Route choice decisions of e-bike users: analysis of GPS tracking data in the Netherlands

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

12 Citations (Scopus)


Over the past years, the usage of electric bikes has emerged. E-bikes are suitable for short and medium distance trips. Therefore, the Dutch government promotes using e-bikes for daily commuting trips. However, the impact of increasing demand on the cycling infrastructure is unclear. Additionally, route choice models for e-bikes are limited. This paper estimates a route choice model for e-bike users in the Noord-Brabant region of The Netherlands. The data used are based on 17626 trips from 742 users including user profiles extracted from GPS data. In order to analyze the data, a mixed logit model is applied on the route choice of respondents with addition of the path-size attribute. Mixed logit model allows a panel data setup and enables the examination of preference heterogeneity around the mean of distance attribute. Moreover, the path-size attribute is included on the model to account for the overlap between alternatives. Socio-demographic characteristics and trip-related factors are found to be influencing on the route choice decisions of e-bike and bike users. There are differences on the significance of variables between e-bike and bike users.
Original languageEnglish
Title of host publicationGeospatial Technologies for Local and Regional Development - Proceedings of the 22nd AGILE Conference on Geographic Information Science
EditorsAli Mansourian, Phaedon Kyriakidis, Dimitrios Skarlatos, Diofantos Hadjimitsis
Number of pages17
ISBN (Electronic)978-3-030-14745-7
ISBN (Print)978-3-030-14744-0
Publication statusPublished - 2020
Event22nd AGILE Conference on Geographic Information Science, 2019 - Limassol, Cyprus
Duration: 17 Jun 201920 Jun 2019

Publication series

NameLecture Notes in Geoinformation and Cartography
ISSN (Print)1863-2246
ISSN (Electronic)1863-2351


Conference22nd AGILE Conference on Geographic Information Science, 2019


  • Big data
  • E-bike
  • GPS
  • Route choice


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