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Accounting for the Theory of Planned Behaviour in departure time choice

Lookup NU author(s): Professor Elisabetta Cherchi


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Motivating people to change their departure time could play a key role in reducing peak-hour congestion, which remains one of the most prevalent transport problems in large urban areas. To achieve this behavioural change, it is necessary to better understand the factors that influence departure time choice. So far departure time choice modelling focussed mainly on objective factors, such as time and costs as main behavioural determinants.In this study, we derived psychological factors based on the Theory of Planned Behaviour, estimated them based on structural equation modelling, and included them into a discrete choice model. The psychological factors were measured based on an online questionnaire addressed to car commuters to the city centre of Copenhagen (N = 286). The questionnaire additionally included a travel diary and a stated preference experiment with nine departure time choice scenarios. All psychological factors had a significant effect on departure time choice and could improve the model as compared to a basic discrete choice model without latent constructs. As expected, the effects of the psychological factors were different depending on framework conditions: for people with fixed starting times at work, the intention to arrive at work on time (as estimated by subjective norm, attitude, perceived behavioural control) had the strongest effect; for people with flexible working hours, the attitude towards short travel time was most relevant. Limitations, the inclusion of additional psychological factors and their possible interactions are discussed.

Publication metadata

Author(s): Thorhauge M, Haustein S, Cherchi E

Publication type: Article

Publication status: Published

Journal: Transportation Research Part F

Year: 2016

Volume: 38

Pages: 94-105

Print publication date: 01/04/2016

Online publication date: 08/02/2016

Acceptance date: 19/01/2016

ISSN (print): 1369-8478

ISSN (electronic): 1873-5517

Publisher: Elsevier


DOI: 10.1016/j.trf.2016.01


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