Injury severity analysis of electric bike crashes in Changsha, Hunan Province: taking different lighting conditions into consideration

With the increasing use of electric bikes, electric bike crashes occur frequently. Analysing the influencing factors of electric bike crashes is an effective way to reduce mortality and improve road safety. In this paper, spatial analysis is performed by geographic information system (GIS) to present the hot spots of electric bike crashes during daylight and darkness in Changsha, Hunan Province, China. Based on the Ordered Probit (OP) model, we studied the risk factors that led to different severities of electric bike crashes. The results show that the main influencing variables include age, illegal behaviour, collision type and road factors. During daylight and darkness, elderly electric bike riders over the age of 65 years have a higher probability of fatal crashes. Not following traffic signals and reverse driving are significantly related to the severity of riders' injuries. In darkness, frontal collisions are significant factors causing rider injury. In daylight, more serious crashes will be caused in bend and slope road segments. In darkness, roads with no physically separated bicycle lanes increases the risk of riders being injured. Electric bike crashes are mainly concentrated in the commercial, public service and residential areas in the main urban area. In suburbs at darkness, electric bike riders are more likely to be seriously injured. Adding protection measures, such as improved lighting, non-motorized lane facilities and interventions targeting illegal behaviour in the hot spot areas can effectively reduce the number of electric bike crashes in complex traffic.

Automated summary

Spatial analysis using GIS was conducted to identify hot spots of electric bike crashes in Changsha, China. Risk factors leading to different severities of crashes were studied using the Ordered Probit model. The study found that age, illegal behavior, collision type, and road factors were the main influencing variables.