Estimating traffic accidents play a vital role to apply road safety procedures. This study proposes Differential Evolution Algorithm (DEA) models to estimate the number of accidents in Turkey. In the model development, population (P) and the number of vehicles (N) are selected as model parameters. Three model forms, linear, exponential and semi-quadratic models, are developed using DEA with the data covering from 2000 to 2014. Developed models are statistically compared to select the best fit model. The results of the DE models show that the linear model form is suitable to estimate the number of accidents. The statistics of this form is better than other forms in terms of performance criteria which are the Mean Absolute Percentage Errors (MAPE) and the Root Mean Square Errors (RMSE). To investigate the performance of linear DE model for future estimations, a ten-year period from 2015 to 2024 is considered. The results obtained from future estimations reveal the suitability of DE method for road safety applications.
 World Health Organization. (2011). Global Plan fort the Decade of Action for Road Safety 2011-2020. Accessed 09 December 2015, from http://www.who.int/roadsafety/decade_of_action/plan/plan_english.pdf
 Turkish Statistical Institute. (2015). Traffic Accident Statistics 2000-2014. Accessed 28 November 2015, from http://www.tuik.gov.tr
 Smeed, R. J. (1949). Some Statistics Aspects of Road Safety Research. Journal of the Royal Statistical Society. Series A, Part I, 1-34.
 Andreassen, D. C. (1985). Linking Deaths with Vehicles and Population, Traffic Engineering & Control. 26 (11), 547-549.
 Mekky, A. (1985). Effect of Rapid Increase in Motorization Levels on Road Fatality Rates in Some Rich Developing Countries. Accident Analysis & Prevention. 17 (2), 101-109.
 Valli, P. P. (2005). Road Accident Models for Large Metropolitan Cities of India. IATSS Research. 29 (1), 57-65.
 Zegeer, C. V. & Deacon J. A. (1985). Effect of Lane Width, Shoulder Width, and Shoulder Type on Highway Safety. In: Relationship between Safety and Key Highway Features. State of Art Report 6. Transportation Research Board Washington D.C.
 Chakrobort, S. & Roy, S. K. (2005). Traffic Accident Characteristics of Kolkata. Transport and Communications Bullettin for Asia and the Pacific. 74, 75-86.
 Junus, N. W. M., İsmail, M. T. & Arsad, Z. (2015). Predicting Penang Road Accidents Influences: Time Series Regression Versus Structural Time Series. Indian Journal of Science and Technology. 8 (30).
 Shahri, M.E., Balochian, S., Balochian, H. & Zhang, Y. (2014). Design of Fractional - order PID Controllers for Time Delay Systems Using Differential Evolution Algorithm. Indian Journal of Science and Technology. 7 (9), 1307-1315.
 Zellagui, M., Hassan, H. A. & Abdelaziz, A. Y. (2017). Non-dominated sorting gravitational search algorithm for multi-objective optimization of power transformer design. Engineering Review. 37 (1), 27-37.
 Doğan, E., Akgüngör, A. P. & Arslan, T. (2016). Estimation of delay and vehicle stops at signalized intersections using artificial neural network. Engineering Review. 36 (2), 157-165.
 Mussone, L., Ferrari, A. & Oneta, M. (1999). An Analysis of Urban Collision Using an Artificial Intelligence Model. Accident Analysis and Prevention. 31 (8), 705-718.
 Akgüngör, A. P. & Doğan, E. (2008). Estimating Road Accidents of Turkey Based on Regression Analysis and Artificial Neural Network Approach. Advances in Transportation studies an International Journal Section A 16. 11-22.
 Akgüngör, A. P. & Doğan, E. (2009). An Artificial Intelligent Aprroach to Traffic Accident Estimation: Model Development and Application. Transport. 24 (2), 135-142.
 Akgüngör, A. P. & Doğan, E. (2009). An Application of Modified Smeed, Adapted Andreassen and Artificial Neural Network Accident Models to Three Metropolitan Cities of Turkey. Scientific Research and Essays. 4 (9), 906-913.
 Codur, M. Y. & Tortum, A. (2015). An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey. Promet-Traffic & Transportation. 27 (3), 217-225.
 Storn, R. & Price, K. (1997). Differential Evolution - A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Journal of Global Optimization. 11 (4), 341-359.
 Storn, R. (2008). Differential Evolution Research - Trends and Open Questions Advances in Differential Evolution, Springer-Verlag Berlin Heidelberg. Chakraborty, U.K.
 Karaboğa, D. (2004). Artificial Intelligence Optimization Algorithms (in Turkish). Atlas Yayın Dağıtımı. İstanbul.