E-bike Use Research Articles

Journey Attributes, E-Bike Use, and Perception of Driving Behavior of Motorists as Predictors of Bicycle Crash Involvement and Severity

Previous studies have revealed the relevance of e-bike use, perception of driving behavior of motorists, and instrumental and affective factors in work and leisure journeys among regular cyclists. However, the importance of these factors as predictors of bicycle crash involvement and severity is less well-known. The aim of the present study was to investigate the role of journey attributes, e-bike use, and perception of driving behavior of motorists in predicting bicycle crash involvement and severity, while controlling for sociodemographic factors, cycling levels, cycling environment, and purposes of cycling. We collected data from an online panel of 2,389 respondents from six European countries (Sweden, Netherlands, United Kingdom, Hungary, Italy, Spain). Using the generalized linear model, we found that both bicycle crash involvement and severity were related to lower age, being employed, using the bicycle for traveling to or from college/university, not using the bicycle for leisure/training, and using an e-bike. Bicycle crash severity was associated with lower affective attributes, higher instrumental attributes, and the perception of good driving behavior of motorists.

Space-time analytics of human physiology for urban planning

Recent advancements in mobile sensing and wearable technologies create new opportunities to improve our understanding of how people experience their environment. This understanding can inform urban design decisions. Currently, an important urban design issue is the adaptation of infrastructure to increasing cycle and e-bike use. Using data collected from 12 cyclists on a cycle highway between two municipalities in The Netherlands, we coupled location and wearable emotion data at a high spatiotemporal resolution to model and examine relationships between cyclists' emotional arousal (operationalized as skin conductance responses) and visual stimuli from the environment (operationalized as extent of visible land cover type). We specifically took a within-participants multilevel modeling approach to determine relationships between different types of viewable land cover area and emotional arousal, while controlling for speed, direction, distance to roads, and directional change. Surprisingly, our model suggests ride segments with views of larger natural, recreational, agricultural, and forested areas were more emotionally arousing for participants. Conversely, segments with views of larger developed areas were less arousing. The presented methodological framework, spatial-emotional analyses, and findings from multilevel modeling provide new opportunities for spatial, data-driven approaches to portable sensing and urban planning research. Furthermore, our findings have implications for design of infrastructure to optimize cycling experiences.

Safety of e-bikes compared to conventional bicycles: What role does cyclists’ health condition play?

IntroductionThe rising use of e-bikes (EBs) presents an opportunity to increase active transportation but may compromise road safety due to increased travel speed and weight compared to conventional bicycle (CBs). Evidence comparing the safety of EBs and CBs is limited while the influence of cyclists’ health status has been neglected. MethodsThis cross-sectional case-control study compared EB and CB users in the Netherlands. Data were gathered 1) through a survey among crash casualties treated at emergency departments aged >16 (N = 2383) and 2) control group data were collected among cyclists without any known crash experience, randomly drawn from a panel of the Dutch population (N = 1860). Using logistic regressions, we assessed the likelihood of crashes and crash severity while adjusting for bicycle use and health status. ResultsEB users had poorer health than CB users, but they were not more likely to be involved in a crash or to sustain more severe injuries. However, older female cyclists did have an elevated risk on EBs and sustained more severe injuries. Health-related factors such as the presence of morbid conditions, medication use, and the body mass index were neither associated with crash likelihood nor associated with injury severity and accordingly did not explain the findings for older females. However, balance and coordination problems, and the use of anti-epileptic drugs were associated with crashes. ConclusionOur findings provide support that EB users have a poorer health status than CB users, while general health status is unrelated to the likelihood and severity of bicycle crashes. EBs enable more vulnerable groups to cycle or keep cycling but, after controlling for bicycle use, EB users are not more likely to be involved in a crash or to sustain severe injuries. As older females run a higher risk on an EB and are more likely to fall while (dis)mounting, we recommend to promote EBs enabling safer (dis)mounting such as by reduced saddle height.

The role and importance of e-bikes in CITY logistics, with special emphasis on sustainable transport

Urban environments depend on the efficiency of logistics activities, which due to several negative impacts are not desirable in cities. Researchers are paying increasing attention to the development of environmentally friendly logistics systems, by applying electric vehicles, hybrid vehicles, cargo trams, and other vehicles to alternative propulsion systems. To achieve environmentally-friendly logistics systems, researchers have defined several "scenarios", to achieve sustainable transport, with the solutions presented in this paper. Also, the paper analyzes the role and importance of the use of e-bikes in CITY logistics, regarding sustainable transport.

The impact of e-cycling on travel behaviour: A scoping review

IntroductionElectrically assisted bicycles (e-bikes) have become increasingly popular in the past decade. This review aimed to scope the literature to identify what is known about the frequency and duration of e-bike use, their impact on travel behaviour, the purposes for which e-bikes are used and factors associated with e-bike use. In addition, the review aimed to identify gaps in the literature and highlight future research priorities. MethodsA scoping review of published and unpublished literature in any language. Relevant articles were identified through searching six databases, two grey literature platforms and reference lists. Searches were conducted until August 2019. Data were extracted using a standardised extraction form and descriptive and narrative results are provided. ResultsSeventy-six studies met the inclusion criteria. The volume of research has increased since 2017 and primarily examines personal e-bike use, as opposed to e-bike share/rental schemes or organizational e-bike initiatives. The use of e-bikes increased the frequency and duration of cycling compared to conventional cycling and may help overcome barriers associated with conventional cycling. The uptake in e-cycling largely substitutes for conventional cycling or private car journeys, though the degree of substitution depends on the primary transport mode prior to e-bike acquisition. E-bikes are primarily used for utilitarian reasons, though older adults also engage in recreational e-cycling. Research priorities include quantitatively examining e-bike use, their impact on overall transport behaviour and identifying determinants of e-cycling to inform intervention and policy. ConclusionsThis review suggests that the personal use of e-bikes is associated with a reduction in motorized vehicle use, which has potential positive impacts on the environment and health. The impacts of e-bike share schemes and workplace initiatives are less well understood. Evidence describing the purposes for which e-bikes are used, and the factors associated with usage, are useful to inform e-cycling promotion policy.

From conventional to electrically-assisted cycling. A biographical approach to the adoption of the e-bike

Sales of electrically-assisted bicycles (e-bikes) have been rising in many European countries. Due to their electrical assistance, e-bikes could increase the number of people cycling and the potential uses of cycling. Existing research has not investigated the links between conventional cycling and e-bike use at the individual level. Using qualitative, retrospective data, this paper aims to determine how e-bike use fits into an existing cycling trajectory over the life course. E-bike users in the Swiss city of Lausanne (N = 24) are interviewed to compare their cycling trajectories. They fall into two main trajectories: “restorative” and “resilient”, which each represent different relationships to cycling and different reasons to adopt the e-bike over the life course. E-bikes might serve as both a way to restore an interrupted cycling practice, or to keep existing cyclists despite threats posed by changing personal and spatial contexts.

Getting Physically Active by E-Bike: An Active Commuting Intervention Study

<strong>Introduction:</strong> Increased physical activity (PA) is a major component in promoting public health, and active commuting could make an important contribution. Using an electric bicycle (e-bike) is reported to be of at least moderate intensity PA and is an alternative to other modes of transport for commuting. We aimed to explore the experience of commuting by e-bike in an intervention study. <strong>Methods:</strong> Focus group interviews with 21 previously inactive participants from three cities in Norway, who were given access to an e-bike for commuting for three (N = 2) or eight months (N = 19). Participants cycled for 226 days on average. The interviews were analyzed using systematic text condensation. <strong>Results:</strong> The use of e-bikes and active commuting stimulated some participants to take on additional PA in their leisure time. Others preferred active commuting to be their daily PA. Participants’ experiences of commuting by e-bike were characterized by a motivational shift from external reasons for e-biking, such as pressure to get fit, to feelings of well-being, enjoyment and recognition of the health benefits. <strong>Conclusion:</strong> Commuting by e-bike was convenient and promoted good health. Promoting e-bikes could lead to long term changes among inactive people because positive experiences stimulate autonomous reasons for active commuting.

Effect of environmental awareness on electric bicycle users’ mode choices

Travellers’ environmental awareness can affect their mode choices. The primary objective of this study is to identify the effect of electric bicycle (e-bike) users’ environmental awareness on their mode choice when the use of e-bikes is prohibited in urban areas in China. The data were collected via a questionnaire survey administered at ten locations in Nanjing, China. Using mixed multinomial logit (MMNL) models, we examined the relationship between the e-bike users’ mode choice and their environmental awareness, combined with socioeconomic and demographic characteristics and trip attributes. The results show that the level of environmental awareness, gender, age, education, income, the ownership of car and conventional bike, and trip distance affect e-bike users’ choices significantly. Those with a high level of environmental awareness are more likely to choose zero-emission transport modes. A stratified analysis reveals that the effect of environmental awareness is associated with their original transport mode choice prior to their use of the e-bike. With a high level of environmental awareness, original car users tend to opt for moderate- or zero-emission modes; original bus and metro users incline to choose a zero-emission mode or their original mode; and few original cyclists and walkers favour moderate- or high-emission modes. The results of the current study provide transport authorities with insights to establish sustainable urban transportation management policies and strategies to increase the share of zero- and low-emission transport modes.

Injury patterns associated with personal mobility devices and electric bicycles: an analysis from an acute general hospital in Singapore.

The increasing popularity of personal mobility devices (PMDs) and electric bicycles (e-bikes) in Singapore has met with growing public concern over safety. Following the passing of the Active Mobility Bill, there remains a gap in the local literature in terms of injury patterns arising from the use of these devices. A retrospective study was conducted on the Singapore National Trauma Registry (SNTR) from the emergency department of an acute hospital from 1 January 2016 to 31 December 2016. All patients with injuries related to the use of PMDs and e-bikes were included. Data captured included demographic information, device used and injury severity score (ISS). The 259 patients identified had a mean age of 32.1 years and a male predominance. Most (83.4%) were injured from falling off their devices. Devices commonly associated with injury were scooters, skateboards and motorised bicycles. Most injuries were mild (94.6%, ISS < 9) and were mainly external wounds (89.2%) and upper and lower limb injuries (35.5%). There was no difference in ISS in terms of the device used (motorised vs. non-motorised). More severe injuries (ISS ≥ 9) were caused by e-bikes (42.9%) and electric scooters (28.6%). Factors associated with these injuries were older age, use of e-bikes, and injuries to the head, face and thorax. Most patients (86.1%) were discharged and one died. While most injuries associated with PMDs and e-bikes are minor, serious injuries could result from their use. The SNTR should be updated to include PMDs and e-bikes as a category.

Modal shift implications of e-bike use in the Netherlands: Moving towards sustainability?

This paper investigates the modal shift patterns of e-bike users in the Dutch context. We focus on the change in e-bikers’ travel behavior to assess whether this change benefits sustainability. Our study provides direct ecologically valid evidence on modal shift by using a longitudinal dataset from the Netherlands Mobility Panel survey. We examine e-bikers’ modal shift patterns before and after acquiring an e-bike. The findings indicate that after e-bike adoptions, conventional bike use reduces significantly, while car use reduces less strongly. Nonetheless, the share of car kilometers is much larger than that of conventional bikes at the baseline. Besides, the emission rate per passenger kilometer of an e-bike is several times lower than that of a car. These imply a net environmental gain after e-bike adoptions. The present study also sheds light on modal shifts at a disaggregated level by investigating those e-bikers who are more likely to drive less after e-bike adoption. The findings suggest that e-bikers younger than 50 and those around retirement age (60–69) seem more likely to step out of their cars. Additionally, people living in rural areas tend to be more likely to reduce their car use than their counterparts in highly urbanized areas. Based on our findings, we present policy recommendations for achieving a greener shift in mobility systems.

Commuter e-bike use is associated with increased total physical activity over time

Abstract Background Active commuting such as cycling has been recommended as a practical way to increase population physical activity levels and improve health. Beside positive health effects observed in experimental studies, less is known about how e-bikes can improve public health outcomes in real-life settings. Objectives To describe the patterns over time in physical activity, sedentary behavior, cardiorespiratory fitness and psychophysiological well-being in a population of new commuter e-bike users. Methods This was a population-based prospective longitudinal study in France. 33 new users of e-bike were recruited and measured at 4-time points (T0, T1, T2, T3), from before the effective beginning of the e-bike use (T0) until a mean of 4.6 months of use (T3) (min: 3.2, max: 7). Repeated measures multilevel modeling was used on 4 waves of measurements to assess variations over time of e-bike use, total moderate and vigorous physical activity (MVPA) and total sedentary time (SED-time). Pre-post comparisons between T0 and T3 were performed to detect change in cardiorespiratory fitness, markers of adiposity, self-esteem, quality of life (SF12-v2), and physical activity motivations (EMAPS). Results Adherence to e-bike commuting was high and stable, with a mean of 82% (3%) of weekly use. Multilevel modeling indicated that MVPA followed a significant increasing curvilinear trajectory over time, plateauing after 90 days (+ 21 MET-h/week compared to mean T0 at 33 MET-h/week). No significant change was found in SED-time, averaging a mean of 8.6 hours/day. Greater adherence to using an e-bike was associated with greater increases in MVPA. Several markers of physical and mental health improved. Conclusions New commuter e-bike users undergo a significant increase in MVPA while being already at a high level before e-bike use onset. This increase in MVPA could be meaningful to attenuate detrimental effects of a persistent high sedentary behavior. Key messages MVPA significantly increases among new commuter e-bike users, along with improvements in physical and mental health. High SED-time is persistent, making increase in MVPA in a population with a moderately high initial level still crucial.

Pedal-Assist Mountain Bikes: A Pilot Study Comparison of the Exercise Response, Perceptions, and Beliefs of Experienced Mountain Bikers.

BackgroundMountain biking is an aerobic physical activity that has experienced rapid growth. The emergence of the electric pedal-assist mountain bike (eMTB), while not without its critics, presents the potential for an even larger segment of the population to enjoy the health benefits of mountain biking. Although the research focused on the use of e-bikes generally is growing, there is limited research specifically targeting eMTB use. Research is needed exploring the potential exercise response of riding an eMTB, together with the beliefs and perceptions of mountain bikers who have and have not experienced eMTB riding.ObjectiveThis study aimed to compare conventional mountain bike and eMTB use. This was done by investigating 2 questions: (1) What proportion of exercise response is retained for an experienced mountain biker while using an eMTB when compared with a conventional mountain bike? and (2) What are the perceptions and beliefs of experienced mountain bikers toward eMTBs both before and after riding an eMTB?MethodsA convergent mixed methods data collection approach was used in the study. Participants completed both a pre- and postride questionnaire, and data regarding heart rate were collected. Heart rates from each ride were compared against each other.ResultsThe average heart rate during eMTB use was 94% (31/33) of the average heart rate during conventional mountain bike use. Therefore, eMTB use in this study achieved a majority of the exercise response and exceeded established biometric thresholds for cardiovascular fitness. Paired t test statistics were calculated to compare beliefs of conventional mountain bikes and eMTBs and to compare mean heart rate and speed between conventional mountain bike and eMTB use on the study loop. Participants overwhelmingly perceived the potential impact of eMTB use to be positive on both pre- and post-eMTB ride questionnaires.ConclusionsDespite the measured benefit, participants’ perceived exertion while riding the eMTB was low.

Energy and greenhouse gas emission reduction potentials resulting from different commuter electric bicycle adoption scenarios in Switzerland

To ensure long-term availability of mobility and to keep the effects of humanity on global climate limited, politics, research, and industry currently search for ways to reduce greenhouse gas (GHG) emissions caused by it. A potential reduction could be achieved by transitioning towards more efficient transport modes, optimally powered by renewable energy. Based on mobility (commutes), population, and weather data from Switzerland, we present a model to compute energy savings due to a (hypothetical) widespread deployment of electric bicycles. In different scenarios, we analyze the dependence of the commuting energy demand on users’ preferences about when to take the bike, such as an aversion to biking on cold and rainy days. Our study shows that GHG emission reductions of up to approx. 10% of the total emissions from diesel and gasoline are possible. In combination with a widespread deployment of electric vehicles, further savings of up to 17.5% could be achieved. In particular, the willingness to drive longer commutes by e-bike influences the potential GHG emission reductions, followed by the willingness to use the bicycle at temperatures below 10°C. Using these results, we identify the spatial energy and greenhouse gas savings potential and thus regions that are particularly suited for e-bike use. The identification of regions with high saving potentials allows for targeted marketing, transition-supporting incentives, or infrastructural changes to maximize the reduction of emissions.

A Comparison of Manual versus Electric Bicycle Injuries Presenting to a Pediatric Emergency Department

BackgroundThe use of electric bicycles (E-bikes) has dramatically increased over the last decade. E-bikes offer an inexpensive, alternative form of transport, but also pose a new public health challenge in terms of safety and injury prevention.ObjectiveThe aim of this study was to describe the epidemiology and severity of E-bike related injuries among children treated in the emergency department (ED) and to compare these to manual bicycle related injuries.MethodsA retrospective observational study of all pediatric patients presenting to the ED between December 2014 and November 2015 with an injury related to E-bike or manual bicycle use. Data including demographics, diagnosis, injury severity score (ISS), and outcome were compared.ResultsA total of 196 cyclist injuries presented to the ED; 85 related to E-bike use and 111 to manual bicycle riders. The mean age of E-bikers was 13.7 years (7.5–16 years) and of manual bicycle riders was 9.9 years (3–16 years). Injuries to the head and the extremities were common in both groups. E-bikers had significantly more intra-abdominal organ injury (P=0.047). Injury severity scores were low overall, but injuries of higher severity (ISS>9) only occurred among the E-bikers.ConclusionsPediatric E-bike injuries tend to be more severe than those sustained during manual bicycle riding. Further research into bicycle and other road and pavement users could lead to enhanced regulation regarding E-bike usage.

Physical activity of electric bicycle users compared to conventional bicycle users and non-cyclists: Insights based on health and transport data from an online survey in seven European cities

Physical activity has been widely associated with beneficial health effects. The use of electric-assist bicycles (e-bikes) can lead to increased or decreased physical activity, depending on the transport mode substituted.This study aimed to compare physical activity levels of e-bikers and conventional bicycle users (cyclists) as well as across e-bike user groups based on the transport mode substituted by e-bike. Physical activity, transport and user related parameters were analysed. Data from the longitudinal on-line survey of the PASTA project were used. The survey recruited over 10,000 participants in seven European cities.Physical activity levels, measured in Metabolic Equivalent Task minutes per week (MET min/wk), were similar among e-bikers and cyclists (4463 vs. 4085). E-bikers reported significantly longer trip distances for both e-bike (9.4 km) and bicycle trips (8.4 km) compared to cyclists for bicycle trips (4.8 km), as well as longer daily travel distances for e-bike than cyclists for bicycle (8.0 vs. 5.3 km per person, per day, respectively). Travel-related activities of e-bikers who switched from cycling decreased by around 200 MET min/wk., while those switching from private motorized vehicle and public transport gained around 550 and 800 MET min/wk. respectively.Therefore, this data suggests that e-bike use leads to substantial increases in physical activity in e-bikers switching from private motorized vehicle and public transport, while net losses in physical activity in e-bikers switching from cycling were much less due to increases in overall travel distance.

The role of psychological and socio-demographical factors for electric bike use in Norway

The electric bike (e-bike) is emerging as a new sustainable transport mode in Norway and has the potential to lead to increased cycling among the population. However, little is known about psychosocial determinants of e-bike use. The aim of the study was to examine the role of normative and environmental beliefs, the perceived attributes of e-bikes, and innovativeness and demographical factors related to e-bike use in a Norwegian sample. An online survey was used to collect data from 910 respondents, including both e-bike users (252) and non-users (658). The respondents were recruited via a commercial panel (response rate 42.04%) and a Facebook post. A structural equation modeling analysis was used to analyze the data. The structural model had a good fit to the data. The results showed that attitudes towards e-bike use followed by innovativeness were the most important predictors of e-bike use. The normative processes measured within the Norm Activation Model activated positive attitudes towards e-bike use, which in turn predicted e-bike use. There was a negative relationship between e-bike and conventional bike use, while a positive relationship was found between car and e-bike use. The results are discussed with regard to their implications for interventions aiming to promote e-bike use.

Older adults’ environmental preferences for transportation cycling

IntroductionCycling is a non-polluting and healthy transportation mode for older adults. However, there is limited knowledge about the infrastructural changes necessary to stimulate cycling among older adults (≥65 years). This is particularly true for electric cycling (e-biking), an increasingly popular form of cycling. The current experiment with manipulated photographs examined the environmental preferences for transportation cycling among older adults. Additionally, it examined whether subgroups with different environmental preferences exist and whether these subgroups differ on socio-demographics, health characteristics, transport behaviour, e-bike use and cycling levels. MethodsA structured questionnaire and choice-based conjoint exercise was completed by 895 Flemish older adults. The conjoint exercise included 13 choice tasks each presenting two street situations, which were manipulated on nine environmental attributes. Hierarchical Bayes and latent class analyses were applied to obtain environmental preferences and identify subgroups. ResultsIn the total sample, type of cycle path was the most important environmental attribute (importance = 40.0, 95% CI = 39.0–41.0) determining older adults’ preference for transportation cycling. The second most important attribute was traffic density (16.7, 95% CI = 15.9–17.4), followed by cycle path evenness (11.8, 95% CI = 11.4–12.1) and distance (10.6, 95% CI = 10.1–11.0). Six subgroups with different environmental preferences were identified. These subgroups could be characterized based on differences in cycling limitations, driving status, e-bike use and cycling levels. ConclusionsThe provision of well-separated cycle paths should be considered a priority in urban planning initiatives aiming to stimulate transportation cycling among older adults. Such initiatives should be evaluated to validate the current findings and optimize future initiatives.

E-bike-related cranial injuries in pediatric population.

E-bikes are being used increasingly by all age groups. Children riding e-bikes often do not use safety equipment such as helmets, and are at increased risk for injuries requiring neurosurgery. The most common type of injury among pediatric e-bikers is head and neck trauma. We describe our experience treating cranial injuries. Data regarding children (< 18years old) with e-bike-related cranial injuries were collected retrospectively from two tertiary centers. Twenty patients were included. Seventeen were e-bike users, and three were hit as pedestrians. The average age at admission was 11.3 ± 4.85 (range 1.5-17) years old. All 17 e-bike users did not wear a helmet. Seventeen of the 20 (85%) suffered from skull fractures (70% involving the frontal bone), nine involving more than one region. Six patients (30%) had intracerebral contusions, 3 (15%) an epidural hematoma, and 6 (30%) a subdural hematoma. Three patients (15%) underwent surgery, two of them for depressed skull fracture reduction, and one for insertion of intracranial pressure monitor. One patient died (5%); 1 (5%) had a Glasgow Outcome Scale (GOS) of 3, 5 (25%) had a GOS of 2, and 13 (65%) were discharged without any neurological deficit (GOS 1). E-bikes may inflict various cranial injuries, including fractures and intracranial bleeds, and may lead to significant morbidity and mortality. Education of children to use protective gear, wide exposure of younger adolescents to traffic laws, and limiting the use of e-bikes to older children, are all necessary actions.

Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model

Rapidly increasing e-bike use in China has resulted in new traffic problems including rising accident rates at intersections related to e-bike drivers’ decision-making during multiple signal phases. Traditional one-step decision models (such as GHM) lack randomness and cannot adequately model e-bike drivers’ complex behavior. Therefore, this study used a Hidden Markov Driving Model (HMDM) to analyze e-bike drivers’ decision-making process based on high-resolution trajectory data. Video data were collected at three intersections in Shanghai and processed for use in the HMDM model. Five decision types (pass, stop, stop-pass, pass-stop, and multiple) composed of speed and acceleration/deceleration information were defined and used to analyze the impact of flashing green signals on e-bike drivers’ behavior and decision-making processes. Approximately 40% of drivers made multiple decisions during the flashing green and yellow signal phases, in contrast to the traditional GHM model assumption that drivers only make one decision. Distance from stop-line had the most obvious influence on the number of decisions. The use of flashing green signals nearly eliminated the dilemma zone for e-bike drivers but enlarged the option zone, inducing more stop/pass decisions. HMDM can be applied to improve the accuracy of traffic simulation, the fine design of traffic signals, the stability analysis of traffic control schemes, and so on.

Awareness, riding behaviors, and legislative attitudes toward electric bikes among two types of road users: An investigation in Tianjin, a municipality in China

Objective: This study aimed to understand the awareness, riding behaviors, and legislative attitudes of electric bike (e-bike) users and non-e-bike road users toward e-bikes in Tianjin, a municipality in China.Methods: This cross-sectional investigation included 2 components. The field survey was conducted in 9 districts (4 urban and 5 rural) from 2015 to 2017. Adult participants were interviewed using a street intercept method. The online survey was conducted using the WeChat social platform. A series of questions related to awareness and practice of e-bike safety-related issues was asked.Results: A total of 16,859 interviews were completed, including 1,653 (794 e-bike users and 859 other road users) in-person interviews by field survey and 15,206 (4,426 e-bike users and 10,780 other road users) by online survey. Except for ignoring a red light when making a left turn (P = .000) and waiting across the white line at a red light (P = .000), which e-bike users were lower than other road users, there were no significant differences between e-bike users and other road users in regards to the correct awareness rate of riding in the opposite direction, riding in a motor vehicle lane, running red lights, jaywalking, speeding, carrying adult passengers, and riding on the expressway (P > .05). In addition, 74.2% of e-bike users considered helmet use necessary (P = .000) and 54.7% thought that installing a windshield is wrong (P = .000), which was higher than other road users (49.1 and 48.4%, respectively). Other road users had greater awareness than e-bike users regarding whether turning left at a red light is wrong (91.7 vs. 85.1%), waiting across the white line when the light is red is wrong (86.6 vs. 75.9%), lighs should be turned on at night (94.8 vs. 90.3%). Correct practices were far behind awareness in all kinds of violation behaviors (P = .000) among e-bike users in the field survey. The rate of public support for legislation including those who very much support and support legislation was 93.34% in the field survey and 87.68% in the online survey.Conclusions: E-bike users had lower correct awareness rate than other road users regarding e-bike violation behaviors but were more concerned about safety protection than other road users. E-bike users’ correct practices were far behind their awareness for all kinds of violation behaviors. Public education on the traffic law needs to be strengthened among e-bike users. The public strongly supported special regulations for e-bike use, such as helmet use.