Prediction of traffic accidents based on weather conditions in Gilan province using artificial neural network

Autor: Shandiz Moslehi, Arsalan Gholami, Zahra Haghdoust, Hosein Abed, Saman Mohammadpour, Mohammad Ashkan Moslehi
Jazyk: perština
Rok vydání: 2021
Předmět:
Zdroj: مدیریت سلامت, Vol 24, Iss 3, Pp 67-78 (2021)
ISSN: 2008-1219
2008-1200
Popis: Introduction: Road traffic accidents are one of the leading causes of death worldwide, including Iran. There are several factors involved in the occurrence of them; using different models, these factors can be identified and the occurrence of road traffic accidents can be predicted. The purpose of this study was to predict road traffic accidents based on weather conditions using artificial neural network model. Methods: In the present study, traffic data during the years 2014 to 2017, were examined using a multilayer perceptron network. Network input variables included minimum temperature, average temperature, average rainfall, maximum wind speed, glaciation, air pressure, fog concentration and output variable was the number of accidents. Results: The designed network with seven neurons in the input layer, four neurons in the middle layer, and one neuron in the output layer with Lunberg-Marquardt optimization function and sigmoid tangent transfer function in the middle layer and linear transmission function in the output layer was selected as the optimal network. The results showed that the designed network with the correlation coefficient of 0.90 and mean square error of 0.01 has a high ability to predict road traffic accidents. Conclusion: The results showed that the artificial neural network has a good performance for predicting road traffic accidents. Given the importance of predicting road traffic accidents and its role in promoting the health of people in such accidents, the results of this study can be used to expand more effective preventive measures for policy makers and researchers.
Databáze: OpenAIRE
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