Road crash fatality is a universal problem of the transportation system. A massive death toll
caused annually due to road crash incidents, and among them, vulnerable road users (VRU) …

This paper employs artificial neural network (ANN) to develop an accident appraisal expert
system. Two ANN models–party-based and case-based–with different hidden neurons are …

Road traffic injuries cost countries 3% of their annual GDP. In developing countries like
India, every year around 150,000 people die on roads. The type of vehicles involved in a …

Crash severity is undoubtedly a fundamental aspect of a crash event. Although machine
learning algorithms for predicting crash severity have recently gained interest by the …

Objective The aim of this study was to explore a suitable method for analyzing road transport
accidents that involve hazardous materials and to explore the main factors that influence the …

This work proposes to use deep neural networks (DNN) model for prediction of road
accidents. DNN consists of two or more hidden layers with large number of nodes. Accident …

Road accidents is a global issue that cause deaths and injuries besides other direct and
indirect losses. Countries and international organisations have designed technologies …

Ahead of an unavoidable collision, the actual crash constellation and along with it, the crash
severity can significantly change based on the driver actions. To justify the use of safety …

Traffic accidents pose a significant public safety concern, leading to numerous injuries and
fatalities worldwide. Predicting the severity of these accidents is crucial for developing …

As a key area of traffic safety research, crash severity modelling has attracted tremendous
attention. Recently, there has been growing interest in applying machine learning (ML) …