Abstract
AbstractIn recent years, the e-bike has become increasingly popular in many European countries. With higher speeds and less effort needed, the e-bike is a promising mode of transport to many, and it is considered a good alternative for certain car trips by policy-makers and planners. A major limitation of many studies that investigate such substitution effects of the e-bike, is their reliance on cross-sectional data which do not allow an assessment of within-person travel mode changes. As a consequence, there is currently no consensus about the e-bike’s potential to replace car trips. Furthermore, there has been little research focusing on heterogeneity among e-bike users. In this respect, it is likely that different groups exist that use the e-bike for different reasons (e.g. leisure vs commute travel), something which will also influence possible substitution patterns. This paper contributes to the literature in two ways: (1) it presents a statistical analysis to assess the extent to which e-bike trips are substituting trips by other travel modes based on longitudinal data; (2) it reveals different user groups among the e-bike population. A Random Intercept Cross-Lagged Panel Model is estimated using five waves of data from the Netherlands Mobility Panel. Furthermore, a Latent Class Analysis is performed using data from the Dutch national travel survey. Results show that, when using longitudinal data, the substitution effects between e-bike and the competing travel modes of car and public transport are not as significant as reported in earlier research. In general, e-bike trips only significantly reduce conventional bicycle trips in the Netherlands, which can be regarded an unwanted effect from a policy-viewpoint. For commuting, the e-bike also substitutes car trips. Furthermore, results show that there are five different user groups with their own distinct behaviour patterns and socio-demographic characteristics. They also show that groups that use the e-bike primarily for commuting or education are growing at a much higher rate than groups that mainly use the e-bike for leisure and shopping purposes.
Publisher
Springer Science and Business Media LLC
Subject
Transportation,Development,Civil and Structural Engineering
Reference34 articles.
1. Bourne, J.E., Sauchelli, S., Perry, R., Page, A., Leary, S., England, C., Cooper, A.R.: Health benefits of electrically-assisted cycling: a systematic review. Int. j. Behav. Nutr. Phys. Act. 15(1), 116 (2018)
2. Brown, T.A.: Confirmatory Factor Analysis for Applied Research. Guilford Publications (2014)
3. Cherry, C., Cervero, R.: Use characteristics and mode choice behavior of electric bike users in China. Transp. Policy 14(3), 247–257 (2007). https://doi.org/10.1016/j.tranpol.2007.02.005
4. Cherry, C.R., Yang, H., Jones, L.R., He, M.: Dynamics of electric bike ownership and use in Kunming, China. Transp. Policy 45, 127–135 (2016)
5. CONEBI: European Bicycle Market 2017 Edition. Industry & Market Profile, Brussels (2017)
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