The purpose of this article is to present a structured review of publications utilizing machine
learning methods to aid in engineering risk assessment. A keyword search is performed to …

Accurately modelling crashes, and predicting crash occurrence and associated severities
are a prerequisite for devising countermeasures and develo** effective road safety …

The analysis of highway accident data is largely dominated by traditional statistical methods
(standard regression-based approaches), advanced statistical methods (such as models …

In this paper, three algorithms are applied to obtain the parameters of Radial Basis Function
(RBF) kernels of Support Vector Machines (SVM), which include: PSO (Particle Swarm …

Virtually every statistical analysis of highway safety data is predicated on the assumption
that the estimated model parameters are temporally stable. That is, the assumption that the …

Safety is a major concern of transportation planners and engineers in their design of
highway rail grade crossings (HRGCs). Safety agencies rely on prediction models to …

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 …

Road safety modeling is a valuable strategy for promoting safe mobility, enabling the
development of crash prediction models (CPM) and the investigation of factors contributing …

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 …