Abstract
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.
| Original language | English |
|---|---|
| Number of pages | 28 |
| Journal | Proceedings of the ICE - Transport |
| Early online date | 4 Jan 2022 |
| DOIs | |
| Publication status | E-pub ahead of print - 4 Jan 2022 |
Keywords
- Electric vehicles
- Injury severity prediction
- optimizable classification tree
- STATS19
- Vulnerable road users
- Zero-emissions vehicles
ASJC Scopus subject areas
- Civil and Structural Engineering
- Transportation