April 10th 2018
User characteristics and trip patterns of e-bike use in the Netherlands Results from the Dutch National Travel Survey and the Mobility Panel Netherlands
Maarten Kroesen
Lucas Harms
TU Delft
KiM Netherlands Institute forTransport Policy Analysis
Ministry of Infrastructureand Watermanagement
12 oktober 2017
The rise of E-bikes
• After China and Japan, adoption of e-bikes in European countries is increasing rapidly.
• In 2015, e-bikes accounted for 6.5% of all bikes sold in the 28 EU member countries.
Bicycle-style (pedal assisted) e-bikes Scooter style e-bikes (with throttle control)
April 10th 2018
Trends in the Netherlands
2016: 12.5% of Dutch population (aged >12) owns an e-bike (1.8 million)
2017: 31% of all bikes sold were e-bikes (294,000)
0
200
400
600
800
1000
1200
1400
1600
Overige fietsen
Elektrische fietsen
*1,000
*1,000,000
Source: OViN 2013-2016
Source: RAI/BOVAG
0
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8
10
12
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16
2013 2014 2015 2016
bezit geen e-fiets
Elektrische fiets bezitters
April 10th 2018
Rise of the e-bike
• Two interrelated questions:
1. Who are the users of e-bikes?
2. Will uptake result in environmental and/or health benefits?
to what extent does the use of the e-bike substitute/complement other modes of travel?
April 10th 2018
1. Who are the users of e-bikes?
• E-bikes allow users to cover greater distances with less effort– Average bicycle trip distance = 3.6 km (OViN 2016)
– Average e-bike trip distance = 5.0 km (OViN 2016)
• Mainly older people and/or people with physical limitations
• In the Netherlands, the ‘elderly image’ inhibited the uptake of the e-bike among younger segments of the population (Hendriksen, 2008)
• But is this still the case?– More generally: which users groups can be identified? And how are they
using the e-bike?
• Answer has implications for the second question
April 10th 2018
Most e-bikers are 65+
Oct 5th, 2017
Share of e-bike trips by ageSource: CBS (OViN 2013/2016 – calculation by KiM)
Mostly used for leisure purposes
Oct 5th, 2017
Share of e-bike trips by trip purposeSource: CBS (OViN 2013/2016 – calculation by KiM)
2. What are the environmental/health benefits of e-bikes?
• Only environmental and health benefits if motorised (car) travel is substituted.
• In principle, e-bike has more potential than normal bike (speed/distance).
• (cross-sectional) empirical evidence indicates that substitution strongly depends on available alternatives:
– China: mainly bus, some cities PT
– US/Australia: mainly car
– Europe: car and bicycle
• Evidence is mainly based on self-reported behavioural changes from cross-sectional convenience samples. Need for:
1. Actual behavioural changes
2. Panel data
3. Representative samples
April 10th 2018
2. What are the environmental/health benefits of e-bikes?
April 10th 2018
Research questions & methods
1. What are the socio-demographic user profiles of e-bike owners?
– For what trip purposes do they use the e-bike?– How have profile shares developed over the past years?
Latent class analysis based on repeated cross-sectional data (OViN 2013-2016)
2. To what extent is e-bike use substituting/complementing other modes of travel?
– How does e-bike ownership affect travel by various modes?– How does the transition to e-bike ownership affect travel by
various modes?– How does e-bike use affect travel by other modes?
Regression models and cross-lagged panel model based on panel data (MPN 2013-2016)
April 10th 2018
Conceptualmodel
Gender
Age
Level of education
Primary occupation
Household composition
Socio-demographic and household characteristics
Socio-demographic user profiles
E-bike use
Bicycle use
Car use
PT use
Indicators
Inactive
covariates
• Latent class model
• A probabilistic clustering technique
• Latent Gold 5.0
Year
Active covariate
April 10th 2018
Data• Dutch mobility survey (OViN)
– Annual national survey with ~40.000 participants representative of the Dutch population
– Includes personal characteristics and 1-day travel diary
• From 2013 onwards: e-bike ownership and use
• Selection of individuals with an e-bike
– 2013: 25,993 3,413 e-bike owners
– 2014: 36,305 4,170 e-bike owners
– 2015: 31,941 4,014 e-bike owners
– 2016: 31,845 4,404 e-bike owners
• 16,001 in total.
April 10th 2018
Gender
Age
Level of education
Primary occupation
Household composition
Socio-demographic and household characteristics
Socio-demographic user profiles
E-bike use
Bicycle use
Car use
PT use
Indicators
Inactive
covariates
Year
Active covariate
April 10th 2018
Modelestimation
• 5-class solution
was optimal
1 2 3 4 5
Overall class size (%) 53.7 21.0 15.3 8.5 1.4
Gender
Male (%) 43 64 4 2 44
Female (%) 57 36 96 98 56
Age
12-20 (%) 0 0 0 0 85
21-30 (%) 0 3 0 8 15
31-40 (%) 0 10 0 16 0
41-50 (%) 0 27 2 30 0
51-64 (%) 5 60 98 45 0
>64 (%) 95 0 0 0 0
Mean 72.2 53.2 58.2 44.5 16.6
Education level
Low (%) 58 29 51 20 77
Intermediate (%) 25 38 36 49 21
High (%) 17 33 14 31 3
Occupational status
Employed 12-30 hours per week (%) 1 6 40 55 2
Employed >= 30 hours per week (%) 1 75 0 15 3
Works in household (%) 0 1 39 18 0
Student (%) 0 0 0 1 91
Unemployed (%) 0 4 4 2 1
Incapacitated (%) 0 11 9 5 0
Retired (%) 98 1 1 0 0
Other (%) 0 3 7 4 3
Household composition
Single without child(ren) (%) 25 18 9 3 8
Couple without child(ren) (%) 73 47 74 6 0
Couple with child(ren) (%) 2 31 15 82 79
Other (%) 1 4 2 9 13
Mode use on the day of the survey (covariates)
E-bike work trip (yes) (%) 1 11 7 11 5
E-bike school trip (yes) (%) 0 0 0 1 11
E-bike maintenance trip (yes) (%) 12 7 14 13 4
E-bike social/recreational trip (yes) (%)
13 8 10 9 8
Car trip (yes) (%) 28 47 34 44 9
Public transport trip (yes) (%) 2 4 2 2 10
Conventional bicycle trip (yes) (%) 11 10 12 11 26
Class 1: retired old-age recreational user
April 10th 2018
1 2 3 4 5
Overall class size (%) 53.7 21.0 15.3 8.5 1.4
Gender
Male (%) 43 64 4 2 44
Female (%) 57 36 96 98 56
Age
12-20 (%) 0 0 0 0 85
21-30 (%) 0 3 0 8 15
31-40 (%) 0 10 0 16 0
41-50 (%) 0 27 2 30 0
51-64 (%) 5 60 98 45 0
>64 (%) 95 0 0 0 0
Mean 72.2 53.2 58.2 44.5 16.6
Education level
Low (%) 58 29 51 20 77
Intermediate (%) 25 38 36 49 21
High (%) 17 33 14 31 3
Occupational status
Employed 12-30 hours per week (%) 1 6 40 55 2
Employed >= 30 hours per week (%) 1 75 0 15 3
Works in household (%) 0 1 39 18 0
Student (%) 0 0 0 1 91
Unemployed (%) 0 4 4 2 1
Incapacitated (%) 0 11 9 5 0
Retired (%) 98 1 1 0 0
Other (%) 0 3 7 4 3
Household composition
Single without child(ren) (%) 25 18 9 3 8
Couple without child(ren) (%) 73 47 74 6 0
Couple with child(ren) (%) 2 31 15 82 79
Other (%) 1 4 2 9 13
Mode use on the day of the survey (covariates)
E-bike work trip (yes) (%) 1 11 7 11 5
E-bike school trip (yes) (%) 0 0 0 1 11
E-bike maintenance trip (yes) (%) 12 7 14 13 4
E-bike social/recreational trip (yes) (%)
13 8 10 9 8
Car trip (yes) (%) 28 47 34 44 9
Public transport trip (yes) (%) 2 4 2 2 10
Conventional bicycle trip (yes) (%) 11 10 12 11 26
Class 1: retired old-age recreational userClass 2: full-time employed middle-aged user
April 10th 2018
1 2 3 4 5
Overall class size (%) 53.7 21.0 15.3 8.5 1.4
Gender
Male (%) 43 64 4 2 44
Female (%) 57 36 96 98 56
Age
12-20 (%) 0 0 0 0 85
21-30 (%) 0 3 0 8 15
31-40 (%) 0 10 0 16 0
41-50 (%) 0 27 2 30 0
51-64 (%) 5 60 98 45 0
>64 (%) 95 0 0 0 0
Mean 72.2 53.2 58.2 44.5 16.6
Education level
Low (%) 58 29 51 20 77
Intermediate (%) 25 38 36 49 21
High (%) 17 33 14 31 3
Occupational status
Employed 12-30 hours per week (%) 1 6 40 55 2
Employed >= 30 hours per week (%) 1 75 0 15 3
Works in household (%) 0 1 39 18 0
Student (%) 0 0 0 1 91
Unemployed (%) 0 4 4 2 1
Incapacitated (%) 0 11 9 5 0
Retired (%) 98 1 1 0 0
Other (%) 0 3 7 4 3
Household composition
Single without child(ren) (%) 25 18 9 3 8
Couple without child(ren) (%) 73 47 74 6 0
Couple with child(ren) (%) 2 31 15 82 79
Other (%) 1 4 2 9 13
Mode use on the day of the survey (covariates)
E-bike work trip (yes) (%) 1 11 7 11 5
E-bike school trip (yes) (%) 0 0 0 1 11
E-bike maintenance trip (yes) (%) 12 7 14 13 4
E-bike social/recreational trip (yes) (%)
13 8 10 9 8
Car trip (yes) (%) 28 47 34 44 9
Public transport trip (yes) (%) 2 4 2 2 10
Conventional bicycle trip (yes) (%) 11 10 12 11 26
Class 1: retired old-age recreational userClass 2: full-time employed middle-aged userClass 3: Older female recreational user
April 10th 2018
1 2 3 4 5
Overall class size (%) 53.7 21.0 15.3 8.5 1.4
Gender
Male (%) 43 64 4 2 44
Female (%) 57 36 96 98 56
Age
12-20 (%) 0 0 0 0 85
21-30 (%) 0 3 0 8 15
31-40 (%) 0 10 0 16 0
41-50 (%) 0 27 2 30 0
51-64 (%) 5 60 98 45 0
>64 (%) 95 0 0 0 0
Mean 72.2 53.2 58.2 44.5 16.6
Education level
Low (%) 58 29 51 20 77
Intermediate (%) 25 38 36 49 21
High (%) 17 33 14 31 3
Occupational status
Employed 12-30 hours per week (%) 1 6 40 55 2
Employed >= 30 hours per week (%) 1 75 0 15 3
Works in household (%) 0 1 39 18 0
Student (%) 0 0 0 1 91
Unemployed (%) 0 4 4 2 1
Incapacitated (%) 0 11 9 5 0
Retired (%) 98 1 1 0 0
Other (%) 0 3 7 4 3
Household composition
Single without child(ren) (%) 25 18 9 3 8
Couple without child(ren) (%) 73 47 74 6 0
Couple with child(ren) (%) 2 31 15 82 79
Other (%) 1 4 2 9 13
Mode use on the day of the survey (covariates)
E-bike work trip (yes) (%) 1 11 7 11 5
E-bike school trip (yes) (%) 0 0 0 1 11
E-bike maintenance trip (yes) (%) 12 7 14 13 4
E-bike social/recreational trip (yes) (%)
13 8 10 9 8
Car trip (yes) (%) 28 47 34 44 9
Public transport trip (yes) (%) 2 4 2 2 10
Conventional bicycle trip (yes) (%) 11 10 12 11 26
Class 1: retired old-age recreational userClass 2: full-time employed middle-aged userClass 3: Older female recreational userClass 4: Younger female non-recreational user
April 10th 2018
1 2 3 4 5
Overall class size (%) 53.7 21.0 15.3 8.5 1.4
Gender
Male (%) 43 64 4 2 44
Female (%) 57 36 96 98 56
Age
12-20 (%) 0 0 0 0 85
21-30 (%) 0 3 0 8 15
31-40 (%) 0 10 0 16 0
41-50 (%) 0 27 2 30 0
51-64 (%) 5 60 98 45 0
>64 (%) 95 0 0 0 0
Mean 72.2 53.2 58.2 44.5 16.6
Education level
Low (%) 58 29 51 20 77
Intermediate (%) 25 38 36 49 21
High (%) 17 33 14 31 3
Occupational status
Employed 12-30 hours per week (%) 1 6 40 55 2
Employed >= 30 hours per week (%) 1 75 0 15 3
Works in household (%) 0 1 39 18 0
Student (%) 0 0 0 1 91
Unemployed (%) 0 4 4 2 1
Incapacitated (%) 0 11 9 5 0
Retired (%) 98 1 1 0 0
Other (%) 0 3 7 4 3
Household composition
Single without child(ren) (%) 25 18 9 3 8
Couple without child(ren) (%) 73 47 74 6 0
Couple with child(ren) (%) 2 31 15 82 79
Other (%) 1 4 2 9 13
Mode use on the day of the survey (covariates)
E-bike work trip (yes) (%) 1 11 7 11 5
E-bike school trip (yes) (%) 0 0 0 1 11
E-bike maintenance trip (yes) (%) 12 7 14 13 4
E-bike social/recreational trip (yes) (%)
13 8 10 9 8
Car trip (yes) (%) 28 47 34 44 9
Public transport trip (yes) (%) 2 4 2 2 10
Conventional bicycle trip (yes) (%) 11 10 12 11 26
Class 1: retired old-age recreational userClass 2: full-time employed middle-aged utilitarian userClass 3: Older female recreational userClass 4: Younger female utilitarian userClass 5: Young studentutilitarian user
April 10th 2018
Weighted class sizes (pop.=17M)
ClassRelativesize
1 2 3 4 5 Total
2013 (%) 56.2 17.4 16.3 8.7 1.5 1,169,678
2014 (%) 53.8 19.8 16.8 8.3 1.3 1,369,210
2015 (%) 49.4 22.5 17.0 9.7 1.5 1,629,749
2016 (%) 49.7 23.9 14.4 10.2 1.8 1,831,556Absolute size
2013 657,680 203,421 190,133 101,274 17,170 1,169,678
2014 736,584 271,220 230,267 113,562 17,577 1,369,210
2015 804,387 366,012 276,385 158,608 24,357 1,629,749
2016 910,318 438,148 262,982 187,204 32,904 1,831,556% growth2013-2016
38.4 115.4 38.3 84.8 91.6 56.6
Class 1: retired old-age recreational userClass 2: full-time employed middle-aged utilitarian userClass 3: Older female recreational userClass 4: Younger female utilitarian userClass 5: Young studentutilitarian user
April 10th 2018
Research questions & methods
1. What are the socio-demographic user profiles of e-bike owners?
– For what trip purposes do they use the e-bike?
– How have profile shares developed over the past years?
Latent class analysis based on repeated cross-sectional data (OViN 2013-2016)
2. To what extent is e-bike use substituting/complementing other modes of travel?
– How does e-bike ownership affect travel by various modes?
– How does the transition to e-bike ownership affect travel by various modes?
– How does e-bike use affect travel by other modes?
Regression models and cross-lagged panel model based on panel data (MPN 2013-2016)
April 10th 2018
Distance travelled (in km) by different modes (MPN, 2013)
Owns e-bike?
No Yes p-value
Car driver 57.5 44.6 0.002
Car passenger 16.8 21.2 0.094
Train 15.8 6.4 0.000
BTM 3.7 1.5 0.005
Moped 0.4 0.3 0.552
Bicycle 6.7 2.0 0.000
E-bike 0.0 8.4 0.000
Walk 1.5 1.7 0.310
Group size 3,518 467
How does e-bike ownership affect travel behaviour?
Effect of e-bike ownership on travel behaviour needs to be controlled for relevant confounding variables (e.g. age)
April 10th 2018
Distance travelled (in km) by different modes (MPN, 2013)
Owns e-bike?
No Yes p-value
Car driver 57.5 44.6 0.002
Car passenger 16.8 21.2 0.094
Train 15.8 6.4 0.000
BTM 3.7 1.5 0.005
Moped 0.4 0.3 0.552
Bicycle 6.7 2.0 0.000
E-bike 0.0 8.4 0.000
Walk 1.5 1.7 0.310
Group size 3,518 467
How does e-bike ownership affect travel behaviour?
Unstandardized effects (B’s) of e-bike ownership on mode use based on results of multivariate regression models with age, gender and education level as control variables
Only significant effect on (conventional) bicycle use
April 10th 2018
Research questions & methods
1. What are the socio-demographic user profiles of e-bike owners?
– For what trip purposes do they use the e-bike?
– How have profile shares developed over the past years?
Latent class analysis based on repeated cross-sectional data (OViN 2013-2016)
2. To what extent is e-bike use substituting/complementing other modes of travel?
– How does e-bike ownership affect travel by various modes?
– How does the transition to e-bike ownership affect travel by various modes?
– How does e-bike use affect travel by other modes?
Regression models and cross-lagged panel model based on panel data (MPN 2013-2016)
April 10th 2018
Adoption of e-bike in MPN
In total 306 people ‘transitioned to’ e-bike ownership:
– 2013-2014 98
– 2014-2015 117
– 2015-2016 91
April 10th 2018
Differences in distance travelled (in km) by different modes between before and after the transition to e-bike ownership (MPN, 2013-2016)
Diff. p-value
Car driver -1,9 0,648
Car passenger 1,2 0,743
Train 2,1 0,340
BTM -1,6 0,199
Moped -0,4 0,224
Bicycle -4,2 0,000
E-bike 6,8 0,000
Walk -0,4 0,029
Group size 306
How does transition to e-bike ownership affect travel behaviour?
Note that because these are ‘within-person’ differences time-constant individual characteristics are automatically controlled
Again, only significant effect on (conventional) bicycle use
April 10th 2018
Research questions & methods
1. What are the socio-demographic user profiles of e-bike owners?
– For what trip purposes do they use the e-bike?
– How have profile shares developed over the past years?
Latent class analysis based on repeated cross-sectional data (OViN 2013-2016)
2. To what extent is e-bike use substituting/complementing other modes of travel?
– How does e-bike ownership affect travel by various modes?
– How does the transition to e-bike ownership affect travel by various modes?
– How does e-bike use affect travel by other modes?
Regression models and cross-lagged panel model based on panel data (MPN 2013-2016)
April 10th 2018
28
3*5=15 first-order stability coefficients2*5=10 second-order stability coefficients10*4=40 correlations between exogenous variables and error terms16*3=48 (first-order) cross-lagged parameters
Cross-lagged panel model
Data & model estimation
• 4 waves of the MPN (2013-2016)
• Mode use operationalised as number of trips by each mode (over the course of 3 days)
• All individuals >=1 wave were included
n=8,687
• Full information ML estimation
Pattern # %
p0001 1355 15.6
p0010 165 1.9
p0011 158 1.8
p0100 1255 14.4
p0101 276 3.2
p0110 595 6.8
p0111 887 10.2
p1000 1080 12.4
p1001 43 0.5
p1010 176 2.0
p1011 159 1.8
p1100 525 6.0
p1101 234 2.7
p1110 532 6.1
p1111 1247 14.4
Total 8687 100.0
April 10th 2018
Baseline correlations (2013)
Car driver Train BTM Bicycle E-bike
Car driver 1
Train -0.155**
1
BTM -0.166**
0.327**
1
Bicycle -0.241**
0.159**
0.019 1
E-bike -0.059**
-0.037*
-0.050**
-0.106**
1
April 10th 2018
Stability coefficients
(standardized, all sign. at p<0.000)
Stability coefficients (standardized)
First-order Second-order
Car driver 0.510 0.291
Train 0.438 0.237
BTM 0.395 0.226
Bicycle 0.538 0.217
E-bike 0.512 0.209
April 10th 2018
Cross-lagged parameters (standardized)
Only 3 (out of 16) were significant:
• Bike (t-1) car (t): -0.046 (p=0.000)
• E-bike (t-1) bike (t): -0.017 (p=0.018)
• Train (t-1) bike (t): 0.018 (p=0.029)
April 10th 2018
Conclusions
• Middle-aged and young segment who use the e-bike for utilitarian purposes are strongly upcoming in the Netherlands
– This finding suggests that image of the e-bike is changing
• E-bike strongly reduces travel by conventional bicycle
• Presently, little evidence that e-bike is substituting other modes
• But this might change when younger/utilitarian user segments continue to grow
April 10th 2018
Questions & discussion
Maarten Kroesen
Delft University of Technology
Lucas Harms
KiM Netherlands Institute for Transport Policy Analysis [email protected]
April 10th 2018