Prediction of pedal cyclists and pedestrian fatalities from total monthly accidents and registered private car numbers

Kiarash Ghasemlou; Metin Mutlu Aydin; Mehmet Sinan Yıldırım

doi:10.5604/08669546.1169209

Authors

Kiarash Ghasemlou İstanbul Technical University, Department of Civil Engineering, İstanbul, Turkey Author
Metin Mutlu Aydin Gümüşhane University, Department of Civil Engineering, Gümüşhane, Turkey Author
Mehmet Sinan Yıldırım Celal Bayar University, Department of Civil Engineering, Manisa, Turkey Author

DOI:

https://doi.org/10.5604/08669546.1169209

Keywords:

traffic accidents, cyclist, pedestrians, artificial linear network, regression trees, multiple linear

Abstract

Accident prevention is relatively a complex issue considering the effectiveness of the injury prevention technologies as well as more detailed assessment of the complex interactions between the road condition, vehicle and human factor. For many years, highway agencies and vehicle manufacturers showed great efforts to reduce the injuries resulting from the vehicle crashes. Many researchers used a broad range of methods to evaluate the impact of several factors on traffic accidents and injuries. Recent developments lead up to capable for determining the effects of these factors. According to World Health Organization (WHO), cyclists and pedestrians comprise respectively 1.6% and 16.3% in traffic crash fatalities in 2013. Also in Turkey crash fatalities for pedestrian and cyclists are respectively 20.6% and 3% according to Turkish Statistical Instıtute data in 2013. The relationship between cycling and pedestrian rates and injury rates over time is also unknown. This paper aims to predict the crash severity with the traffic injury data of the Konya City in Turkey by implementing the Artificial Neural Networks (ANN), Regression Trees (RT) and Multiple Linear Regression modelling (MLRM) method.

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