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1 Ministry of Infrastructure and Watermanagement 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 for Transport Policy Analysis
2 12 oktober 2017
3 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)
4 Trends in the Netherlands *1,000 Overige fietsen Elektrische fietsen 2017: 31% of all bikes sold were e-bikes (294,000) Source: RAI/BOVAG *1,000, bezit geen e-fiets Elektrische fiets bezitters Source: OViN : 12.5% of Dutch population (aged >12) owns an e-bike (1.8 million)
5 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?
6 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
7 Most e-bikers are 65+ Share of e-bike trips by age Source: CBS (OViN 2013/2016 calculation by KiM) Oct 5th, 2017
8 Mostly used for leisure purposes Share of e-bike trips by trip purpose Source: CBS (OViN 2013/2016 calculation by KiM) Oct 5th, 2017
9 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
10 2. What are the environmental/health benefits of e-bikes?
11 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 ) 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 )
12 Conceptual model Latent class model Indicators Socio-demographic and household characteristics Gender Inactive covariates E-bike use A probabilistic clustering technique Latent Gold 5.0 Age Level of education Sociodemographic user profiles Bicycle use Car use Primary occupation PT use Household composition Year Active covariate
13 Data Dutch mobility survey (OViN) Annual national survey with ~ 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.
14 Model estimation 5-class solution was optimal Indicators Socio-demographic and household characteristics Gender Inactive covariates E-bike use Age Level of education Primary occupation Household composition Sociodemographic user profiles Year Bicycle use Car use PT use Active covariate
15 Overall class size (%) Gender Male (%) Female (%) Age (%) (%) (%) (%) (%) >64 (%) Mean Education level Low (%) Intermediate (%) High (%) Occupational status Employed hours per week (%) Employed >= 30 hours per week (%) Works in household (%) Student (%) Unemployed (%) Incapacitated (%) Retired (%) Other (%) Household composition Single without child(ren) (%) Couple without child(ren) (%) Couple with child(ren) (%) Other (%) Mode use on the day of the survey (covariates) E-bike work trip (yes) (%) E-bike school trip (yes) (%) E-bike maintenance trip (yes) (%) E-bike social/recreational trip (yes) (%) Car trip (yes) (%) Public transport trip (yes) (%) Conventional bicycle trip (yes) (%) Class 1: retired oldage recreational user
16 Overall class size (%) Gender Male (%) Female (%) Age (%) (%) (%) (%) (%) >64 (%) Mean Education level Low (%) Intermediate (%) High (%) Occupational status Employed hours per week (%) Employed >= 30 hours per week (%) Works in household (%) Student (%) Unemployed (%) Incapacitated (%) Retired (%) Other (%) Household composition Single without child(ren) (%) Couple without child(ren) (%) Couple with child(ren) (%) Other (%) Mode use on the day of the survey (covariates) E-bike work trip (yes) (%) E-bike school trip (yes) (%) E-bike maintenance trip (yes) (%) E-bike social/recreational trip (yes) (%) Car trip (yes) (%) Public transport trip (yes) (%) Conventional bicycle trip (yes) (%) Class 1: retired oldage recreational user Class 2: full-time employed middle-aged user
17 Overall class size (%) Gender Male (%) Female (%) Age (%) (%) (%) (%) (%) >64 (%) Mean Education level Low (%) Intermediate (%) High (%) Occupational status Employed hours per week (%) Employed >= 30 hours per week (%) Works in household (%) Student (%) Unemployed (%) Incapacitated (%) Retired (%) Other (%) Household composition Single without child(ren) (%) Couple without child(ren) (%) Couple with child(ren) (%) Other (%) Mode use on the day of the survey (covariates) E-bike work trip (yes) (%) E-bike school trip (yes) (%) E-bike maintenance trip (yes) (%) E-bike social/recreational trip (yes) (%) Car trip (yes) (%) Public transport trip (yes) (%) Conventional bicycle trip (yes) (%) Class 1: retired oldage recreational user Class 2: full-time employed middleaged user Class 3: Older female recreational user
18 Overall class size (%) Gender Male (%) Female (%) Age (%) (%) (%) (%) (%) >64 (%) Mean Education level Low (%) Intermediate (%) High (%) Occupational status Employed hours per week (%) Employed >= 30 hours per week (%) Works in household (%) Student (%) Unemployed (%) Incapacitated (%) Retired (%) Other (%) Household composition Single without child(ren) (%) Couple without child(ren) (%) Couple with child(ren) (%) Other (%) Mode use on the day of the survey (covariates) E-bike work trip (yes) (%) E-bike school trip (yes) (%) E-bike maintenance trip (yes) (%) E-bike social/recreational trip (yes) (%) Car trip (yes) (%) Public transport trip (yes) (%) Conventional bicycle trip (yes) (%) Class 1: retired oldage recreational user Class 2: full-time employed middleaged user Class 3: Older female recreational user Class 4: Younger female non-recreational user
19 Overall class size (%) Gender Male (%) Female (%) Age (%) (%) (%) (%) (%) >64 (%) Mean Education level Low (%) Intermediate (%) High (%) Occupational status Employed hours per week (%) Employed >= 30 hours per week (%) Works in household (%) Student (%) Unemployed (%) Incapacitated (%) Retired (%) Other (%) Household composition Single without child(ren) (%) Couple without child(ren) (%) Couple with child(ren) (%) Other (%) Mode use on the day of the survey (covariates) E-bike work trip (yes) (%) E-bike school trip (yes) (%) E-bike maintenance trip (yes) (%) E-bike social/recreational trip (yes) (%) Car trip (yes) (%) Public transport trip (yes) (%) Conventional bicycle trip (yes) (%) Class 1: retired oldage recreational user Class 2: full-time employed middle-aged utilitarian user Class 3: Older female recreational user Class 4: Younger female utilitarian user Class 5: Young student utilitarian user
20 Weighted class sizes (pop.=17m) Relative size Class Total 2013 (%) ,169, (%) ,369, (%) ,629, (%) ,831,556 Absolute size , , , ,274 17,170 1,169, , , , ,562 17,577 1,369, , , , ,608 24,357 1,629, , , , ,204 32,904 1,831,556 % growth Class 1: retired oldage recreational user Class 2: full-time employed middle-aged utilitarian user Class 3: Older female recreational user Class 4: Younger female utilitarian user Class 5: Young student utilitarian user
21 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 ) 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 )
22 How does e-bike ownership affect travel behaviour? Distance travelled (in km) by different modes (MPN, 2013) Owns e-bike? No Yes p-value Car driver Car passenger Train BTM Moped Bicycle E-bike Walk Group size 3, Effect of e-bike ownership on travel behaviour needs to be controlled for relevant confounding variables (e.g. age)
23 How does e-bike ownership affect travel behaviour? Distance travelled (in km) by different modes (MPN, 2013) Owns e-bike? No Yes p-value Car driver Car passenger Train BTM Moped Bicycle E-bike Walk Group size 3, Only significant effect on (conventional) bicycle use 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
24 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 ) 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 )
25 Adoption of e-bike in MPN In total 306 people transitioned to e-bike ownership:
26 How does transition to e-bike ownership affect travel behaviour? Differences in distance travelled (in km) by different modes between before and after the transition to e-bike ownership (MPN, ) 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 Again, only significant effect on (conventional) bicycle use Note that because these are within-person differences time-constant individual characteristics are automatically controlled
27 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 ) 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 )
28 Cross-lagged panel model 3*5=15 first-order stability coefficients 2*5=10 second-order stability coefficients 10*4=40 correlations between exogenous variables and error terms 16*3=48 (first-order) cross-lagged parameters 28
29 Data & model estimation 4 waves of the MPN ( ) 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 # % p p p p p p p p p p p p p p p Total
30 Baseline correlations (2013) Car driver Train BTM Bicycle E-bike Car driver 1 Train ** 1 BTM ** ** 1 Bicycle ** ** E-bike ** * ** ** 1
31 Stability coefficients (standardized, all sign. at p<0.000) Stability coefficients (standardized) First-order Second-order Car driver Train BTM Bicycle E-bike
32 Cross-lagged parameters (standardized) Only 3 (out of 16) were significant: Bike (t-1) car (t): (p=0.000) E-bike (t-1) bike (t): (p=0.018) Train (t-1) bike (t): (p=0.029)
33 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
34 Questions & discussion Maarten Kroesen Delft University of Technology Lucas Harms KiM Netherlands Institute for Transport Policy Analysis
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