Presenting Model of Intensity Estimation of Vehicle Accidents Using Accident Data (Case Study: Babol-Ganj Afrooz Road)

Document Type : Original Article

Authors

1 Department of Civil Engineering, Malard Branch, Islamic Azad University, Malard, Tehran, Iran.

2 M.Sc., Grad., Aryan Institute of Science and Technology, Amirkola, Mazandaran, Iran

Abstract

Due to its beautiful nature, province of Mazandaran which is always one of the tourist and tourist provinces, where many people travel to the province every year in different seasons. The province is also one of the country's agricultural hubs, which contribute a lot to the transportation of this industry through road transport, which creates high traffic volume traffic and, as a result, traffic accidents. The statistics provided in recent years show that this province is the seventh province for casualties caused by accidents in the country. Based on this, a lot of research has been carried out on the identification of factors affecting the severity of accidents and there have been many advances in this regard, but studies on the relationship between the severity of accidents and the type of collision have been negligible, so further research in this It seems necessary. In this research, using a model of logistic polynomials from a set of selection models for predicting the intensity of accidents has been used. Also, using a prediction model of two sets of data mining algorithms including CART algorithm as one of decision tree algorithms and ANN-MLP algorithm, an array of artificial neural network algorithms was used and the required results were extracted and with each other have been compared. According to the studies carried out in this study, it has been shown that the best model was the MNL model in terms of the correct prediction and the ability to present the prediction formula for each level. The results obtained in the forecasting section show that the estimated formula is able to predict the severity of accidents at 0 (zero) and 1 levels with sufficient accuracy.

Keywords

References

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