Various studies on how to prevent and deal with traffic accidents are ongoing. In the past,
the key research emphasis was on passive accident response measures that analyzed …

This paper investigates the use of two well-known artificial neural network (ANN) paradigms:
the multilayer perceptron (MLP) and fuzzy adaptive resonance theory (ART) neural networks …

Road accident refers to an incident where at least one land vehicle with one or more people
injured or killed. While there are many variables attributed to road accident, ranging from …

Vulnerable road users (VRUs) are exposed to the highest risk in the road traffic environment.
Analyzing contributing factors that affect injury severity facilitates injury severity prediction …

With more cars on the road and an increasing travel rate, one of the main issues in
transportation engineering is how to make roads safe. The evaluation of the level of road …

Crash severity prediction has been raised as a key problem in traffic accident studies. Thus,
to progress in this area, in this study, a thorough artificial neural network combined with an …

The aimed of this research is to evaluate and compare different approaches to modeling
crash severity as well as investigating the effect of risk factors on the fatality outcomes of …

Until now, considerable efforts have been made to determine which modelling technique
performs the best for predicting accident frequency based on crash data. In this regard, the …

Real-time crash risk evaluation models will likely play a key role in Active Traffic
Management (ATM). Models have been developed to predict crash occurrence in order to …

Rapid increase of traffic volume on urban roads over time has changed the traffic scenario
globally. It has also increased the ratio of road accidents that can be severe and fatal in the …