E-bike ownership and usage: an analysis of Germany

Figures & data

Table 1. Classification of e-bikes in Germany.

	Pedelec	S-Pedelec
Motor assistance up to [km/h]	25 while pedaling	45 while pedaling
Regulatory	Bicycle	Motorcycle
Requiring driver’s license and license plate	No	Yes

Table 2. Key findings of the influence of e-bikes on mobility behavior.

Citation	Location of study	Methods	Survey period	Key findings
Bigazzi and Wong (2020)	World-wide	Meta study	2006–2017	E-bikes displace more transit trips in China and more car trips in Europe, North America, and Australia.
Castro et al. (2019)	Europe	Longitudinal survey, travel diaries, sampling strategy across cities	2014–2017	The average cycling trip duration of e-bike owners (35.0 for a c-bike and 41.9 min for an e-bike) is significantly higher than that of c-bike owners (25.6 min). Average trip distance is significantly different when comparing e-bike owners (9.4 km for e-bike trips and 8.4 km for c-bike trips) with c-bike owners (4.8 km for c-bike trips). Average daily cycling time is similar for e-biking among e-bike owners and c-biking among c-bike owners (32.2 vs. 30.3 min), but e-bike owners also cycle 13.4 min on a c-bike. E-bike owners travel significantly longer daily distances by e-bike than c-bike owners travel by c-bike (8.0 vs. 5.3 km per person per day). In addition, e-bike owners travel 2.5 km/d by c-bike. E-bikers tend to substitute their primary mode of transportation.
Cherry et al. (2016)	CN	Travel diaries	2006–2012	Many of those transitioning from non-motorized modes (ie walking and bicycle) to e-bikes would transition from e-bikes to more motorized modes (bus, taxi, car). 24% of e-bike trips would have been made by car (including taxis).
Fyhri and Sundfør (2020)	NOR	Travel diaries, panel	2014	After purchasing an e-bike, the average distance traveled by bicycle increased from 2.1 to 9.2 km, and the mode share by bicycle increased from 17 to 49%. The increase in cycling km is slightly lower in the purchase case than in the experimental loan case, but the mode share is higher.
Fyhri and Fearnley (2015)	NOR	Travel diaries, experimental	2013	Loaning an e-bike to users increases cycling, expressed in the number of trips, distance cycled, and cycling share. The effect is more remarkable for women than for men. The impact on distance cycled is the greatest for non-commuting trips.
Kroesen (2017)	NL	1-day Travel diaries	2013–2015	On average, e-bike owners travel 3.0 km with their e-bike, while non-e-bike owners travel 2.6 km with their c-bike. E-bike owners travel an additional 0.9 km/day by c-bike. E-bike owners travel less by car (−5.8 km) and by public transportation (−9.4 km). They also travel less overall than non-e-bike owners. E-bike ownership reduces c-bike ownership from 81 to 49% but correlates with slightly higher car ownership. Neither the ownership of an e-bike nor a c-bike acts as a car substitute. In terms of mode use, e-bike ownership increases the use of e-bikes and decreases the use of c-bikes, car driving, and public transportation. The signs of the effects of socio-demographic and household variables on e-bike use are opposite to the impact of these variables on e-bike ownership. For example, age increases the probability of owning an e-bike but has a negative effect on e-bike use.
MacArthur et al. (2018)	USA	Online survey	2017	The main reasons for buying an e-bike are: to replace car trips, to ride with less effort, for recreation purposes, because someone lives or works in a hilly area, or to improve fitness. Most e-bike users (93.4%) rode a c-bike before owning an e-bike. The e-bike leads to falling c-bike use but increases total cycling. Exercise, recreation, and commuting are the trip purposes most likely to be made primarily by e-bikes. Older people and those with physical limitations are likelier to use e-bikes for recreational and exercise-based trips. In contrast, younger people and those without physical limitations use the e-bike as their primary mode of commuting. E-bikes also replace many trips that people would have made by active or public transit modes, most of which are commuting (39.8%) and recreation or exercise trips (29.4%).
Plazier et al. (2017)	NL	GPS-based travel diaries and in-depth interviews	2015–2016	E-bike use is highest for work-related, single-destination trips. Participants state that commuting by e-bike gives them the advantages of cycling over motorized transport (enjoyment of outdoor, physical activity; independence) while mitigating its relative disadvantages (longer travel time; increased effort). Results demonstrate that e-bikes can substitute motorized commuting modes for distances perceived as too long to be covered by c-bike, and stress the importance of a positive experience in e-bike commuting.
Sun et al. (2020)	NL	Travel diaries, panel	2013–2016	The results indicate that purchasing an e-bike reduces the use of a c-bike more significantly than driving a car or walking. Looking at the level of cycling by e-bike and c-bike together, the share of cycling almost doubles, while the car remains the main mode of transport despite a 16% drop in mode share. C-bike ownership drops from 81.3% to 43% after purchasing an e-bike, while car ownership drops from 92.5 to 86.9%. E-bikes boost cycling trips mostly for distances between 5 and 20 km, indicating a range extender effect for cycling. For commuting, e-bike trips challenge the dominance of car trips, as the car mode share drops from 76.3% to 50.8%. People living in highly urbanized areas are less likely to reduce their car use after adopting an e-bike than new e-bikers in non-urbanized areas.

Figure 1. Bike ownership on the individual level by the economic status of the household (n = 188,900).

Figure 2. Bike ownership related to age (n = 188,900).

Table 3. Average cycled kilometer related to bike ownership (n = 188,900).

Ownership	Mean daily distance (km)
	Cycled	Driven
C- and e-bike	4.0	32.3
Only e-bike	2.5	28.4
Only c-bike	1.8	37.1
No bike	0.1	25.5
All	1.6	34.3

Table 4. Coefficients multinomial logit model for the bike ownership (Mc-Fadden R² = 0.101).

Reference:	No bike ownership	Only c-bike ownership	Only e-bike ownership	C-bike and e-bike ownership
Intercept		0.208***	−5.603***	−4.207***
Econ. status of the household (ref. = middle)	Very low	−0.113***	−0.853***	−0.564***
	Low	−0.168***	−0.639***	−0.398***
	High	0.441***	0.296***	0.623***
	Very high	0.502***	0.312***	0.808***
Living place (ref. = middle-sized town in an urban region or central city in a rural region)	Metropole	−0.082***	−0.824***	−0.729***
	City	−0.237***	−0.183***	−0.376***
	Provincial area in urban region	0.119***	0.146**	0.23***
	Middle-sized town or provincial area in a rural region	−0.123***	–	–
Gender	Male	0.232***	0.231***	0.26***
Age	Years >16	−0.057***	0.085***	−0.109***
	Years > 25	0.113***	–	0.227***
	Years > 40	−0.081***	−0.047***	−0.093***
	Years > 70	−0.059***	−0.157***	−0.146***
Driver’s license ownership		0.585***	0.628***	0.84***
Car ownership	Every time	0.374***	0.527***	0.515***
	Sometimes	0.35***	0.345***	0.523***
Occupation	School student	0.998***	–	0.966***
	University student	0.998***	–	0.725***
	Employed	0.359***	–	0.252**
	Homemaker	0.176***	0.722***	0.443***
	Retired	0.177***	0.197***	0.335***
Public transportation pass		−0.057***	−0.669***	−0.244***

Significance codes: “***” 0 < Pr(>|z|) <0.001, “**” 0.001 < Pr(>|z|) < 0.01.

Figure 3. Probabilities for bike ownership related to age.

Table 5. Mode share (n = 231,069).

Mode	Share [%]
Walk	25.1
Cycling	13.2
PT	9.1
Car	52.6

Figure 4. Mode choice related to trip chain length for different bike ownership.

Table 6. Coefficients multinomial logit model for mode choice (McFadden R² = 0.318).

Reference:	Car	Walking	Cycling	PT
Intercept		0.542***	–	−3.748***
Gender	Male	–	0.119***	–
Age	Years > 16	0.072***	0.01***	–
	Years > 16 and e-bike ownership	–	0.009***	–
	Years > 30	−0.088***	–	−0.01***
	Years > 50	0.025***	−0.012***	0.032***
Living place (ref. = middle-sized town in an urban region)	Metropolis	0.706***	0.92***	1.334***
	Metropolis and e-bike ownership	–	−0.562***	–
	City	0.367***	0.519***	0.61***
	Provincial area in an urban region or a central city in a rural region	0.122***	0.126***	–
	Middle-sized town or provincial area in a rural region	−0.249***	−0.289***	−0.745***
Public transportation pass		0.371***	0.403***	2.502***
Season (ref. = summer)	Winter	0.082***	−0.814***	0.159***
	Winter and e-bike ownership	–	−0.531***	–
	Spring	–	−0.286***	–
	Spring and e-bike ownership	–	−0.141**	–
	Autumn	–	−0.342***	–
	Autumn and e-bike ownership	–	−0.284***	–
Trip chain purpose (ref. = work and “other trip purposes”)	Education	0.756***	0.64***	0.988***
	Business	–	−0.387***	−0.19***
	Business and e-bike ownership	–	0.919***	–
	Shopping	0.188***	−0.381***	−1.128***
	Private errands	0.615***	−0.238***	−0.574***
	Private errands and e-bike ownership	–	0.272***	–
	Leisure	2.04***	0.61***	−0.123***
	Leisure and e-bike ownership	–	0.221***	–
Trip length	- [km]	−0.78***	−0.286***	0.092***
	- and e-bike ownership [km]	–	0.027***	–
	Over 5 km [km]	0.551***	0.122***	−0.082***
	Over 10 km [km]	0.06***	0.11***	–

Significance codes: “***” 0 < Pr(>|z|) <0.001, “**” 0.001 < Pr(>|z|) < 0.01.

Figure 5. Probabilities for different modes by age for the reference case of a trip chain length of 5 km.

Figure 6. Probabilities for different modes related to trip chain length and e-bike ownership for a 50-year-old reference person.

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