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E-bikes in the Mainstream: Reviewing a Decade of Research


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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-bike research, as well as the most critical research gaps requiring attention. China leads the world in e-bike sales, followed by the Netherlands and Germany. E-bikes can maintain speed with less effort. E-bikes are found to increase bicycle usage. E-bikes have the potential to displace conventional motorised (internal combustion) modes, but there are open questions about their role in displacing traditional bicycles. E-bikes have been shown to provide health benefits and an order of magnitude less carbon dioxide than a car travelling the same distance. Safety issues have emerged as a policy issue in several jurisdictions and e-bike numbers are now approaching levels in which adequate safety data are able to be collected. Research on e-bikes is still in its infancy. As e-bike usage continues to grow, so too will the need for further research, in order to provide the necessary data to inform policy-makers and industry.
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E-bikes in the Mainstream: Reviewing a
Decade of Research
Elliot Fishmana & Christopher Cherryb
a Institute for Sensible Transport, Suite 13/39 Park Crescent,
Fairfield, Melbourne Victoria 3078, Australia
b Department of Civil and Environmental Engineering, University
of Tennessee-Knoxville, 321 JD Tickle Bldg, Knoxville, TN
37996-2313, USA
Published online: 30 Jul 2015.
To cite this article: Elliot Fishman & Christopher Cherry (2015): E-bikes in the Mainstream:
Reviewing a Decade of Research, Transport Reviews, DOI: 10.1080/01441647.2015.1069907
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E-bikes in the Mainstream: Reviewing a Decade of
Institute for Sensible Transport, Suite 13/39 Park Crescent, Fairfield, Melbourne Victoria 3078,
Australia; ∗∗Department of Civil and Environmental Engineering, University of Tennessee-
Knoxville, 321 JD Tickle Bldg, Knoxville, TN 37996-2313, USA
(Received 5 November 2014; revised 29 June 2015; accepted 2 July 2015)
ABSTRACT Electric bicycles (e-bikes) represent one of the fastest growing segments of the trans-
port 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-bike research, as well as the
most critical research gaps requiring attention. China leads the world in e-bike sales, followed by
the Netherlands and Germany. E-bikes can maintain speed with less effort. E-bikes are found to
increase bicycle usage. E-bikes have the potential to displace conventional motorised (internal
combustion) modes, but there are open questions about their role in displacing traditional bicycles.
E-bikes have been shown to provide health benefits and an order of magnitude less carbon dioxide
than a car travelling the same distance. Safety issues have emerged as a policy issue in several jur-
isdictions and e-bike numbers are now approaching levels in which adequate safety data are able to be
collected. Research on e-bikes is still in its infancy. As e-bike usage continues to grow, so too will the
need for further research, in order to provide the necessary data to inform policy-makers and
1. Introduction
There is a growing interest in the bicycle’s role in Western urban transport systems
(Fishman, 2014; Handy, van Wee, & Kroesen, 2014; Pucher & Buehler, 2012). Com-
bining more bicycle-friendly cities with rapid advances in technology has resulted
in a dramatic increase in the purchase and use of e-bikes (MacArthur, Dill, &
Person, 2014). Commercially available e-bikes originated in Japan in the early
1980s (Rose, 2012), but technological and cost factors limited market attractiveness
until the early 2000s (Jamerson & Benjamin, 2013). Improved battery and motor
technology, component modularity, as well as economies of scale improvements
have meant e-bikes can now travel longer distances, are faster, and are more
affordable than ever. In the past decade more than 150 million e-bikes have
been sold (Jamerson & Benjamin, 2013), the largest and most rapid uptake of
alternative fuelled vehicles in the history of motorisation.
Corresponding author. Email:
Transport Reviews, 2015
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The dramatic growth in e-bike use has several important implications for
researchers and practitioners across a variety of fields, including transport plan-
ning, engineering, traffic safety, public policy, and the bicycle market. A
growing body of research since the mid-2000s examines a wide variety of ques-
tions related to e-bikes. This paper sets out to review a decade of research on
the burgeoning topic of e-bikes, focusing on emerging themes in e-bike research,
as well as the most critical research gaps requiring further study. This paper is
organised around several topical themes, with geographic difference included
within those theme areas. The following sections describe: categories and defi-
nitions, demographics, global markets, purchase motivation, impacts on trip fre-
quency and mode choice, health, environment, and safety. The last section
summarises key findings across these topics.
1.1. Review of the Literature
Relevant papers were collected via a scan of Scopus and Google Scholar databases,
using the terms ‘electric bike’, ‘electric bicycle’, ‘e-bike’, and ‘pedelec’, conducted
between May and November 2014. This review is limited to vehicles that are
classified as bicycles in most contexts, they have two wheels, they have a mode
of human assistance (even if not required for electric power assistance), and
they are relatively low speed. This review includes small electric scooters that
are prevalent in China and regulated as bicycles, but does not include electric
motorcycles capable of high speeds and without pedal assistance. The next
section describes these definitions and regulations that bound this review. Most
research published in the late 2000s focused on growth in the Asian market.
Most North American and European research has been published in the past
five years. The grey literature was also scanned, especially for industry reports,
which may not be identified in academic search engines. This review was
restricted to English language publications, and emphasises on transport rather
than recreational e-bike research.
E-bike research varies by subject and geography. Eastern (e.g. China) studies
tend to focus on operations, safety, and market growth revolving around large
volumes of e-bikes. Western studies (e.g. North America and Europe) tend to
focus on emerging markets, health, and behaviour of nascent markets. This
paper focuses on both geographic and topical themes in the literature aiming to
answer important questions about safety, mode shift, behaviour, demographics,
and technological trends. The paper’s purpose is to provide the policy-maker,
researcher, and industry participant with a succinct distillation of the latest
research concerning e-bike usage, impacts, and trends.
2. Categories, Design, and Performance of Electric Two-Wheelers
A wide variety of e-bikes are commercially available, with varying performance
and design characteristics (Cherry, Weinert, & Xinmiao, 2009; Rose, 2012). There
is a spectrum of e-bike designs, from bicycle-style e-bikes (BSEBs) to scooter-
style e-bikes (SSEBs). This section provides brief definitions, characteristics, and
operating performance of different variations of e-bikes.
There are a few clear defining lines between the different e-bike technologies
(Figure 1). The underlying technology is the same, with three main components:
battery, controller, and motor. The distinctions are mainly related to performance
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(e.g. speed), cosmetic design, and two main control modes (throttle control or
pedal assist). The spectrum of designs from BSEB to SSEB is illustrated in
Figure 1, where the top-left (a) vehicle is clearly a BSEB and the bottom right (f)
vehicle is clearly an SSEB. The biggest design change occurs between (c) and
(d), but both vehicles have common features (i.e. flat footboard and pedals) and
the key differences are purely cosmetic. Most pedals on SSEBs do not provide
much function and are generally included for regulatory purposes. BSEBs can
be solely electric powered or require pedal assistance. The motor power generally
ranges from 200 W to 1000 W, weight range from 20 kg to 45 kg, electric range can
be as high as 150 km, and speeds are generally less than 45 km/h. The predomi-
nant European and North American bike designs include derivatives of Figure
1(a), while China’s market includes all styles of e-bike shown in Figure 1.In
some Chinese cities, the vast majority of e-bikes fall in the style of vehicles
shown in Figure 1(d) (f), where those styles of e-bikes are relatively rare in
North America and Europe. As such, most e-bike research in North America
and Europe follows the same themes as bicycle research. Research in China
often parallels approaches seen in research of larger powered two-wheelers.
It is important to note that the term ‘e-bike’ is synonymous with BSEBs and
SSEBs in the literature. Unless explicitly stated otherwise, e-bike will be used in
this paper to refer to all electric two-wheelers shown in Figure 1. In general, refer-
ences to e-bikes in the European, North American, and Australian context refer to
BSEBs (i.e. the bicycle has functional pedals, but is assisted by an electric motor),
consistent with the distinction made by MacArthur et al. (2014). References to
e-bikes in the Asian context can include any e-bike shown in Figure 1. With
improvements in battery and motor technology, there is a trend for e-bike
design to more closely resemble traditional bicycles. In most countries, e-bikes
that require pedalling to activate the motor (pedelecs) are legally classified as
bicycles. Regulations vary across countries, regions, and even between jurisdic-
tions in the same urban area, with some classifying them equivalent to motor-
cycles, sometimes intentionally and sometimes because regulations have failed
to keep up with technology. A very brief synthesis of complex regulations is as
Figure 1. Range of e-bike designs, from BSEB (a) to SSEB (f).
E-bikes in the Mainstream 3
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follows, and is presented in Table 1 (adapted from Macarthur and Kobel (2014)). In
China, e-bikes are classified, through national technical industrial standards, as
bicycles if they weigh less than 45 kg, travel less than 20 km/h, and have
pedals. The vehicles are regulated at the factory and regulations can be loosely
enforced with most e-bikes exceeding top speed and weight. Some cities impose
added local use restrictions. In the USA, e-bikes are regulated by the Consumer
Product Safety Commission and are required to travel lower than 32 km/h
solely on electric power and have top motor power of 750 W. The European Com-
mission regulates throttle-controlled e-bikes through a type of approvals process,
with two main categories, ‘powered bicycles’ (speed ,25 km/h, motor power ,
1000 W) and ‘moped’ (speed 25 45 km/h, motor power 10004000 W). Pedelecs
(speed ,25 km/h, motor power ,250 W) are regulated as bicycles. So-called
Speed-pedelecs (S-Pedelecs) are classified as mopeds and generally require
additional licencing and rider regulations. Interested readers are encouraged to
refer to Macarthur and Kobel (2014) and Reed Business Information (2014).
3. Demographics of E-bike Users
As with other transport modes, e-bike users are diverse. A number of studies have
examined the demographic characteristics of e-bike users in different cities and
3.1. North America and Europe
Qualitative research conducted in California found participants (e-bike users in
the Sacramento area) were older, better educated, and had higher incomes than
Table 1. Main performance regulations of global e-bike markets (modified from
Macarthur and Kobel (2014)
Motor power
limit (W)
Top speed
(km/h) Notes
USA 750 32 Operable pedals required. S-pedelecs are
allowable above 32 km/h since they require
human power. States are allowed to regulate
use differently
Canada 500 32 Electric power above 3 km/h
Australia 200–250 Operable pedals required. Pedelecs allowed
250 W power. Throttle e-bikes allows 200 W
European Union
250 25 Pedelecs classified as bicycles
European Union
(powered cycles)
250–1000 25 Requires pedals. Generally throttle powered
European Union
1000– 4000 45 S-pedelec must weigh ,35 kg. Motor power
must be ,4 times human power
China 12 Pedals required, but often removed by consumer/
retailer. Must weigh ,45 kg. Cities can
regulate use differently. Inconsistent
enforcement of design standards
Japan 250 24 Pedelec only, power tapers from 15 to 24 km/h
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the general California population (Popovich et al., 2014). In the first comprehen-
sive North American study of e-bike users (MacArthur et al., 2014), respondents
were 85% male, 71% were over 44 years old, and 90% were Caucasian. Some
34% had a graduate degree. Almost all respondents (94%) rode a traditional
bike as an adult prior to using their e-bike. An Austrian study (Wolf & Seebauer,
2014) found e-bike owners to be disproportionately older than the general popu-
lation, and consequently more likely to be retired. In contrast to other studies, Wolf
and Seebauer’s (2014) sample were more likely to own a car, and have lower edu-
cational and income levels than the general population. To some extent this may
simply reflect the fact that the Austrian study participants were recruited using
traditional mail surveys, whereas most of the other studies reported in this
section relied on online surveys, which may attract a younger demographic.
3.2. Asia and Australia
Cherry and Cervero (2007) examined traditional and e-bike users in China and
found e-bike users had significantly higher income and educational attainment
than traditional bike users, suggesting e-bikes are a motorisation stepping stone
as China’s economy grows. A study on Australian e-bike users (Johnson &
Rose, 2013) received 529 respondents, of which 71% were male and just over
half were aged between 41 and 60 years. Interestingly, 47% earned considerably
more than the population average, and had high car ownership rates (94%)
(Australian Bureau of Statistics, 2013). Similarly, respondents had achieved
higher levels of education than the general Australian population (.70%
having a tertiary degree). A common weakness in the studies identified above
is self-selection bias since the survey methods rely on e-bike owner response.
More research is required using sampling techniques capturing a more represen-
tative sample of the general e-bike user population.
4. Global E-bike Sales
An estimated 31 million e-bikes were sold in 2012 with forecasts to reach 47.6
million by 2018 (MacArthur et al., 2014). E-bike sales data are limited due to defi-
nition challenges across jurisdictions and lack of public registration processes.
Available data show China accounts for some 93% of global e-bike sales in 2012
(Table 2) and has resulted in approximately twice as many people owning
e-bikes as cars (Ji, Cherry, Bechle, Wu, & Marshall, 2012). E-bikes sold in China
are almost exclusively throttle-controlled, requiring no pedaling effort on behalf
of the rider and often take the form of SSEBs.
4.1. Asian E-bike Sales
The rate of e-bike sales growth in China outstrips other personal modes (Ji et al.,
2012). Weinert, Ma, and Cherry (2007) identify that credibility to e-bikes was
granted through legislation that governed standards for e-bike size and perform-
ance characteristics. Moreover, e-bikes were formally classified as bicycles
(China Central Government, 2004), and thereby avoided the licencing and
helmet regulations associated with some other powered two-wheelers, as well
as allowing their use on standard bicycle infrastructure. Perhaps the most
E-bikes in the Mainstream 5
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pertinent policy prompting the growth in e-bikes in China came from a ban on
petrol-powered scooters and mopeds in many cities (Yang, 2010).
E-bike sales in other developing Asian countries have been slow relative to
China. Many Asian cities rely on gasoline two-wheelers that have higher speeds
and cargo-carrying capacity than most e-bikes. Most Asian cities do not have
robust dedicated bicycling infrastructure, which benefits e-bike riders in China.
Moreover, lack of marketing, experience, maintenance infrastructure, and
higher up-front costs of e-bikes diminish overall market share (Cherry & Jones,
2009; Jones, Cherry, Vu, & Nguyen, 2013). Some Indian and South Asian
markets have experienced growth in the industry. Developing Asian countries col-
lectively sold fewer than 200 000 e-bikes in 2012 (Jamerson & Benjamin, 2013).
Japan is a unique case in the Asian e-bike market. As technological innovators,
many of the key early e-bike developments (batteries and motors) came from Japa-
nese companies. E-bike sales in Japan are approaching half a million annually, far
exceeding all other Asian markets combined, except China (Jamerson & Benjamin,
4.2. European E-bike Sales
Germany and the Netherlands are the two leading e-bike markets in Europe,
accounting for 44% and 21% of all EU sales. The next closest countries, in terms
of gross sales, are France, Italy, and Austria, each with 5% of EU sales (Association
of the European Two-Wheeler Parts’ and |Accessories’ Industry, 2013). European
e-bike sales grew almost 10-fold between 2007 and 2012 (European Two-Wheel
Retailers’ Association, n.d.). In the Netherlands, 16.9% of new bicycles sold are
Table 2. Global e-bike sales (estimates)
2011 2012 2013 2014 2015
China 31 000 000 29 000 000 31 600 000 34 200 000 36 800 000
India 79 000 29 000 39 000 40 000 70 000
Japan 409 000 385 000 400 000 420 000 440 000
Europe 1 234 500 1 483 000 1 759 000 2 016 000 2 318 000
Taiwan 33 500 31 000 33 500 36 000 39 500
SE Asia 50 000 65 000 75 000 80 000 90 000
USA 80 000 100 000 200 000 250 000 300 000
Total 32 886 000 31 093 000 34 106 500 37 042 000 40 057 500
Sources: Taken from Electric Bikes Worldwide Reports (2013), China data from 2011, and China Bicycles
Association. Other sources report 2012 ranging from a low of 22 million to a high of 31 million. EBWR
concludes 2012 small sales dip with growth to 2015. India data from Naveen Munjal of HeroEco
(includes e-bikes and SSEB that are called scooters in India. Japan data from Japan Bicycle Promotion,
Institute and Masao Ono Tokyo R&D Co. Ltd. European data from Hannes Neupert, ExtraEnergy.
Taiwan data from Victor Ko KYMCO (with e-moped 25 km/h max., 40 kg weight classed as e-bike). SE
Asia data from Prakit Lertyaovarit, Bangkok Cycle. The USA data from a survey of 54 companies and
total was extrapolated from a small number of survey returns. Several USA companies were reporting
strong sales beginning in 2013 suggesting that this may be a ‘breakout’ year for EB sales in the USA and
is reflected in the 2013 to 2015 estimates.
Disclaimer from EBWR: EBWR sales tables are estimates based on information from a variety of
sources, domestic/overseas. 2013, 2014, and 2015 numbers are EBWR estimates based on extrapolating
trends. There is no official record of EB/ES sales in most countries. A caveat on China EB numbers:
these include SSEBs that have pedals, or sometimes only provisions for pedals. These are legally e-bikes
in China but also can be regarded as light motor scooters.
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e-bikes (Association of the European Two-Wheeler Parts’ and Accessories’ Indus-
try, 2013). The 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). In
these countries, infrastructure and safety barriers have been overcome and there-
fore offer the most fertile market for the introduction of the e-bike. In Switzerland
e-bike sales accounted for one in every seven bicycles sold in 2011, with total
volumes increasing 25% on the previous year. It is now estimated that e-bikes
account for just over 5% of the national bicycle fleet in Switzerland (Bike
Europe, 2015). Some distortion may exist in these Swiss sales figures, because of
cross-border purchases, given favourable currency rates.
4.3. E-bike Sales in North America and Australia
E-bikes in North America are somewhat difficult to track because about half of the
e-bikes in the market are ‘retrofit’ kits, where cyclists convert their conventional
Table 3. E-bike sales in the European Union & Switzerland, 2012
share (%) Population
E-bike sales
per 1000
Total bike
sales (all
percentage of
total bike sales
The Netherlands 175 000 21 16 779 575 10.4 1 035 000 16.9
Switzerland 50 000 28 039 060 6.2 330 313 15.1
Denmark 30 000 4 5 602 628 5.4 550 000 5.5
Austria 45 000 5 8 451 860 5.3 410 000 10.9
Germany 380 000 45 82 020 578 4.6 3 966 000 9.6
Belgium 25 000 3 11 161 642 2.2 450 000 5.6
Luxembourg 1000 0 537 039 1.9 10 000 10.0
Sweden 11 000 1 9 555 893 1.2 555 000 1.9
Czech Republic 10 000 1 10 516 125 1.0 350 000 2.9
Slovenia 2000 0 2 058 821 1.0 250 000 0.8
Finland 5000 1 5 426 674 0.9 330 000 1.5
Italy 46 000 5 59 685 227 0.8 1 606 000 2.9
France 46 000 5 65 633 194 0.7 2 835 000 1.6
Lithuania 2000 0 2 971 905 0.7 115 000 1.7
Spain 30 000 4 46 704 308 0.6 780 000 3.9
Great Britain 30 000 4 63 896 071 0.5 3 600 000 0.8
Ireland 2000 0 4 591 087 0.4 95 000 2.1
Slovakia 2000 0 5 410 836 0.4 300 000 0.7
Poland 5000 1 38 533 299 0.1 992 000 0.5
Greece 1000 0 11 062 508 0.1 320 000 0.3
EU 27 854 000 100 492 448 575 1.7 18 879 313 4.2
Note: The Bold signifies EU totals and averages.
Source: EU e-bike sales (Association of the European Two-Wheeler Parts’ and Accessories’ Industry,
EU Population (European Commission, 2013) and Swiss data (Bike Europe, 2015; Hummel, 2015)
NB: Hungary, Bulgaria, Cyprus, Estonia, Latvia, Malta, and Romania recorded zero e-bike sales and
have therefore not been included. Swiss data are from 2013.
E-bikes in the Mainstream 7
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bicycle to an e-bike by adding a motor, battery, and controller kit (MacArthur et al.,
2014). Combing through customs and import data, Benjamin (2014) estimates that
about 200 000 e-bikes were bought into the USA in 2013. No systematic sales data
are captured on e-bike sales in Australia (Johnson & Rose, 2013) and therefore the
authors are unable to report even an approximate figure on the number of e-bike
sales there.
5. E-bike User Benefits and Motivations for Purchase
A key benefit of e-bikes is that they can maintain speed with less effort (Popovich
et al., 2014). This helps to overcome some of the most commonly cited barriers to
traditional bike riding. Online surveys from 553 e-bike owners in North America
(MacArthur et al., 2014) and 529 e-bike owners in Australia (Johnson & Rose,
2013) suggest the increased speed and reduced physical exertion are motivating
factors for e-bike purchase, allowing riders to arrive at their destination in a comfor-
table state (Popovich et al., 2014). Heinen, van Wee, and Maat (2010) review factors
associated with bicycle commuting and note that topography, distance, and time
limitations can act as barriers to bicycle riding (non e-bike). E-bikes potentially miti-
gate each of these factors. High temperatures, poor air quality, and precipitation can
also push riders towards e-bikes instead of bicycles (Campbell, 2012). There is some
evidence that e-bikes provide mobility to those with physical limitations that pro-
hibit cycling (Langford, 2013; MacArthur et al., 2014; Rose, 2012). A consistent
theme emerging from interviews with Californian e-bike owners is that the electri-
cal assistance offered by e-bikes had made cycling fun again (Popovich et al., 2014).
E-bikes enable longer trips for a greater variety of trip purposes (Langford, Cherry,
Yoon, Worley, & Smith, 2013). The aforementioned Austrian study found that
motivation for e-bike use differs according to trip purpose. For those using
e-bikes predominately for transport purposes, the social context (positive to
e-bikes) and environmental beliefs are important determinants, whereas for
leisure use, health is a more important motivator (Wolf & Seebauer, 2014).
One of the most frequently cited benefits of e-bikes is the potential to act as a
replacement for motor vehicle use. This appears to be a key motivation for
e-bike purchase from Australia and North America (Johnson & Rose, 2013;
MacArthur et al., 2014; Popovich et al., 2014). While much of the research relies
on self-reported behaviour and is subject to response bias, research suggests
e-bikes may reduce the number of trips taken by car.
6. Travel Behaviour Impacts
Where e-bikes are used as a replacement for motor vehicle trips, potential benefits
may arise through reductions in congestion, emissions, and improvements to
health through physical activity and lowering local air pollution (Gojanovic,
Welker, Iglesias, Daucourt, & Gremion, 2011; Rose, 2012). This section reviews
research examining the impacts of e-bikes, in terms of changes in travel behaviour,
health, and the environment.
6.1. Impact on Riding Frequency and Distance
The previously reported study of e-bike users in North America (MacArthur et al.,
2014) found e-bikes may increase cycling participation. Some 55% of the sample
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indicated they rode weekly or daily prior to owning an e-bike and since e-bike
ownership, 93% ride weekly or daily. Despite potential self-selection bias, or
short-term novelty of e-bikes, this result would appear too large to discount
entirely. E-bike riders in a mixed e-bike and conventional bikeshare system rode
13% farther than their conventional bikeshare counterparts (Langford et al.,
2013). A recent Norwegian study randomly selected 66 individuals who were
given an e-bike and compared their use to a control group of 160 individuals
(Fyhri & Fearnley, 2015). The researchers found cycling trips increased from 0.9
to 1.4 per day and distances increased from 4.8 km to 10.5 km following the pro-
vision of the e-bike. The control group showed no increase. The increase among
the e-bike group was greatest for women.
Cherry and Cervero (2007) examined e-bike usage in two major Chinese cities,
Kunming and Shanghai. E-bike users were found to travel greater distances than
those using traditional bicycles (Kunming +22%, Shanghai +9%). This study
also found travel speed was between 10 and 15% higher for e-bikes, but mean
travel times were similar, supporting literature on constant travel time budgets
(Marchetti, 1994). A more recent study by the same authors found that e-bike
tour distances increased by over 50% between 2006 and 2012 (Cherry, Yang,
Jones, & He, 2014).
6.2. Impact on Mode and Vehicle Choice
In China, e-bikes are a mainstream mode with dramatic potential to replace other
motorised modes. In Kunming, up to 25% of e-bike riders substitute car-based
trips and nearly 60% replace public transport (bus) trips. Only a small fraction
(7%) of e-bike trips substitute bicycle trips (Cherry et al., 2014). This finding is
somewhat consistent across different cities with high-quality transit systems,
including Shanghai (Cherry & Cervero, 2007) and Jinan (Montgomery, 2010). By
contrast, e-bikes substitute more than 60% of bicycle trips in Shijiazhuang
(Weinert, Ma, Yang, & Cherry, 2007).
Two other studies, one in Taiwan (Chiu & Tzeng, 1999) and one in Vietnam
(Jones et al., 2013), investigated two-wheeler purchase decisions using the
stated preference method. Chiu and Tzeng (1999) found that there is a significant
potential for e-bike adoption, especially among women. Jones et al. (2013) found
that in the context of stiff competition with traditional motorcycles, relatively low
performing e-bikes (i.e. SSEBs) have little chance for high market penetration
without substantial performance and price incentives.
A recent evaluation of the first e-bike sharing programme in North America
found displaced car trips accounted for 11% of all e-bike trips and 0% of all
bicycle trips in that system (Langford et al., 2013). Most of the displaced trips
for that programme were walk trips, since it was a campus-based system.
MacArthur et al. (2014) found replacing car trips was cited by almost 65% of
respondents as one of their primary reasons for beginning to use an e-bike. In
recent Australian research, 60% of respondents to an online survey cited replacing
some car trips as a main motivation for e-bike purchase, followed by 49% who said
they were motivated by being able to ride with less effort (Johnson & Rose, 2013).
Neither of those two surveys documents actual car substitution. The recent Nor-
wegian study referred to earlier found that those provided with an e-bike
increased the proportion of trips done by bike from 28% to 48% (Fyhri & Fearnley,
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The research to date on the impact of e-bikes on cycling and car use suggests
that e-bikes facilitate more frequent cycling, and trips of greater distance. In
North America and Australia and some Chinese studies, e-bikes appear to be
used as a replacement for some car trips, although little data exist to understand
the precise magnitude of this effect. An ongoing effort by several researchers aims
to generate more naturalistic use data that can result in detailed substitution
7. Health Impacts of E-bikes
Several studies have emerged over the previous decade seeking to measure the
impact of e-bikes on health. For the purposes of this review, the meaning of the
term ‘health’ is restricted to improved health from increased physical activity.
Simons, Van Es, and Hendriksen (2009) conducted a study in the Netherlands
on 12 healthy, physically active subjects, who rode a 4.3 km route three times
using an e-bike while measuring physiological performance. The first circuit
was undertaken without any power assistance, the second while the e-bike was
on eco mode, and the final circuit was completed using the most electrical assist-
ance. The researchers measured physiological variables such as heart rate and
oxygen consumption as well as power applied through the pedals. The results
showed that all three power settings provided a useful contribution to meeting
minimum physical activity requirements. Even with electrical assistance, riders
achieved the necessary physical activity intensity (between 3 6 Metabolic Equiv-
alent of Task, or METs
) to help reduce the chance of sedentary lifestyle diseases.
Not surprisingly, riders under the most powerful assistance setting achieved a
higher average speed, which had the effect of reducing overall riding time.
While this does have the effect of reducing the duration of physical activity,
there is some evidence to suggest that those riding e-bikes tend to spend more
time on their bikes than if they did not have an e-bike available (MacArthur
et al., 2014).
Gojanovic et al. (2011) set out to examine whether e-bikes were able to provide
sufficient physical activity for the user to gain health benefits. Conducted in a hilly
part of Lausanne, Switzerland, 18 sedentary participants (12 female) performed
four set trips at their own pace. The first trip involved a 1.7 km uphill walk, the
second a predominately uphill 5.1 km trip on a conventional bicycle, an e-bike
with a standard power settings and with a high power setting. The walking and
e-bike (high power setting) resulted in average METs of 6.5 and 6.1, respectively
(no significant difference). The e-bike using the standard power setting and the
traditional bike resulted in an average MET of 7.3 and 8.2, respectively. These
results led the authors to conclude that e-bikes are effective in providing health-
enhancing physical activity in a topographically challenging environment.
Similarly, in Sperlich, Zinner, Hebert-Losier, Born, and Holmberg (2012), eight
sedentary females were required to cycle at their own pace along a 9.5 km
route, once on an e-bike and again on a conventional bike (the order was random-
ised). Measures of physical exertion were lower when using an e-bike compared
to a traditional bike, but the level of enjoyment and speed was higher. Despite the
lower levels of physical activity recorded by participants on e-bikes, the energy
expenditure was found to be within the range necessary for health enhancement.
de Geus, Kempenaers, Lataire, and Meeusen (2013) found positive physiological
changes in 20 people following a 6-week period of e-bike use.
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Finally, Langford (2013) investigated the 19 users of a bicycle and electric bike-
sharing system in Knoxville, Tennessee, USA, on a fixed 4.4 km hilly course using
laboratory, GPS, and onboard power metres to measure physical exertion. This
research found that energy expenditure per unit time for e-bike trips is 11% less
than that for regular bicycle trips and 8% more than for walking trips. Average
cruising speed for the three modes was 5.1 km/h for walking, 14.4 km/h for
bicycle, and 16.4 km/h for e-bike. Walking trips, while requiring less energy per
unit time, take longer to complete and, in this case, require a greater amount of
total energy from the user, consistent with other active transport research
(Fishman, Bo
¨cker, & Helbich, 2015). Considering the performance advantages of
e-bikes over the course of the trips studied, the total energy demanded for
e-bike trips was 21% less than required for regular bicycles trips and 62% less
than for walking trips.
Overall, the clear theme emerging from research on e-bikes and physical
activity is that they provide a lower level of physical activity than traditional
bikes, but still achieve a level necessary for health enhancement. Moreover,
there appears to be some added enjoyment experienced by e-bike users, although
these are in experimental conditions, and it is not clear whether enjoyment levels
are sustained when using an e-bike on a more consistent basis (i.e. without the
novelty factor).
8. Environmental Impacts of E-bikes
The environmental impacts of e-bikes are dependent to a large degree on the mode
they replace (Cherry & Cervero, 2007). E-bikes that replace fully non-motorised
modes (i.e. walking or bicycle) result in net negative impact on the environment.
However, e-bikes are generally very energy efficient because of their light weight
and electric drive technology, with most e-bikes consuming less than 2 kWh/
100 km, about one-tenth the energy consumption of a small electric car (Ji et al.,
2012) and around 40 less
carbon dioxide (from power plants) than a standard
car travelling the same distance (Ji et al., 2012).
In perhaps the most comprehensive study of its type, Cherry, Weinert, and
Xinmiao (2009) assessed the environmental impact of e-bikes in China and com-
pared them with other, competing modes of transport, including environmental
costs associated with vehicle production. The results indicate that e-bikes offer a
considerable environmental improvement (in terms of most emissions) compared
to car use and similar emissions intensity, on a per passenger kilometre basis, to
that of bus travel. Moreover, the source of emissions is usually far from population
centres, relative to conventional vehicles, so health impacts from conventional pol-
lution are even lower than the emission factors suggest. In China, the health effects
of emissions from power plants are lower by a factor of five compared to equival-
ent tailpipe emissions (Ji et al., 2012). Cherry et al. (2009) conclude that environ-
mental gains occur where e-bikes are used as a replacement for motorised
Even where the power sector has among the highest emission factors (e.g. China
and Australia), emissions of CO
and other conventional pollution from e-bikes
are relatively low. Other countries, where e-bikes are gaining popularity (e.g.
the Netherlands and Germany), have power sector emission factors that are
approximately half those of China and Australia (International Energy Agency,
2012), further reducing emission rates of e-bikes. In summary, the emissions of
E-bikes in the Mainstream 11
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e-bikes are inconsequential and likely better than the set of alternative modes,
even in large numbers and if the power sector is dominated by coal (e.g. China).
It is estimated that 95% of e-bikes in China (mostly ‘e-scooters’) use lead-acid
batteries, though other batteries have been entering the market in recent years
(Jamerson & Benjamin, 2013). E-bikes have been a large driver of the increase in
lead consumption in China (Van der Kuijp, Huang, & Cherry, 2013) and associated
battery manufacturing, recycling, and disposal processes found to be a major
source of environmental contamination. Lead poisoning is associated with a
range of adverse impacts on humans including developmental disorders, lower-
ing of IQ, and reduced life expectancy (Sanders, Liu, Buchner, & Tchounwou,
2009). Lithium Ion (Li-ion) batteries can increase vehicle and environmental per-
formance (Rose, 2012; Weinert, Burke, & Wei, 2007) and there is a general trend
towards Li-ion batteries. Improving the environmental efficiency of the lead
industry and transitioning to different battery technologies will improve the chal-
lenges with battery-source pollution.
9. E-bikes and Safety
Safety concerns are perhaps the greatest driver of e-bike regulation globally. An
increasing body of research in recent years focused on the safety issues related
to e-bikes. Much of this work has taken place in China, where e-bike numbers
are high enough to identify safety trends. China-oriented safety studies generally
cover both SSEBs and BSEBs. Safety studies outside Asia almost exclusively cover
9.1. Perceived Safety and Behaviour
There is some evidence that e-bikes change the perception of safety, compared to
riding traditional bikes. In a North American survey of e-bike owners 60% feel
safer riding an e-bike and 42% said the e-bike had assisted in avoiding crashes.
The reasons given to explain this apparent effect ranged from increased accelera-
tion to clear an intersection, keeping up with traffic, and improved balance at
higher speeds (MacArthur et al., 2014). A similar perception-oriented analysis
in China found that women feel safer traversing intersections on e-bikes but
that there were reservations about increases in e-bike speed in mixed use
bicycle lanes (Weinert, Ma, Yang, et al., 2007). Another study found that only
about half of e-bike riders thought that riding an e-bike was safer than riding a
bicycle (Lin, He, Tan, & He, 2008). A more recent study by Yao and Wu (2012)
investigated user behaviour in relation to crash history and found that e-bike
riders who have been involved in at-fault crashes generally have lower safety atti-
tudes and risk perception, and are therefore more likely to engage in aberrant be-
haviour, which includes making errors, impulsive and aggressive behaviour, and
rule violation.
In the USA, the improved performance of e-bikes prompts e-bike riders to state
that they are more likely to obey traffic rules (e.g. stopping at stop signs) compared
to a traditional bicycle (Popovich et al., 2014). However Langford, Chen, and
Cherry (2015), using GPS equipped e-bikes and bicycles in a bikesharing
system, found that e-bike and bicycle riders behave very similarly at traffic
control devices, violating at about equal rates.
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9.2. Empirical Analyses of E-bike Safety Behaviour in China
There are few opportunities for extensive e-bike crash analysis outside of China.
Within China, most safety research has focused on two key approaches to analysis
(1) conflict and aberrant behaviour, generally at intersections using video-based
observations and (2) crash and injury data from hospital and crash records.
The main articles that investigate e-bike-related behaviour at intersections gen-
erally arrive at the same conclusion; intersections have high levels of aberrant
behaviour among all road users and e-bike riders tend to be worse than other
road users. One of the first studies (Wu, Yao, & Zhang, 2012) that investigated
intersection behaviour found that red light running among cyclists and e-bike
riders in China was very high (56%) and that many demographic and situational
factors contributed to red light running. Across all categories, e-bike riders ran red
lights at a higher rate (63%) than bicyclists (50%) but controlling for confounding
variables, the authors could not find a statistical difference between e-bike and
bicycle red light running behaviour that could be owed to the vehicle type
itself. They suggest several possible reasons for this, including the general accep-
tance of red light running, little legal differentiation between bicycles and e-bikes,
and a small sample that simply could not statistically account for the differences.
A similar study by the same authors (Zhang & Wu, 2013) investigated the effect of
installed sun shades on red light running (i.e. the hypothesis being that riders are
more likely to stop to wait under the shades). This time, with a larger sample, they
found that e-bike riders were 1.8 times more likely to run the red light than bicy-
clists, controlling for other variables. One possible explanation is that e-bike per-
formance characteristics entice riders to take more risks at intersections. Beyond
simply running red lights immediately, e-bike riders also wait for a shorter dur-
ation at a red signal phase before ultimately running the red light, controlling
for other variables (Yang, Huan, Abdel-Aty, Peng, & Gao, 2015).
Du et al. (2013b) observed over 18 000 e-bike riders at multiple intersections in
Suzhou with the aim of identifying unsafe riding behaviour. They focus on a host
of unsafe riding behaviour (even if not prohibited by regulation) that include
talking on the phone, helmet use, wrong-way riding, riding outside the bicycle
lane, and red light running. The authors found a relatively high rate of risky be-
haviour, with one in four infringing on intersection regulations, although the
red light running rate was only 5% and consistent with studies of general
bicycle riders (Johnson Newstead, Charlton, & Oxley, 2011). It is unclear
however if this rate is of all e-bike riders or only those facing red lights.
Bai, Liu, Chen, Zhang, and Wang (2013) focused on video conflict analysis at inter-
sections. This study is the first that acknowledges high amounts of rule-breaking be-
haviour (inputs) of all road users and focuses on outcomes (i.e. near-misses). The
authors categorised risky behaviour, similar to earlier studies, and found that
about 7% of e-bike riders violated red lights, slightly more than bicyclists. Categor-
ising 16 types of conflicting movements, they found that automobile drivers failing
to yield are responsible for over three-quarters of the conflicts. E-bike and bicycle
riders are about evenly responsible for the other quarter of the conflicts. However,
the conflict rates are higher with e-bikes than bicyclists, regardless of fault.
E-bikes in China share bicycle infrastructure, resulting in some concerns regard-
ing differential speed performance. Three studies investigated speed and all
found e-bikes cruising speeds were 4050% faster than bicycles despite share
facilities (Cherry & He, 2010; Lin et al., 2008; Yang et al., 2014). This speed
E-bikes in the Mainstream 13
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differential is a safety concern among regulators, though there is little empirical
evidence of added risk.
9.3. Empirical Analysis of E-bike Crash Data in China
Crash data in China are subject to concerns over reliability, yet there have been few
recent studies using crash data from police records. Feng et al. (2010) examining
police crash data from 2004 to 2008 found the rapid rise in e-bike use had resulted
in a corresponding increase in injury burden. The rates of mortality and injury per
100 000 population increased by 6.5 times and 3.7 times, respectively between 2004
and 2008. However, they found that both injuries and fatalities per 100 000 regis-
tered e-bikes decreased slightly over the same period. The researchers found
overall declining casualty rates across other modes. The authors recommend
that current regulations need improvement and that better enforcement is
required to boost safety levels.
A more recent study focused on over 500 non-fatal hospitalisations over seven
months in rural Suzhou (Du et al., 2013a). During this period, e-bike riders
accounted for about 25% of all hospitalised injuries, which is more than half of
all injuries from road crashes. About half of all e-bike rider injuries were the
result of a collision with a motor vehicle and 46% of all riders suffered head inju-
ries, resulting in the policy recommendation to encourage or mandate helmet use.
The authors suggest that the current increase in e-bike injuries could be replacing
relatively low bicycle injury rates. Hu, Lv, Zhu, and Fang (2014) follow a similar
approach to earlier studies, focusing on hospitalisation records of 205 injured
bicycle and e-bike riders (including six deaths) in Hefei between 2009 and 2011.
One-third of e-bike riders suffered severe injuries, while only 17% of bicyclists suf-
fered severe injuries. Nearly two-thirds of the hospitalisations resulted from a vio-
lation of traffic rules. Being struck by a large motor vehicle increases the odds of
severe injury by 2.5, compared to small motor vehicles.
One of the major gaps in the safety literature in China is the lack of research
attributing fault or causal crash analysis on rising e-bike casualty burden.
E-bike riders fall into a class of vulnerable road users that have characteristics
(e.g., speed) that likely increase exposure and risk relative to bicyclists, but
whose riders are largely injured by heavy vehicles.
9.4. Empirical Analysis of E-bike Crash Data in Europe
E-bikes are beginning to reach sufficient levels of market penetration to observe
crashes in some European countries. Two recent studies focus on investigating
crash or hospitalisation data. Papoutsi, Martinolli, Braun, and Exadaktylos
(2014) investigated hospitalisation data for e-bike riders in Switzerland. The
authors investigated 23 crashes that were reported to the emergency department
(ED). Just over one-quarter of the reported crashes results in head injuries, with
upper extremities being the second highest injured region. Interestingly, most of
the crashes reported were as a result of being caught in a tram rail, not a result
of motor vehicle collision. The authors found that crashes tend to be less severe
in Switzerland than China, in part due to wider use of helmets and the relatively
low number of e-bike crashes involving motor vehicles.
Schepers, Fishman, den Hertog, Wolt, and Schwab (2014) compared the safety
outcomes of e-bike and bicycle use in the Netherlands, using data from ED, as
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Table 4. Major themes and findings
E-bike theme Major findings in the literature
Categories, design, &
E-bike style varies widely, from those that largely resemble standard
bicycles to those that appear more like scooters, without functional pedals
(often added for regulatory rather than propulsive purposes). In Europe,
North America, and Australia, e-bikes generally share more similarities to
regular bicycles, whereas in Asia, they more often resemble a scooter.
Interpreting research in these geographic areas must be bounded in the
context of e-bike type, which is often difficult to distinguish
Demographics of users A number of studies (in China, North America, andAustralia) have foundthat
e-bike users report higher income and educational attainment than regular
cyclists and in some cases, the general population. E-bike users in Western
countries tend to be older than regular bicycle riders. A common weakness
in research on the demographics of e-bike users is self-selection bias
Sales It is estimated 31 million e-bikes were sold in 2012, with about 90% of these
coming from Chinese consumers. E-bikes are a fast-growing category
within the bike industry. The Dutch purchase more e-bikes per capita
than any other European country. Many of the countries with high e-bike
sales (on a per capita basis) also have high rates of general cycling. E-bike
sales in Europe are experiencing a stronger growth trend than regular
bike sales, and now account for up to one in six of all bike sales
A major gap exists concerning reliable e-bikes sales data from North
America and Australia
User motivations and
The ability to maintain speed with less effort was found to be the central
motivation for e-bike use, especially in hilly and hot conditions.
Additionally, experienced, older riders who now find a regular bike too
physically demanding are drawn to e-bikes. Examination of Chinese and
North American e-bike users shows they often have longer travel
distances than regular bike users, although further research is required to
determine whether the longer travel distance was stimulated by e-bike
availability or if e-bike users choose e-bikes because their trip distances
are longer
Impacts on transport
E-bike users appear to ride more frequently and further, although self-
selection bias makes it difficult to determine the degree to which this is
representative of the e-bike user population in general. Research in China,
North America, and Australia shows that e-bikes have a greater capacity
to replace car use than standard bicycles. The precise degree to which car
use is reduced however is currently unknown and remains a pertinent
topic for future research
Physical activity impact Studies on the physical intensity of e-bike use consistently found subjects
met the requirements to reduce sedentary lifestyle disease (between 3 and
6 METs). Given that e-bike users appear to ride more frequently and for
greater duration than regular bicycle riders, e-bikes may contribute to
boosting overall physical activity levels, although more research is
required, especially using randomised controlled trials over an extended
period (e.g. six months or more)
Environmental impact When used to replace motorised transport, e-bikes offer a clear
environmental benefit. E-bike travel emits an order of magnitude less
carbon dioxide (from power plants) than a car travelling the same
distance and emit fewer emissions than buses, on per person kilometre
Many of the e-bikes in China rely on lead-acid batteries yielding significant
increases in lead consumption and environmental pollution. The trend
towards Lithium batteries promises both performance and
environmental benefits over lead-acid batteries
E-bikes in the Mainstream 15
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well as surveys of cyclists without any known crash experience. In total, 294 e-bike
and 1699 bicycle crash victims were included in the study, as well as 791 e-bike users
and 517 bicycle riders without any known crash involvement (control group). The
authors conclude that after controlling for age, gender, and the amount of cycling,
e-bike use is associated with a fairly small increase in risk of ED treatment due to a
crash, but that for those treated at an ED, e-bike users are no more likely than bicycle
riders to be admitted to hospital (i.e. crashes are equally severe). Females were less
likely to be treated at an ED, although there is some uncertainty about the accuracy
of self-reporting between the sexes that make gender comparison problematic.
Those aged 50 56 years were less likely to require ED treatment than those aged
1649 years. The age group with the highest likelihood of ED treatment included
those 65 years and older. Overall differences in safety outcomes were not dramatic
between e-bike and bicycle riders. Finally, the authors note that e-bikes may trigger
modal shift and this may have wider impacts on transport safety generally, particu-
larly if the shift substitutes motor vehicles.
10. Conclusion
Several themes emerged from this review of the e-bike literature. E-bike use has
grown dramatically over the past decade and there is little evidence to suggest
this growth will slow in the coming decade, as market penetration is low in
most countries. Table 4 provides a synthesis of the key findings of this review.
Despite the growth in e-bike research over recent years, several important gaps
in knowledge are apparent. Government agencies have generally not yet inte-
grated e-bikes as a travel mode option as part of travel surveys, hospital admis-
sions, and police crash databases. As e-bike use continues to grow, it will
become more important to offer ‘e-bike’ as an option on standardised forms
related to transport, providing much needed data on the level of e-bike use at
Table 4. Continued
E-bike theme Major findings in the literature
Road safety Most Chinese and North American research found e-bike users have higher
levels of perceived safety. However current evidence suggests e-bike users
are exposed to greater risks than regular bicycles, though the precise
nature and magnitude of this effect is largely unknown and likely
depends on the type (i.e. performance) of the e-bike, among other factors
Chinese studies of e-bike users at intersections have found higher levels of
aberrant behaviour. E-bike crash risk in China appears to be higher than
for regular bicycles and this is broadly consistent with the findings of
recent Dutch research
Future research Government agencies should consider creating e-bikes as a separate option
on travel surveys and hospital admission forms. This will help develop an
important data source, in order to improve the knowledge base on usage
and safety. More research is required on the influence of e-bikes on travel
behaviour, enabling further understanding of the impacts e-bikes may
have on health, congestion, emissions, and safety. More research is
needed to accurately assess vehicle use and mode substitution of e-bike
users, likely moving away from static surveys to more naturalistic
assessment approaches
16 E. Fishman and C. Cherry
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the population level. This will help address the low sample size associated with
many of the current e-bike studies. Including e-bike questions on population
surveys will also serve to enhance understanding of any demographic difference
between e-bike owners and the general population.
A number of sub-topics could not be covered in this review and these include
e-bike regulation, technological issues, bikeshare integration, and freight/cargo
e-bike potential. Moreover, research on the effectiveness of government policy
promoting e-bike use was not included, nor was research examining safety
issues between e-bike users and pedestrians on shared paths.
In order to better understand the impact of e-bikes, comprehensive studies are
needed to quantify the influence of e-bikes on travel behaviour. E-bike research
can benefit from the movement towards more naturalistic data collection tech-
niques that are occurring throughout the transport field, relying on better technol-
ogy to gather more detailed use information. This will help provide the necessary
details to model the impact of current and future e-bike use on health, climate
change, local air and noise pollution, congestion, transport costs, and safety.
Importantly, safety issues associated with e-bike use need to include the impact
on all road users, rather than just injury burden on e-bike riders themselves.
Disclosure statement
No potential conflict of interest was reported by the authors.
1. A physiological measure expressing the energy cost of physical activities and defined as the ratio of
metabolic rate (and therefore the rate of energy consumption) during a specific physical activity to a
reference metabolic rate, set by convention to 3.5 ml O
2. This will vary according to electricity generation factors and vehicle type.
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... However, environmental factors including traffic conditions or inadequate safe cycling lanes are among the barriers that limit cycling on roads. Netherlands and Denmark show the highest eBikes sales in Europe, which has become one of the factors leading to developed cycling infrastructure to overcome safety barriers on road (Fishman and Cherry 2016). Wolf and Seebauer (2014) highlighted that the usage of eBikes could be encouraged by developing infrastructure, such as proper bike paths, policies of reducing speed limits for cars, or restricting car traffic in the centre of cities. Rich et al. (2021) also suggested that improved and connected infrastructure in the form of cycle superhighways, which are developed beyond city borders and specifically focus on commuters, can stimulate potential substitution impact among transport options as it is easier to travel by bike. ...
... This attribute is important, considering that the tropical weather in Malaysia is humid and hot all year round. Next, H 5 could be supported by previous studies (Rich et al. 2021;Fishman and Cherry 2016) as eBikes infrastructure such as the safe and connected cycling lanes, which are able to stimulate the self-efficacy of Malaysian youth to commute. To illustrate this point, they have higher autonomy while commuting with eBikes due to the easy access around the city and the ability to avoid heavy traffic and parking issues. ...
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This study examined (1) the effect of environmental concern, environmental knowledge, and health consciousness on attitude towards eBikes; (2) the effect of eBikes attributes of interest and infrastructure on perceived behavioural control; and finally, (3) the effect of attitude towards eBikes, subjective norms, and perceived behavioural control on eBikes commuting intention and its usage among Malaysian youth. This study adopted a cross-sectional design and convenience sampling, and collected quantitative data from 699 Malaysian youth through an online survey. Findings revealed that (1) environmental concern, environmental knowledge, and health consciousness had a positive and significant impact on attitude towards eBikes; (2) eBikes attributes of interest and infrastructure were positively and significantly related to perceived behavioural control; (3) attitude towards eBikes, subjective norms, and perceived behavioural control had a positive and significant effect on eBikes commuting intention; (4) eBikes commuting intention was positively and significantly related to the usage of eBikes; (5) eBikes commuting intention significantly mediated the relationship between attitude towards eBikes and perceived behav- ioural control on the usage of eBikes among Malaysian youth; and (6) eBikes commuting intention did not mediate the relationship between social norm and usage of eBikes. Although environmental knowledge and social norm are proven to have a positive and significant relationship, this analysis demonstrated a relatively low effect size. To promote environmental and sustainable development in cities through the mass adoption of eBikes among Malaysian youth, policymakers should highlight the benefits of using eBikes, introduce proper policies, and involve the development of improved and connected cycling paths in the city sustainable infrastructure plans.
... Regarding transport systems that can lead the change towards more sustainable mobility patterns in peripheral areas, beyond the use of electric bikes with long, dedicated motorways currently being implemented in some countries such as Netherlands, the UK and Germany (Fishman and Cherry, 2016;Kroesen, 2017), it is clear that, considering both capacity and speed, commuter railways must play a predominant role in this transformation (Papa and Bertolini, 2015). In general, the actions planned for commuter rail systems are oriented to their expansion and upgrading, especially to connect peripheral areas to the main city, so they become a more efficient transport mode that can compete with private vehicles. ...
After decades of sprawl and car-dependent urban developments, especially on the peripheries of metropolitan areas, new policies are being oriented towards more efficient and sustainable mobility. In the process of change towards more sustainable mobility patterns in peripheral areas, commuter railways must play a predominant role because of their capacity and speed. The main aim of this paper is to analyse commuter rail stations’ catchment areas (SCAs) to identify typologies of these nodes in large metropolitan areas as a key step in proposing strategies for making mobility much more sustainable, both in the medium and proximity scales, by promoting soft mobility towards stations, a strategy that will facilitate commuters’ use of daily rail transport. The method proposed is based on a twofold clustering analysis. The first is to consider urban-planning variables to detect consolidated stations; the second is to use both public space and land-use variables to characterise the quality of the urban environment for walking/cycling. These methods are applied in the metropolitan area of Madrid and offer some key insights. The results show that different typologies are found – from consolidated, dense and mixed-use SCAs, with large residential developments, to low-density SCAs in sprawl areas or low consolidated mixed-use SCAs with large parking spaces, following the park-and-ride model, among others. This identification of SCAs’ typologies is key for policy makers to propose different strategies, which could be small projects oriented towards improving public spaces promoting soft mobility, or deeper changes that require a re-densification process in the stations’ surroundings, breaking with the rigid definition of transit-oriented developments and adapting the decisions taken to each context.
... Governments in many countries, such as Australia and China, have formulated policies encouraging cycling to promote environmentally-friendly traffic systems (Yang et al., 2015a;Nakanishi and Black, 2015). Although cycling has many advantages, two-wheelers have also caused numerous traffic safety issues (Fishman and Cherry, 2016). In 2021, over 35,000 nonmotorized accidents occurred in China, and 4,525 non-motorized riders died in accidents (CSY, 2022). ...
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This paper aims to empirically evaluate the ordered and unordered discrete outcome frameworks to approach riders' red-light running (RLR) decisions and compare the differences in influencing factors between riders' risk-taking and opportunistic RLR behaviors. A total of 2057 cyclist samples approaching the intersections during red signals were observed by video in Beijing, China. To better capture the unobserved heterogeneity, apart from the traditional models, three advanced models including the random thresholds random parameters hierarchical ordered logit (RTRPHOL) model, the random parameters logit model with heterogeneity in means and variances (RPLHMV) model, and the correlated random parameters logit model with heterogeneity in means (CRPLHM), are developed. Results show that: 1) the unordered framework statistically outperformed its ordered counterparts, and the RPLHMV and CRPLHM models are statistically better than others. 2) The female and e-bicycle indicators produce a heterogeneity-in-means effect, and the low-volume and left-side indicators produce a heterogeneity-in-variances effect. 3) e-bike riders and riders from the right side are more inclined to have risk-taking behavior than opportunistic behavior, and both RLR behaviors of cyclists are most susceptible to the number of violating individual indicator. Findings illustrate that multilayer unobserved heterogeneity should be adequately considered in developing precise micro-simulation and practical guidance in traffic safety.
... Electric automobiles and hybrid electric vehicles are the ideal answer for reducing environmental impact [1]. Electric bicycles (e-bikes) for commuting in urban areas are a viable solution to the major problems of pollution, traffic, and road congestion due to their advantageous properties such as low cost, convenience of use, and tiny footprint [2]. The battery is the most suited energy storage and power supply component for use in high-power EV applications. ...
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Batteries are one of the most important components in electric vehicles. Batteries are used as an energy source for the entire electrical system and as a place to store electrical energy during charging process. The battery functions to supply electric current in order to operate the electric machine. The lithium-ion battery is a type of secondary battery (rechargeable battery) that can be recharged and is an environmentally friendly battery. This battery has excellent energy storage stability and higher energy density compared to other types of secondary batteries, making this type of battery increasingly attractive for use in electric vehicles. This study aims to design the size of an electric bicycle battery according to its specifications. In addition, an analysis of the calculation of the capacity of the battery to be used and the time for the recharging process is also carried out.
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China leads the world in both public bikeshare and private electric bike (e-bike) growth. Current trajectories indicate the viability of deploying large-scale shared e-bike (e-bikeshare) systems in China. We employ a stated preference survey and multinomial logit to model the factors influencing the choice to switch from an existing transportation mode to bikeshare or e-bikeshare in Beijing. Demand is influenced by distinct sets of factors: the bikeshare choice is most sensitive to measures of effort and comfort while the e-bikeshare choice is more sensitive to user heterogeneities. Bikeshare demand is strongly negatively impacted by trip distance, temperature, precipitation, and poor air quality. User demographics however do not factor strongly on the bikeshare choice, indicating the mode will draw users from across the social spectrum. The e-bikeshare choice is much more tolerant of trip distance, high temperatures and poor air quality, though precipitation is also a highly negative factor. User demographics do play a significant role in e-bikeshare demand. Analysis of impact to the existing transportation system finds that both bikeshare and e-bikeshare will tend to draw users away from the “unsheltered modes”, walk, bike, and e-bike. Although it is unclear if shared bikes are an attractive “first-and-last-mile solution”, it is clear that e-bikeshare is attractive as a bus replacement.
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The rapid adoption of electric bikes (e-bikes) (~150 million in 10 years) has come with debate over their role in China's urban transportation system. While there has been some research quantifying impacts of e-bikes on the transportation system, there has been little work tracking e-bike use patterns over time. This paper investigates e-bike use over a 6-year period. Four bi-annual travel diary surveys of e-bike users were conducted between 2006 and 2012 in Kunming, China. Choice models were developed to investigate factors influencing mode-transition and motorization pathways. As expected, income and vehicle ownership strongly influence car-based transitions. Younger and female respondents were more likely to choose car-based modes. Systematic and unobserved changes over time (time-dynamics) favor car-based modes, with the exception of previous car users who already shifted away from cars being less likely to revert to cars over time. E-bikes act as an intermediate mode, interrupting the transition from bicycle to bus and from bus to car. Over 6 years, e-bikes are displacing prospective bus (65→55%), car/taxi (15→24%) and bicycle (19→7%) trips. Over 40% of e-bike riders now have household car access so e-bikes are effectively replacing many urban car trips.
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Introduction: Modern, urban lifestyles have engineered physical activity out of everyday life and this presents a major threat to human health. The Netherlands is a world leader in active travel, particularly cycling, but little research has sought to quantify the cumulative amount of physical activity through everyday walking and cycling. Methods: Using data collected as part of the Dutch National Travel Survey (2010 - 2012), this paper determines the degree to which Dutch walking and cycling contributes to meeting minimum level of physical activity of 150 minutes of moderate intensity aerobic activity throughout the week. The sample includes 74,465 individuals who recorded at least some travel on the day surveyed. As physical activity benefits are cumulative, all walking and cycling trips are analysed, including those to and from public transport. These trips are then converted into an established measure of physical activity intensity, known as metabolic equivalents of tasks. Multivariate Tobit regression models were performed on a range of socio-demographic, transport resources, urban form and meteorological characteristics. Results: The results reveal that Dutch men and women participate in 24 and 28 minutes of daily physical activity through walking and cycling, which is 41% and 55% more than the minimum recommended level. It should be noted however that some 57% of the entire sample failed to record any walking or cycling, and an investigation of this particular group serves as an important topic of future research. Active transport was positively related with age, income, bicycle ownership, urban density and air temperature. Car ownership had a strong negative relationship with physically active travel. Conclusion: The results of this analysis demonstrate the significance of active transport to counter the emerging issue of sedentary lifestyle disease. The Dutch experience provides other countries with a highly relevant case study in the creation of environments and cultures that support healthy, active living.
Electric bikes have the potential to overcome some of the barriers that prevent Australians from riding a conventional pedal bike. Electric bikes offer assistance to overcome hilly terrain or a lack of fitness and they can assist in rehabilitation after injury or illness. While electric bike sales are increasing, they remain relatively uncommon and little is known about electric bikes in Australia. In this study, we identified characteristics of electric bike owners in Australia and explored the process and motivations of electric bike purchase. An online survey was conducted of electric bike owners in Australia (n=529). In this paper, we analysed the demographic characteristics of electric bike owners and factors underlying the decision to purchase their electric bicycle. Particular attention is paid to the gender distribution of electric bicycle owners along with the reasons for purchase and the types of electric bikes purchased. Electric bikes are a potentially important component in the mix of transport mode options. Accessible to a greater proportion of the community than pedal bicycles, electric bikes could enable more Australians to shift from cars and public transport for personal mobility. Such a shift will have direct benefits in relieving traffic congestion, easing the burden on the public transport system and offering independent mobility options. This study provides the first insights into this growing segment of transportation in Australia.
As electric bicycles (e-bikes) have emerged as a new transportation mode, their role in transportation systems and their impact on users have become important issues for policy makers and engineers. Little safety-related research has been conducted in North America or Europe because of their relatively small numbers. This work describes the results of a naturalistic GPS-based safety study between regular bicycle (i.e., standard bicycle) and e-bike riders in the context of a unique bikesharing system that allows comparisons between instrumented bike technologies. We focus on rider safety behavior under four situations: (1) riding in the correct direction on directional roadway segments, (2) speed on on-road and shared use paths, (3) stopping behavior at stop-controlled intersections, and (4) stopping behavior at signalized intersections. We find that, with few exceptions, riders of e-bike behave very similarly to riders of bicycles. Violation rates were very high for both vehicles. Riders of regular bicycles and e-bikes both ride wrong-way on 45% and 44% of segments, respectively. We find that average on-road speeds of e-bike riders (13.3kph) were higher than regular bicyclists (10.4kph) but shared use path (greenway) speeds of e-bike riders (11.0kph) were lower than regular bicyclists (12.6kph); both significantly different at >95% confidence. At stop control intersections, both bicycle and e-bike riders violate the stop signs at the similar rate with bicycles violating stop signs at a slightly higher rate at low speed thresholds (∼80% violations at 6kph, 40% violations at 11kph). Bicycles and e-bikes violate traffic signals at similar rates (70% violation rate). These findings suggest that, among the same population of users, e-bike riders exhibit nearly identical safety behavior as regular bike riders and should be regulated in similar ways. Users of both technologies have very high violation rates of traffic control devices and interventions should occur to improve compliance. Copyright © 2015. Published by Elsevier Ltd.
The research described in this paper was conducted in part to understand whether different bicycling technology—in this case, electric-assist bicycles (e-bikes)—could reduce barriers to bicycling such as trip distance, topography, time, and rider effort. If so, this technology may result in more bike trips and longer bike trips and may increase the diversity of people bicycling, including people with disabilities or chronic injuries. An e-bike typically resembles a standard pedal bicycle with the addition of a rechargeable battery and electric motor to assist with propulsion. To address these aims, an online survey was conducted of existing e-bike users, who were surveyed about their purchase and use decisions. Responses from 553 e-bike users across North America were analyzed. Results suggest that e-bikes enable users to bike more often, to travel longer distances, and to carry more cargo with them. Additionally, e-bikes allow people who otherwise would not be able to bike (because of physical limitation...
The use of electric bikes (e-bikes) in China has grown tremendously in the past decade. Traffic safety for e-bike riders is an issue of growing public concern because the number of fatalities and injuries is increasing. A study was conducted to identify risk factors affecting involvement of e-bike riders in accidents and to establish the relationships between safety attitudes, risk perception, and aberrant riding behaviors. The data used for analysis were obtained from a self-reported questionnaire survey of a sample of 603 e-bike riders in two large cities in China. The results showed that both gender and automobile driving experience were significantly associated with at-fault accident involvement. Males were more likely to have at-fault accidents than were females, and riders with an automobile driver's license were less likely to have accidents than were those without a driver's license. Two types of aberrant riding behaviors, errors and aggressive behaviors, were found to be significant factors for predicting at-fault accident involvement. Analysis with a structural equation model indicated that safety attitudes and risk perception both significantly affected aberrant riding behaviors. E-bike riders with stronger positive attitudes toward safety and more worry and concern about their traffic risk tended to be less likely to have aberrant riding behaviors. Practical implications for improving road safety of e-bike riders are discussed.
Introduction Modern, urban lifestyles have engineered physical activity out of everyday life and this presents a major threat to human health. The Netherlands is a world leader in active travel, particularly cycling, but little research has sought to quantify the cumulative amount of physical activity through everyday walking and cycling. Methods Using data collected as part of the Dutch National Travel Survey (2010 – 2012), this paper determines the degree to which Dutch walking and cycling contributes to meeting minimum level of physical activity of 150 minutes of moderate intensity aerobic activity throughout the week. The sample includes 74,465 individuals who recorded at least some travel on the day surveyed. As physical activity benefits are cumulative, all walking and cycling trips are analysed, including those to and from public transport. These trips are then converted into an established measure of physical a
Introduction Modern, urban lifestyles have engineered physical activity out of everyday life and this presents a major threat to human health. The Netherlands is a world leader in active travel, particularly cycling, but little research has sought to quantify the cumulative amount of physical activity through everyday walking and cycling. Methods Using data collected as part of the Dutch National Travel Survey (2010 – 2012), this paper determines the degree to which Dutch walking and cycling contributes to meeting minimum level of physical activity of 150 minutes of moderate intensity aerobic activity throughout the week. The sample includes 74,465 individuals who recorded at least some travel on the day surveyed. As physical activity benefits are cumulative, all walking and cycling trips are analysed, including those to and from public transport. These trips are then converted into an established measure of physical a