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

Sahraei M. A., Codur M. K., Codur M. Y., TORTUM A.

TEHNICKI VJESNIK-TECHNICAL GAZETTE, cilt.29, sa.1, ss.190-199, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29 Sayı: 1
  • Basım Tarihi: 2022
  • Doi Numarası: 10.17559/tv-20200620164552
  • Dergi Adı: TEHNICKI VJESNIK-TECHNICAL GAZETTE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.190-199
  • Anahtar Kelimeler: 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
  • Atatürk Üniversitesi Adresli: Evet

Özet

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.