Analysing the trip and user characteristics of the combined bicycle and transit mode

Sanmay Shelat, Raymond Huisman, Niels van Oort

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)
4 Downloads (Pure)

Abstract

Several cities around the world are facing mobility related problems such as traffic congestion and air pollution. Although limited individually, the combination of bicycle and transit offers speed and accessibility that can compete with automobiles by complementing each other's characteristics. Recognising the potential benefits with regard to accessibility, health, and sustainability, several studies have investigated policies that encourage integration of these modes. However, the actual users and trips of the combined bicycle and transit mode have not been extensively studied empirically. This study addresses this gap by (i) reviewing empirical findings on related modes, (ii) deriving user and trip characteristics of the combined bicycle and transit mode in the Netherlands, and (iii) applying latent class cluster analysis to discover prototypical users based on their socio-demographic attributes. Most trips by this combined mode are found to be for relatively long commutes where transit is in the form of trains, and bicycle and walking are access and egress modes respectively. Furthermore, seven user groups are identified and their travel behaviour is discussed. Transport authorities may use these empirical results to further streamline integration of bicycle and transit for its largest users as well as to tailor policies to attract more travellers.

Original languageEnglish
Pages (from-to)68-76
JournalResearch in Transportation Economics
Volume69
DOIs
Publication statusPublished - 2018

Keywords

  • Bicycle
  • Bicycle-transit integration
  • Latent class cluster analysis
  • Netherlands
  • Transit

Fingerprint

Dive into the research topics of 'Analysing the trip and user characteristics of the combined bicycle and transit mode'. Together they form a unique fingerprint.

Cite this