Tehnički vjesnik, Vol. 29 No. 1, 2022.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20200620164552
Forecasting the Accident Frequency and Risk Factors: A Case Study of Erzurum, Turkey
Mohammad Ali Sahraei*
; 1) Erzurum Technical University, Faculty of Engineering and Architecture, Civil Engineering Department, 25050, Erzurum, Turkey 2) Civil Engineering Department, Faculty of Engineering, Girne American University, Girne, N. Cyprus Via Mersin 10, Turkey
Merve Kayacı Çodur
; Erzurum Technical University Faculty of Engineering and Architecture, Industrial Engineering Department, 25050, Erzurum, Turkey
Muhammed Yasin Çodur
; 1) Erzurum Technical University, Faculty of Engineering and Architecture, Civil Engineering Department, 25050, Erzurum, Turkey 2) American University of Middle East, College of Engineering and Technology, Civil Engineering Department, Kuwait
Ahmet Tortum
; Atatürk University Engineering Faculty, Civil Engineering Department, 25240, Erzurum, Turkey
Sažetak
Nowadays, life is intimately associated with transportation, generating several issues on it. Numerous works are available concerning accident prediction techniques depending on independent road and traffic features, while the mix parameters including time, geometry, traffic flow, and weather conditions are still rarely ever taken into consideration. This study aims to predict future accident frequency and the risk factors of traffic accidents. It utilizes the Generalized Linear Model (GLM) and Artificial Neural Networks (ANN) approaches to process and predict traffic data efficiently based on 21500 records of traffic accidents that occurred in Erzurum in Turkey from 2005 to 2019. The results of the comparative evaluation demonstrated that the ANN model outperformed the GLM model. The study revealed that the most effective variable was the number of horizontal curves. The annual average growth rates of accident occurrences based on the ANNꞌs method are predicted to be 11.22% until 2030.
Ključne riječi
accident frequency; artificial neural network; forecasting; generalized linear model; risk factors; traffic accident
Hrčak ID:
269499
URI
Datum izdavanja:
15.2.2022.
Posjeta: 1.204 *