Article 2022 · 1 citations

Forecasting the Accident Frequency and Risk Factors: A Case Study of Erzurum, Turkey

Sahraei, Mohammad Ali, Codur, Merve Kayaci, Codur, Muhammed Yasin, Tortum, Ahmet

Abstract

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 ANN.s method are predicted to be 11.22% until 2030.

Keywords

accident frequency artificial neural network forecasting generalized linear model risk factors traffic accident NEGATIVE BINOMIAL REGRESSION CRASH-FREQUENCY NEURAL-NETWORK TRAFFIC ACCIDENTS PREDICTION MODEL INJURY SEVERITY IMPROVEMENTS

WoS Categories

Engineering, Multidisciplinary

Sustainable Development Goals

Sustainable Cities and Communities

Citation

Journal: TEHNICKI VJESNIK-TECHNICAL GAZETTE
Year: 2022
Volume: 29
Issue: 1
Pages: 190-199
ISSN: 1330-3651
eISSN: 1848-6339
DOI: 10.17559/TV-20200620164552
WoS UID: WOS:000739663500025

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