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.

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Prediction models have been extensively used in the field of road safety, however, none of these models have been particularly applied to zero-emission electric vehicle (EV) related injuries so far; which may lead to different outcomes due to their inaudible engines. Using an optimizable classification tree, this first-ever study aims to predict the likelihood of personal injury severities stemming from EV-related crashes on Britain's roads. The prediction model was found to be capable of detecting significant and insignificant factors. The factors provide important insights into how the severity of injuries can be reduced in the future deployment of EVs. Although there was an increased risk for injuries classified as ‘slight severity’, particularly at lower urban speed limits, several predictors are suggesting that EVs do not pose more of a risk to a certain group. Contrary to popular belief, no convincing evidence has been found to suggest that eco-friendly EVs are ‘silent killers’ for vulnerable road users.