W. (1993) "Association of median width and highway accident rates", Transport Reseasch, Rec.,1401.
- Mahmoudabadi, A. (2010) "Comparison of weighted and simple linear regression and artificial neural network models in freeway accidents prediction (Case study: Qom & Qazvin Freeways in Iran)", Second International Conference on Computer and Network Technology, Thailand, Bangkok, 23-25, Part 7: Traffic and Logistic Management, pp. 392-396.
- Mahmood Abadi, Abbas and Safi Samg Abadi, Azam Dokhy (2008) " Estimation of daily road accidents using neural network relying on traffic status", Second Conference on phased and Smart  Systems, Tehran : Technical University of Malek Ashtar.
- Mussone, L., Ferrari, A. and Oneta, M. (1999) "An analysis of urban collisions using on artificial intelligence model, accident analysis and prevention", Vol. 31, pp 705-718.
- Takagi, T. and Sugeno, T. (1985) "Fuzzy identification of system and its applications to modeling and control". IEEE Transaction on Systems, Man and Cybernetics, Vol. 15, pp. 116– 132.
- Vogt, A. and Bared, J. (1998) "Accident models for two-lane rural segments and intersections", In Transportation Research Record 1635, TRB, National Research Council, Washington, D.C. pp. 18-22.