Traffic injury severity prediction along with identification of contributory factors using learning vector quantization: a case study of the city of London

Abstract

This study presents learning vector quantization neural network modelling to predict injury severity of driver as well as riders, which applies to the backbone of traffic networks for London's central business district. The potential associations between injury severity classes and crash related factors that contribute to their generation are discovered. Accordingly, the model is addressed as an identification technique for contributory factors and range of interventions for road safety. Unsurprisingly, approaching a T/staggered junction is detected as an accident hotspot. Injuries caused by going ahead on a bend and turning manoeuvres are ranked as the next most important contributory factors. Likewise, the affect of most junction actions were almost triple compared to the other indexes. All other sensitive predictors approximately were held near as equal; injuries involving a stationary or parked vehicle, factors related to junction control, crossing facilities, alcohol involvement, rush hours, and vehicle type. Following this implication, with the purpose of maximising the likelihood of injury accuracy, the model is predicted through the most sensitive predictors.