Autonomous vehicles (AVs) or self-driving cars have the potential to provide many benefits
such as improving mobility and reducing the energy and emissions consumed, travel time …

Reducing the severity of injuries resulting from motor-vehicle crashes has long been a
primary emphasis of highway agencies and motor-vehicle manufacturers. While progress …

The analysis of highway-crash data has long been used as a basis for influencing highway
and vehicle designs, as well as directing and implementing a wide variety of regulatory …

Crash injury severity prediction is a promising research target in traffic safety. Traditionally,
various statistical methods were used for modeling crash injury severities. In recent years …

Pedestrians can be categorized as the most vulnerable road users since they have less
protection compared to other road users, which makes their safety of utmost importance for …

Predicting traffic accident severity is essential for traffic accident prevention and vulnerable
road user safety. Furthermore, the explainability of the prediction is crucial for practitioners to …

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 …

This study explores the differences in pedestrian injury severity in three distinct economic
time periods from the recent global recession (the Great Recession): pre-recession …

This study identifies and compares the significant factors affecting pedestrian crash injury
severity at signalized and unsignalized intersections. The factors explored include geometric …

This paper proposes an econometric structure for injury severity analysis at the level of
individual accidents that recognizes the ordinal nature of the categories in which injury …