Commuters’ attitudes and norms related to travel time and punctuality: A psychographic segmentation to reduce congestion

Sonja Haustein*, Mikkel Thorhauge, Elisabetta Cherchi

*Corresponding author for this work

    Research output: Contribution to journalJournal articleResearchpeer-review

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    Congestion remains one of the most prevalent transport problems in big cities. As a starting point for more targeted interventions to reduce congestion, this paper suggests a segmentation of commuters. Based on psychographic factors derived from an expanded Theory of Planned Behaviour, we identify three distinct commuter segments: (1) Unhurried timely commuters, who find it very important to arrive on time but less important to have a short travel time; (2) Self-determined commuters, who find it less important to arrive on lime and depend less on others for their transport choices; and (3) Busy commuters, who find it both important to arrive on time and to have a short travel time. Comparing the segments based on background variables shows that Self-determined commuters are younger and work more often on flextime, while Unhurried timely commuters have longer distances to work and commute more often by public transport. Results of a discrete departure time choice model, estimated based on data from a stated preference experiment, confirm the criterion validity of the segmentation. A scenario simulating a toll ring illustrates that mainly Self-determined commuters would change their departure time as a response to this economic intervention, while we suggest alternative interventions for the two other segments. The results stress the need for more targeted efforts to change departure time choice and point to ways to improve the suggested segmentation approach.
    Original languageEnglish
    JournalTravel Behaviour and Society
    Pages (from-to)41-50
    Number of pages10
    Publication statusPublished - 2018


    • Departure time
    • Segmentation
    • Attitude
    • Stated preference
    • Discrete choice modelling


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