Encyclopedia
Please note this is a comparison between Version 1 by Jibiao Zhou and Version 3 by Bruce Ren.
As an emerging transport tool, electric bicycles (e-bikes) have been widely used due to their low cost and convenient travel characteristics. Numerous studies have shown that the failure to properly use seat belts and safety helmets is one of the major causes of road traffic accidents.
- electric bicycle
- BOP model
- helmet policy
- cycling behavior
- interventions
1. Introduction
As an emerging transport tool, electric bicycles (e-bikes) have been widely used due to their low cost and convenient travel characteristics [1][2][3][4][5][6][7][1,2,3,4,5,6,7]. 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][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 39.1% year on year, with an average annual growth rate of 8.6%. The number of casualties caused by e-bike accidents has increased, and the China Statistical Yearbook (National Bureau of Statistics of China 2017) [9] also 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 98.59%. Hence, large and medium-sized cities in China, such as Guangzhou, Shenzhen and Wenzhou, have issued policies to ban or restrict the use of e-bikes [10]. In addition, in May 2020, Jiangsu and Zhejiang Provinces issued “regulations on electric bicycles”, which explicitly require riders to wear helmets. Any individual who fails to wear a helmet in accordance with these regulations will be warned or fined ¥20 ($3.03) to ¥50 ($7.57) by the traffic administrative department.
Numerous studies [11][12][13][14][15][16][17][18][11,12,13,14,15,16,17,18] have shown that the failure to properly use seat belts and safety helmets is one of the major causes of road traffic accidents. In 2018, the number of e-bike rider fatalities in Ningbo [19] was 387, accounting for 44.79% of the total number of accident fatalities. Among them, deaths caused by head injuries accounted for 88.89% of all e-bike deaths. The data indicate that e-bike riders account for a high proportion of fatalities in road traffic accidents, and that using e-bikes without a helmet is a major cause of casualties. Therefore, it is necessary to strengthen helmet-wearing management.
Wearing a safety helmet can effectively reduce the risk of head injury, which has also been proven by a large number of previous studies [20][21][22][20,21,22], such as those using laboratory tests, real crash data tests, and case–control study methods. A study by Dorschet al. (1987) [23] demonstrated the efficacy of bicycle helmets in real crashes. It was found that the risk of death from head injury was considerably lower for helmeted bicyclists than unhelmet bicyclists. Subsequently, Thompson et al. (1996) [24] examined the effectiveness of bicycle helmets in preventing and protecting against head injuries. The results showed that bicycle helmets, regardless of types, provide protection to cyclists of all ages involved in crashes, including those involving motor vehicles. Recent studies have shown that wearing a safety helmet can reduce the risk of head injury [25][26][27][25,26,27]. These studies indicated that safety helmets provide obvious protection against head injuries. Consequently, interventions to decrease head injury should focus on increasing the helmet wearing rate.
2. Helmet-Wearing Policy on E-Bike Safety Riding Behavior
2.1. Helmet Types and Helmet Usage Rates
Helmets for motorcycles and electric bicycles are divided into three categories according to their shapes: (a) half helmets, (b) three-quarter or 3/4 helmets, and (c) full-face helmets. Moreover, full-face helmets can be broken into three subcategories: off-road helmets, tour-cross helmets, and flip-up helmets, as shown in Figure 1d–f. Generally, although the convenience and comfort of full-face helmets are not as good as those of 3/4 and half helmets, when an accident occurs, they can provide better chin protection. A recent study by Tabary, M. et al. (2021) [28] found that full-face motorcycle helmets may provide better protection from head and facial injury. The advantages that come with full-face helmets are not provided by other styles of helmets; half helmets have better breathability and are light in weight, which is more suitable in summer but offers less protection.
Figure 1. Types of helmet used by e-bike riders. (a) Half helmet, (b) three-quarters helmet, (c) full-face helmet, (d) off-road helmet, (e) tour-cross helmet, (f) flip-up helmet.
A large number of previous studies have proven that helmet usage can help promote e-bike riders’ responsibility in terms of road traffic safety. An earlier statistical survey [29] from the U.S. Consumer Product Safety Commission (CPSC) found that the helmet usage rate increased from 18% in 1991 to 50% in 1998. A survey report [30] by the National Highway Traffic Safety Administration (NHTSA) of the U.S. Department of Transportation (DOT) investigated cyclist and pedestrian attitudes and behaviors in the U.S. in 2012, and found that at least half of cyclists wear helmets for some trips, while 35% of all cyclists wear helmets on all or most of their trips.
In the U.S., safety helmets have gradually begun to be accepted and used by cyclists and e-bike riders. In 1994, the usage rate of safety helmets was 62.5% and reached 70.8% in 2019 [31], which shows that the safety awareness of e-bike riders is high and that the acceptance of safety helmets is also increasing. Compared with the U.S., helmet usage rates in China are still low, and there is a serious imbalance in the proportion of users. According to a survey by iiMedia Research [32] in 2020, 70.11% of Chinese helmet users are male, and only 29.89% are female. Additionally, more than 30% of cyclists are reluctant to wear helmets due to sweltering weather, and nearly 20% of cyclists are reluctant to wear helmets due to the inconvenience of storing them. These results show that the safety awareness of Chinese e-bike riders is still weak.
2.2. Helmet Effectiveness and Collaborative Networks Analysis
The world report on road traffic injury prevention was prepared in 2004 by a joint survey reported by the world health organization (WHO) [33]. It was found that the correct wearing of motorcycle safety helmets can reduce the risk of death from traffic accidents by 40% and the risk of serious head injuries by 70%. For e-bike riders, wearing a safety helmet can also provide the same protection. However, in the early 1990s, the wearing rate of helmets was generally low, and there was controversy regarding whether they could effectively protect human life [34]. Since then, numerous studies [35][36][37][38][35,36,37,38] have shown that helmets can reduce the severity of head and spinal injuries, hospital stays, costs, and mortalities. Helmets have been shown to not only reduce the risk of head and brain injuries, but also provide substantial forehead and midface protection. A prospective case–control study by Thompson et al. [24][39][24,39] examined the effectiveness of bicycle helmets in preventing and protecting against head injuries. It was found that helmets provide a 66% to 88% reduction in the risk of severe head and brain injuries for bicyclists of all ages. Moreover, peer-reviewed studies [28][40][28,40] have confirmed that helmets have an important protective effect on the heads and faces of riders. It is estimated that 75% of bicycle-related fatalities among children could be prevented by wearing bicycle helmets. Meta-analysis results [27][41][42][27,41,42] have shown that the use of helmets can reduce cyclists’ head injuries by 48%, serious head injuries by 60%, brain injuries by 53%, and facial injuries by 23%. In The researchersthis study, we applied the mapping knowledge domain (MKD) approach to perform a comprehensive and objective review of the helmet effectiveness of e-bikes studies, as shown in Figure 2. The Web of Science (WoS) Core Collection was selected as the data source in the MKD approach. thWe researchers searched the literature from 1975 to 2022 using “e-bike”, “helmet effectiveness”, and “helmet” as keywords. After that, the VOSviewer was adopted as the bibliometric analysis software. VOSviewer was developed to offer a superior visualization of the mutation detection function, especially in co-occurrence network analysis.
Figure 2. Collaborative networks analysis for helmet studies. (a) Collaborative networks between countries, (b) collaborative networks between research organizations, (c) co-authorship network, (d) keyword co-occurrence network.
Figure 2a maps the distribution of the main countries in the helmet effectiveness of e-bikes studies. The existing literature on helmet effectiveness of e-bikes comes from 50 countries. Figure 2a shows the number of studies issued from each country and the number of institutions represented. It can be seen that China and the United States have the largest number of articles, at 782 and 214, respectively, accounting for a total contribution rate of 48.87%. Figure 2b shows the cooperation among scientific research institutions by 2022, in which nodes of various colors show cooperative relationships, and the thickness of the link indicates the number of papers produced by the collaboration. In Figure 2c, each node represents an author, the size of the node represents the number of published articles, and the links between the nodes represent the cooperative relationship between the two authors. Keywords often involve subjects such as research interests, research fields, research objects, research topics, and research methods, which play important roles in revealing the research trends of studies. Keyword co-occurrence analysis (KCA) is a common research method in scientometrics [43]. Hence, it was used to generate the keyword co-occurrence network for helmet studies, as shown in Figure 2d. With reference to the characteristics and status of safety helmet studies, the keyword network can be divided into five clusters, namely, cluster 1 (red cluster), cluster 2 (purple cluster), cluster 3 (green cluster), cluster 4 (yellow cluster), and cluster 5 (blue cluster). The co-occurring keywords mainly include helmet use, children, injuries, risk, and impact, the proportions of which are 3.73%, 2.53%, 1.98%, 1.61% and 1.59%, respectively. As shown in Figure 2d, there is a significant correlation between the keywords in each cluster. (a) The red cluster focuses on riding behavior characteristics of e-bikes; (b) the purple cluster focuses on drivers, vehicle, and fault; (c) the green cluster focuses on trauma, mortality, and injuries; (d) the yellow cluster focuses on risk, helmet-use and physical damage; and (e) the blue cluster focuses on children, attitude, and emergency.
