Environmentally-friendly electric vehicles: a silent menace to vulnerable road users?

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.

Investigating The Temporal Trends And Risks Imposed By Different Driver Groups On Other Drivers

Multi-vehicle traffic collisions usually result in increased injury severities to the more vulnerable drivers involved in those accidents. This research study aims at investigating the temporal trends and risks imposed by different driver groups on other drivers using logistic regression. The study is based on analysing accident data for all light-duty two-vehicle collisions in North Carolina from January 1, 2004 to December 31, 2013. Two logistic regression models are developed for each year. The first model, evaluates the probability that a certain driver sustains at least a visible injury caused by the other driver and the second model, evaluates the probability that a driver will cause at least a visible injury to the other driver. The findings of this research may help decision makers identify driver groups that are more dangerous to other drivers so that necessary precautionary measures can be adopted to make our roads a safer place.

Investigating The Temporal Trends And Risks Imposed By Different Driver Groups On Other Drivers

Multi-vehicle traffic collisions usually result in increased injury severities to the more vulnerable drivers involved in those accidents. This research study aims at investigating the temporal trends and risks imposed by different driver groups on other drivers using logistic regression. The study is based on analysing accident data for all light-duty two-vehicle collisions in North Carolina from January 1, 2004 to December 31, 2013. Two logistic regression models are developed for each year. The first model, evaluates the probability that a certain driver sustains at least a visible injury caused by the other driver and the second model, evaluates the probability that a driver will cause at least a visible injury to the other driver. The findings of this research may help decision makers identify driver groups that are more dangerous to other drivers so that necessary precautionary measures can be adopted to make our roads a safer place.