A machine learning approach for building an adaptive, real-time decision support system for emergency response to road traffic injuries.

In this paper, historical data about road traffic accidents are utilized to build a decision support system for emergency response to road traffic injuries in real-time. A cost-sensitive artificial neural network with a novel heuristic cost matrix has been used to build a classifier capable of predicting the injury severity of occupants involved in crashes. The proposed system was designed to be used by the medical services dispatchers to better assess the severity of road traffic injuries, and therefore to better decide the most appropriate emergency response.

Investigating the role of socio-economic factors in comprehension of traffic signs using decision tree algorithm.

Introduction: Drivers' ability to comprehend the meaning of traffic signs is essential to safe driving. Drivers' personal characteristics are believed to play a crucial role in determining drivers' comprehension of traffic signs.

Method: This study investigates the role of age, gender, marital status, license category, educational level, driving experience, monthly income, and number of traffic violation during the last five years in drivers' comprehension of 39 posted traffic signs in the city of Irbid, Jordan.

Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e.