Abstract
ABSTRACTThe Highway Safety Improvement Program (HSIP) requires state highway agencies to improve their roadway network safety through a ‘strategic’ and ‘data-driven’ approach. As part of HSIP, the Federal Highway Administration mandates that states develop a Pavement Friction Management System to reduce the rate of fatal and injury-causing crashes and prioritise their safety improvement projects based on the crash risk. This paper aims to predict the rate of wet and dry vehicle crashes based on surface friction, traffic level, and speed limit using an artificial neural network (ANN). Three learning algorithms, Levenberg–Marquardt, conjugate gradient, and resilient back-propagation, were examined to train the network. Levenberg–Marquardt produced the best precision and was used to develop the model. The results of the study suggest that the ANN model can reliably predict the rate of crashes. The prediction model can be used as a scale to prioritise safety improvement projects based on the rate of fatal and injury-causing crashes.
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