User characteristics and trip patterns of e-bike use in the ......The rise of E-bikes • After China and Japan, adoption of e-bikes in European countries is increasing rapidly. •

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)

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Source: OViN 2013-2016

Source: RAI/BOVAG

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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

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2. What are the environmental/health benefits of e-bikes?

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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

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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


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


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


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




Student (%) 0 0 0 1 91



Retired (%) 98 1 1 0 0

Other (%) 0 3 7 4 3





Other (%) 1 4 2 9 13






13 8 10 9 8

Car trip (yes) (%) 28 47 34 44 9



Class 1: retired old-age recreational userClass 2: full-time employed middle-aged user

April 10th 2018

1 2 3 4 5


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




Student (%) 0 0 0 1 91



Retired (%) 98 1 1 0 0

Other (%) 0 3 7 4 3





Other (%) 1 4 2 9 13






13 8 10 9 8

Car trip (yes) (%) 28 47 34 44 9



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


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




Student (%) 0 0 0 1 91



Retired (%) 98 1 1 0 0

Other (%) 0 3 7 4 3





Other (%) 1 4 2 9 13






13 8 10 9 8

Car trip (yes) (%) 28 47 34 44 9



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


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




Student (%) 0 0 0 1 91



Retired (%) 98 1 1 0 0

Other (%) 0 3 7 4 3





Other (%) 1 4 2 9 13






13 8 10 9 8

Car trip (yes) (%) 28 47 34 44 9



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



– For what trip purposes do they use the e-bike?

– How have profile shares developed over the past years?



– 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?


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











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











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

[email protected]

Lucas Harms

KiM Netherlands Institute for Transport Policy Analysis [email protected]

April 10th 2018

Documents

User characteristics and trip patterns of e-bike use in the ......The rise of E-bikes • After China and Japan, adoption of e-bikes in European countries is increasing rapidly. •