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Electric bicycles (e-bikes) represent one of the fastest growing segments of the transport market. Over 31 million e-bikes were sold in 2012. Research has followed this growth and this paper provides a synthesis of the most pertinent themes emerging over the past on the burgeoning topic of e-bikes. The focus is transport rather than recreational e-...
Context in source publication
Context 1
... Economist (2013) reports that in France traditional bike sales fell 9% in 2012, but e-bike sales increased 15%. Table 3 illustrates the total and normalised (per 1000 people) e-bike sales in EU member states (and Switzerland) during 2012. The Netherlands and Denmark have among the highest e-bike sales per capita in Europe, and are also the countries with the highest rate of general cycling (Pucher & Buehler, 2008). ...
Citations
... E-bike and e-scooter hire schemes offer sustainable ways of travel 3 and may have health impacts on the user 4 , but these are unclear [4][5][6] , particularly in the UK context. When referring to e-bikes, we mean cycles that the user must pedal for assistance; throttle-powered electric bicycles were not considered in this paper 7 . In the UK, the use of e-bikes is legal, but the UK government is yet to make a decision about legalising the use of e-scooters outside trial areas 8 . ...
Background This study aimed to collect information on e-bike and e-scooter use in areas with and without e-bike (EB) and e-bike plus e-scooter (EB+ES) combined share-hire schemes. Methods This study employed a repeated cross-sectional design. An online survey asking questions about demographics, travel, and health was completed by people in August and September 2023 before the schemes were launched in Bristol (EB+ES) and Leeds (EB), with Bradford and Sheffield as control sites. A resurvey was conducted at the same sites one year later, but also in Bath (EB+ES) and Plymouth (EB). We also interviewed eight e-bike and e-scooter users and non-users in Bristol (n=4) and Leeds (n=4). Results Following data cleaning, 3771 remained in the baseline sample and 5370 remained in the resurvey sample. The majority of participants reported having never used an e-bike (baseline: 61%; resurvey: 69%) or e-scooter (baseline: 77%; resurvey: 84%). At baseline, the most common e-bike access route was the use of their own e-bike (45%), with access via a share-hire scheme lower at 25%. In the resurvey sample, access levels were similar via a share-hire scheme (38%) and personal e-bikes (36%). The most common e-scooter access route was a share-hire scheme (baseline: 60%; resurvey: 74%). The most common weekly e-bike and e-scooter destinations were leisure/leisure venues, followed by work/education and shopping/errands. Half said they would not use an e-bike scheme and 63% indicated they would not use an e-scooter scheme. Potential users were willing to walk ~500 m to access an e-bike/e-scooter. Interviewees generally supported share-hire schemes, seeing them as a good addition to the wider transport offer, but with more support for e-bikes and reservations around e-scooters. Conclusions These data will be important for a later evaluation of EB and EB+ES share-hire schemes on public health, social, economic, and environmental factors.
... Additionally, the proliferation of e-bikes has facilitated bicycle commuting by reducing traditional barriers associated with travel distance, commuting time, physical fitness requirements, and topographical challenges. Nonetheless, the initial purchase cost remains a notable barrier to their wider adoption (Fishman and Cherry 2016). ...
... Furthermore, Castro et al. (2010) suggested a maximum efficient commuting distance of up to 7 km for conventional bicycles and up to 15 km for e-bikes. In this regard, Fishman and Cherry (2016) proposed that e-bikes could help mitigate barriers related to distance. However, high purchase costs and infrastructure limitations remain barriers to widespread adoption. ...
Active commuting by bicycle offers health and environmental benefits, yet it remains uncommon among university populations. This study aimed to identify key factors influencing bicycle commuting among university students and staff based on cyclist typology and to assess the applicability of the Theory of Planned Behaviour (TPB) and socio-ecological models. A total of 305 students and 79 staff completed a questionnaire assessing sociodemographic, psychological, social, and environmental variables. Results revealed significant differences based on cyclist typology. Urban cyclists reported fewer perceived barriers (1.96 ± 0.59) and more advantages (3.61 ± 0.40) than non-cyclists (2.71 ± 0.56 and 3.26 ± 0.49, respectively; p < 0.001). While personal and psychological factors were most influential for non-cyclists, environmental aspects were more relevant for urban cyclists and cyclists. Multinomial logistic regression showed that for both cyclists and urban cyclists, bicycle ownership (OR = 0.098-0.104, p < 0.001) and intention to use (OR = 0.091-0.358, p ≤ 0.02) were key predictors of cycling behaviour. Although gender was only a significant predictor for cyclists (OR = 3.41, p = 0.003), this variable did not influence urban cycling behaviour. These findings support using TPB and socio-ecological models to design targeted, multilevel interventions.
... Micromobility has been profusely analysed since its growing interest in the early 2000s (Fishman & Cherry, 2016). Micromobility, as defined by Dediu (2017), refers to vehicles weighing less than 500kg that occupy the space between slow mobility options like push bikes and traditional automobiles. ...
Mega-events, such as the Olympics and the FIFA World Cup, present a significant opportunity to catalyse urban development for the host city and enhance well-being. In preparation for these events, careful planning and deployment of the latest technology are critical for successfully transitioning from Mega-event to permanent development. This study focuses on the upcoming 2032 Summer Olympics, which will be held in the sporting venues of Brisbane, Australia, while looking at the transport network connecting these nodes with micromobility. This study aims to develop a novel workflow that can rapidly generate and iterate through design options using generative artificial intelligence (GenAI) imagery and crowd-sourced feedback to target the current limitations of standard methodologies. Street View Images (SVI) are collected alongside their geographic metadata around the proposed Olympic venues. A random selection of SVI undergoes AI alterations to augment portions of the SVI to enhance aspects of street design based on the essential design criteria of micromobility: natural, artificial, and human factors. Results show that the augmented SVI with pronounced way-pointing devices and cycling infrastructure rank better than their unedited counterparts. Urban designers are encouraged to design noticeable way-pointing signs and infrastructure catered to micromobility to encourage usage around cities.
... The convenience, environmental friendliness, and cost efficiency of e-bikes are the primary reasons for their widespread adoption [6,7]. The primary purpose of e-bike use includes commuting to and from work, as riders perceive them to be faster than bicycles; meanwhile, they are more convenient than public transportation and private cars [8,9]. In China, e-bikes are particularly valued as a flexible transport mode for short-to medium-distance travel [10]. ...
Electric bike (e-bike) accidents have emerged as a significant road safety concern in recent years. Employing a mixed-methods approach, this study seeks to elucidate the mechanisms underlying e-bike accidents and to develop an e-bike safe system aimed at enhancing e-bike safety and accident prevention. Quantitative analysis was employed to identify key components and their relationships through an event-based examination of a structured accident dataset using a Bayesian network. Complementing this, qualitative methods—including observations and interviews—were conducted to gain deeper insights into how riders interact with other components within the system. This study was carried out in Guangzhou, a metropolitan city with an increasing use of e-bikes and e-bike-related accidents. The key findings of this study are as follows: 1. The safe system of e-bike safety comprises critical components, including infrastructure (roads and facilities), e-bikes, riding behavior, individual riders, and other road users. 2. E-bike accidents predominantly result from dysfunctions of the safe system. The alteration of one component influences other components, which may, in turn, provide feedback to the original component. 3. While riders’ mistakes play a role, the interactions between riders and other components also contribute to the accidents. 4. At the individual rider level, barriers to safe riding include a lack of safety knowledge, low penalties for violations, and high opportunity costs associated with safe riding behaviors. Deficiencies in infrastructure, regulations, and law enforcement contribute to violations and risky riding practices. This study contributes to the current body of accident studies by developing an e-bike safe system.
... In this context, electric bicycles (''e-bikes'') are a promising way to increase the modal share of cycling, as they enhance the comfort of cycling through an integrated battery that amplifies the cyclist's pedaling power and thus enables cycling longer distances with less physical effort and time. In the context of our study, we define e-bikes as pedelecs (pedal electric cycle) with a maximum speed of 25 km/h (Philips et al., 2022;Fishman and Cherry, 2016). In Germany, conventional bicycles and e-bikes have become an important means of transportation for daily mobility and leisure activities. ...
... In recent years, the rising popularity of e-bikes has generated a wave of research on how e-bikes are changing travel behavior (see for example Zhou et al. (2023), Fishman and Cherry (2016), Cherry and Cervero (2007), Cherry et al. (2016), Jones et al. (2016), John et al. (2018), Winslott Hiselius and Svensson (2017), Wolf and Seebauer (2014), Bigazzi and Wong (2020), Kroesen (2017), Lee et al. (2015), Sun et al. (2020), Fyhri and Fearnley (2015), Ling et al. (2017)). A short coming of most of these studies is that they use self-recruitment methods, like web-based surveys or experiments in which e-bikes are provided temporarily to participants. ...
... Netherland) (see for example Kroesen, 2017;Lee et al., 2015;Haustein and Møller, 2016), and public transport in regions with a comprehensive public transport system (e.g. Fishman and Cherry, 2016;Cherry and Cervero, 2007;Cherry et al., 2016). However, many of these studies rely mainly on self-selection methods or cross-sectional data, which limits their ability to make within-individual comparisons over time, such as changes in transport-mode choice. ...
In 2023, 1.85 million conventional bicycles and 2.1 million electric bicycles (“e-bike”) were sold in Germany making it the first year in which more e-bikes were sold than conventional bicycles. This trend indicates a growing preference for e-bikes among consumers, so that it is important to study whether increasing sales would also translate into increasing usage of e-bikes. In their new article "Electrifying choices: How electric bicycles impact on mode choice and CO2 emissions", Thomas Hagedorn, Marlena Meier and Jan Wessel analyze (i) the influence of e-bike ownership on transport mode choice and (ii) how a change in e-bike ownership affects CO2 emissions in Germany. Using longitudinal data from household surveys from 2016 to 2022, we first conduct a trip-level analysis with a mixed multinomial logit model (MMNL model) to estimate mode choice probabilities. The results show that the change in e-bike ownership significantly affects travel behavior, by increasing the likelihood of choosing an e-bike as means of transportation by 14.6 percentage points (p.p.), while correspondingly decreasing the likelihood of choosing other modes, especially conventional bicycles by 5.6 p.p, as well as car and public transportation by about 4 p.p each. Second, by using observed changes in individual distances traveled and transport-mode-specific emissions values, we calculate net emissions savings per person after acquiring an e-bike. These savings amount to 526.9 kg CO2 per person and year, which is roughly 6.6% of the average annual total CO2 emissions per capita in Germany. The article is published in Transportation Research Part D: Transport and Environment.
... The development of light electric vehicles (LEVs) is crucial, especially in urban areas, discussed by Fishman and Sumarsono [5,6], where traffic congestion, air pollution, and ...
... The development of light electric vehicles (LEVs) is crucial, especially in urban areas, discussed by Fishman and Sumarsono [5,6], where traffic congestion, air pollution, and limited parking space are significant challenges (Sumarsono and Ehrenberger) [6,7]. LEVs, such as electric scooters, bikes, and small electric cars, offer a compact and efficient mode of transportation that can navigate crowded city streets with ease. ...
This research focuses on the development of a chassis dynamometer for light electric vehicles (LEV), utilizing the Prony Brake method for torque measurement. The primary goal was to create a robust testing platform that accurately assesses the performance characteristics of LEVs under controlled conditions. The dynamometer’s performance evaluation revealed an average error of 0.55 for RPM readings, indicating a moderate level of variability in the sensor’s accuracy. In contrast, the torque measurement yielded a significantly lower average error of 0.03, demonstrating high precision in capturing torque data. Additionally, a standard deviation of 0.34 was observed during the torque versus RPM assessments, reflecting the consistency of the collected data. These findings validate the effectiveness of the chassis dynamometer in delivering reliable performance metrics for LEVs, providing essential insights for future advancements in electric vehicle technology and performance evaluation methodologies.
... It is reasonable to expect that the FFPT program not only significantly reduced air pollutant concentrations, but also might have generated these additional benefits. Second, it is noteworthy that nearly 38 % of trips in Fuzhou were made by electric bikes, which pose significant safety concerns for public health (Fishman and Cherry, 2016). Investigating whether and to what extent the FFPT program can incentive e-bike users to switch to safer public transport would shed light on the potential health benefits of the program in terms of reducing traffic deaths and injuries. ...
Fare-free public transport (FFPT) programs are gaining increasing popularity worldwide as a policy tool to mitigate the negative externalities associated with automobile usage. However, evidence regarding their effectiveness in reducing air pollution, a major automobile externality, remains limited. In this study, we empirically examine the effect of FFPT programs on air quality in Fuzhou, a provincial capital city in China, based on a quasi-experimental design. Using difference-in-differences models, we find that Fuzhou’s FFPT program reduces fine particulate (PM2.5) concentrations by 0.332 µg/m3 (2.1 %) in the short run. Furthermore, the program leads to an increase of 129,486 rides (49.8%) in daily subway ridership. Back-of-the-envelope calculations indicate that the health benefits brought by the FFPT program through air quality improvements, including reduced mortality and healthcare expenditures, amount to about 3.09 billion Chinese yuan (or 478.92 million US dollars) annually, which is six times the loss of fare revenue. These findings highlight the potential of fare-free public transport as a sustainable urban transport policy in urban China and contribute to a better understanding of its cost-effectiveness.
... Just one week of e-bike use was demonstrated to have a positive effect on cognition and wellbeing (Leyland et al., 2019). In addition, e-bikes have the potential to address some of the perceived challenges of conventional bicycles, such as terrain, distance, and transportation of children and goods (Fishman & Cherry, 2016). As the transportation sector is the largest contributor of direct greenhouse gas emissions (GHGs) that are driving climate change, e-bikes also have the potential to reduce GHGs and help mitigate climate change (U.S. Environmental Protection Agency, n.d.; Cherry et al., 2009). ...
Active transportation such as walking, bicycling, scootering, or using a wheelchair has direct physical and mental health benefits. Electric bicycles (e-bikes) have great potential as a form of active transportation. The Massachusetts Clean Energy Center partnered with the Massachusetts Bicycle Coalition (MassBike) to develop and pilot test of a program to provide free pedal assist e-bikes with implementation support for income-eligible residents in Worcester, MA. MassBike received program funding over two years to provide 100 free e-bicycles to income-eligible individuals who live or work in Worcester utilizing a community-engaged approach. The e-bike pilot program took place from August 2022-May 2024. Overall, 64% of participants regularly reported their e-bike usage during the first 13 months of deployment. Per month, participants who reported their usage on average made 17 trips, rode 65 miles, and were physically active for 20 minutes per trip using their e-bike. Program results demonstrated a trend toward increasing number and mileage of trips across participants with seasonal variation, as well as a shift from recreational to utilitarian purposes. Lessons learned include the time needed for behavior shifts and retention and reporting challenges. These preliminary results offer a glimpse of the opportunities for increased physical activity possible with a climate change mitigation strategy of active transportation.
... In the past decade, the popularity of electric bicycles (E-bikes) has grown substantially due to their ease of use compared to traditional bicycles, which require more energy exertion by the user that can be exponentiated by longer distance rides and unfavorable conditions such as wind and heat [1,2]. E-bikes were introduced in the United States (US) in the early 2000s, but did not see significant growth in the market until the next decade; as of 2020, there were as many as 500,000 E-bikes being sold in the United States. ...
... The increase in E-bike-related injuries may be attributable to the increasing popularity of E-bikes in the recent decade, given that the upward trend in E-bike sales mirror injury trends reported by NEISS [23]. The reduced physical exertion required to commute by E-bike also explains its popularity, as traditional bicycles have seen a decrease in sales as E-bikes are becoming more popular [2]. ...
Objectives:
Electric biking (E-bike) is a growing recreation and transportation mode often linked to high-impact injuries. This study aimed to identify the age and sex-specific distribution and primary mechanisms of E-bike-related injuries in the US.
Methods:
The National Electronic Injury Surveillance System (NEISS) was queried for E-bike-related injuries present to US Emergency Department (Eds) from 1 January 2013-31 December 2022. Each narrative was reviewed to exclude injuries not sustained while directly operating an E-bike. Injuries that occurred while riding regular bikes, mopeds, or motorized vehicles that were not E-bikes were also excluded. Patient demographics, injured body part, diagnosis, and disposition were recorded. NEISS narratives were assessed to identify injury mechanisms. Annual injury trends were evaluated by exponential regression and case weighting was completed, all using SPSS statistical software.
Results:
A total of 45,845 nationally estimated (NE) E-bike-related injuries (1,049 NEISS Cases) presented to US EDs from 2013-2022. Exponential regression revealed a significant increase in annual injuries across the study period (p = 0.047). The frequency of injuries remained steady prior to 2019, with a 90.9% increase from 2019 (NE: 2,171) to 2022 (NE: 23749) (p = 0.005). Hospitalization was required for 10.9% of patients following E-bike-related injury, with hospitalization most frequently occurring through fracture (51.2%) and concussion (29.9%). The most injured body part for the youth, middle school and high school age groups was the head, whereas the college, young adult and adult group was the finger. Lastly, 30% of injuries associated with E-biking had motor vehicle involvement.
Conclusion:
The rate of E-bike-related injuries has risen significantly over the last decade, including a large increase post-COVID. Falls causing fractures were the most frequent injury type for both sexes. Greater than 10% of patients required hospital admission. The high rate of vehicle-induced collisions emphasizes the need for additional regulations and legislation to protect E-bikers.
... The integration of real-time air quality monitoring systems into mobile applications allows users to identify safer travel routes [66]. The use of electric bicycles (e-bikes) not only reduces physical exertion in hot conditions but also contributes to lower emissions [67]. Furthermore, policy measures, such as restricting car usage in specific areas or during peak times, have been proposed as strategies to reduce local pollution and encourage active travel [68]. ...
... Built Environment Strategies that focus on the development, modification, or revitalisation of the physical aspects of urban form to enhance transport functionality and user experience [17,19,22,[25][26][27][28][29]40,43,44,47,64,[69][70][71][72][73] Behavioural Adaptation Social and cultural measures aimed at influencing user behaviour towards more sustainable travel choices [32,46,48,49,55,74,75] Climate Adaptation Strategies designed to improve the comfort and practicality of mobility while ensuring that transport systems can withstand and adapt to climaterelated challenges [10,23,24,50,[52][53][54]62,65,76] Disincentives Policy measures implemented to discourage undesirable transport behaviours [16,66,68,77,78] Incentives Policy measures designed to motivate and reward sustainable transport behaviours [57,60,67] IT Adaptation The utilisation of information technology to improve transport system efficiency and user experience [63,79] Mode Integration Strategies designed to create seamless connections between various transport modes, enabling and enhancing the convenience of multi-modal journeys [41,45,58,59,80] Mode Optimisation Strategies aimed at improving an existing transport mode's operational efficiency and capacity [21,31,33,42,51,81,82] Safety and Security Strategies that prioritise the protection of users and assets within the transport system [18,20,56,61,83] 3. ...
... For instance, incentives, IT adaptation, safety and security, and mode integration are not strongly linked to any particular environmental constraint, yet they often function as enablers for other interventions. Incentives, such as subsidies for bike-sharing programmes, can amplify the impact of built environment changes by encouraging greater uptake of sustainable transport modes [67]. Similarly, IT adaptation, which includes the use of realtime information systems and mobile apps, enhances the effectiveness of mode integration and behavioural adaptation strategies by providing users with timely and accessible travel information [63]. ...
Active travel modes, such as walking and cycling, are essential for fostering sustainable urban transportation. However, their adoption in environmentally challenging areas—characterised by steep slopes, extreme weather, and rugged terrain—presents significant obstacles. This study addresses these challenges by conducting a systematic literature review of studies published between 2000 and 2024 to identify strategies that promote active travel in such contexts. Using a structured five-step methodology, 62 relevant articles were selected and analysed to explore common challenges and propose tailored solutions. The findings highlight critical barriers, including topographical difficulties, harsh climatic conditions, and adverse weather, all of which hinder walking and cycling. To address these barriers, this study identifies a range of solutions, including infrastructure enhancements such as bike lifts, e-bike systems, shaded walkways, and heated pavements, as well as policy measures like financial incentives and disincentive regulations. Importantly, this study makes a deliberate effort to avoid overgeneralised solutions by emphasising the need for interventions that are context-sensitive and tailored to specific environmental challenges, urban scales, and local conditions. By providing options for actionable strategies, this research offers a comprehensive foundation for developing inclusive and sustainable policies that encourage active travel in diverse and environmentally constrained urban settings.
