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.
Highlights
Crash intensity prediction models are significantly important for transportation planning studies, and they are recurrently implemented in transportation safety issues
The main reason is the complex relationship between cyclist and vehicle accidents and absence of value of Vehicle Travel Kilometer (VKT), Bike Travel Kilometer (BTK) and Pedestrian Travel Kilometer (PKT) in modelling
This study aimed to evaluate and analyze the application of neural network models for predicting the data came from traffic police department of Konya City that includes 14134 crashes between years of 2009 and 2013
Summary
Crash intensity prediction models are significantly important for transportation planning studies, and they are recurrently implemented in transportation safety issues In this point, to assure a safety, traffic authorities need to understand the causes of a particular accident to propose the appropriate solutions. Many crash prediction models were justified such as poisson models, negative binomial models, linear regression models and empirical analysis techniques (Abdel-Aty et al, 2000; Ivan et al, 2000; Noland et al, 2004; Caliendo et al, 2007; Miaou and Lord, 2003; Mitra and Washington, 2007; Ma et al, 2008; Naderan et al, 2010; Siddiqui et al, 2012; Pulugurtha and Duddu, 2012) Conventional methods such as regression analysis may be used for the traffic accident problems (Ozgan, 2008; Türe, 2008). Prediction business as usual has significant role in evaluation of system For this purpose, monthly or annually data set investigation was seen in literatures. Application of the ANN is well common solution for many of engineering problems by Kiarash Ghasemlou, Metin Mutlu Aydin, Mehmet Sinan Yıldırım Prediction of pedal cyclists and pedestrian fatalities from total monthly accidents
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