Study of the Severity of Accidents in Tehran Using Statistical Modeling and Data Mining Techniques

Backgrounds and Aims: The Tehran province was subject to the second highest incidence of fatalities due to traffic accidents in 1390. Most studies in this field examine rural traffic accidents, but this study is based on the use of logit models and artificial neural networks to evaluate the factors that affect the severity of accidents within the city of Tehran.Materials and Methods: Among the various types of crashes, head-on collisions are specified as the most serious type, which is investigated in this study with the use of Tehran’s accident data. In the modeling process, the severity of the accident is the dependent variable and defined as a binary covariate, which are non-injury accidents and injury accidents. The independent variables are parameters such as the characteristics of the driver, time of the accident, traffic and environmental characteristics. In addition to the prediction accuracy comparison of the two models, the elasticity of the logit model is compared with a sensitivity analysis of the neural network.Results: The results show that the proposed model provides a good estimate of an accident's severity. The explanatory variables that have been determined to be significant in the final models are the driver’s gender, age and education, along with negligence of the traffic rules, inappropriate acceleration, deviation to the left, type of vehicle, pavement conditions, time of the crash and street width.Conclusion: An artificial neural network model can be useful as a statistical model in the analysis of factors that affect the severity of accidents. According to the results, human errors and illiteracy of drivers increase the severity of crashes, and therefore, educating drivers is the main strategy that will reduce accident severity in Iran. 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