Abstract
At present, Chinese authorities are launching a campaign to convince riders of electric bicycles (e-bikes) and scooters to wear helmets. To explore the effectiveness of this new helmet policy on e-bike cycling behavior and improve existing e-bike management, this study investigates the related statistical distribution characteristics, such as demographic information, travel information, cycling behavior information and riders’ subjective attitude information. The behavioral data of 1048 e-bike riders related to helmet policy were collected by a questionnaire survey in Ningbo, China. A bivariate ordered probit (BOP) model was employed to account for the unobserved heterogeneity. The marginal effects of contributory factors were calculated to quantify their impacts, and the results show that the BOP model can explain the common unobserved features in the helmet policy and cycling behavior of e-bike riders, and that good safety habits stem from long-term safety education and training. The BOP model results show that whether wearing a helmet, using an e-bike after 19:00, and sunny days are factors that affect the helmet wearing rate. Helmet wearing, evenings during rush hour, and picking up children are some of the factors that affect e-bike accident rates. Furthermore, there is a remarkable negative correlation between the helmet wearing rate and e-bike accident rate. Based on these results, some interventions are discussed to increase the helmet usage of e-bike riders in Ningbo, China.
Highlights
As an emerging transport tool, electric bicycles (e-bikes) have been widely used due to their low cost and convenient travel characteristics [1–7]
Our work makes the following three contributions. (a) We examine the contributing factors affecting crashes involving electric bicycles and helmetwearing rates at a disaggregated level; (b) we integrate a complete set of original covariates, including demographic information, travel information, cycling behavior information and riders’ subjective attitude information, to examine the contributing factors related to crashes involving electric bicycles and helmet usage rates; and (c) we employ a robust statistical approach, i.e., a bivariate ordered probit (BOP) model, to explore the unobserved heterogeneity among observations
Self-report surveys have limitations, especially in terms of some subjective descriptions, surveys provide the opportunity to supplement this analysis with detailed demographic data, which provides us with subjective attitude factors that have previously been ignored, such as “whether one is safe after wearing a helmet”, “cycling proficiency”, “road security”, “times of punishments when a helmet is not worn in cycling behavior”, “punishment degree” and “reasons for not wearing a helmet”, and their impact on helmet usage and crash involvement
Summary
As an emerging transport tool, electric bicycles (e-bikes) have been widely used due to their low cost and convenient travel characteristics [1–7]. By the end of 2018, the number of e-bikes in China had reached 250 million, with 69 e-bikes per 100 urban households, an increase of 47.44% from 2013 [7,8]. Despite the obvious advantages of e-bikes, their rapid growth has raised a number of safety issues. Statistics show that the total number of e-bike accidents in China was 40,400 in 2013 and reached 56,200 in 2017, an increase of. The number of casualties caused by e-bike accidents has increased, and the China Statistical Yearbook Bureau of Statistics of China 2017) [9] shows that the number of e-bike fatalities was 733 in 2011 and reached 1305 in 2016, an increase of 78.02% in a five-year period. The number of e-bike injuries was 8532 in 2011 and reached 16,944 in 2016, an increase of
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
