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Variability in baseline travel behaviour as a predictor of changes in commuting by active travel, car and public transport: a natural experimental study.

Published version
Peer-reviewed

Repository DOI


Type

Article

Change log

Authors

Heinen, Eva 

Abstract

PURPOSE: To strengthen our understanding of the impact of baseline variability in mode choice on the likelihood of travel behaviour change. METHODS: Quasi-experimental analyses in a cohort study of 450 commuters exposed to a new guided busway with a path for walking and cycling in Cambridge, UK. Exposure to the intervention was defined using the shortest network distance from each participant's home to the busway. Variability in commuter travel behaviour at baseline was defined using the Herfindahl-Hirschman Index, the number of different modes of transport used over a week, and the proportion of trips made by the main (combination of) mode(s). The outcomes were changes in the share of commute trips (i) involving any active travel, (ii) involving any public transport, and (iii) made entirely by car. Variability and change data were derived from a self-reported seven-day record collected before (2009) and after (2012) the intervention. Separate multinomial regression models were estimated to assess the influence of baseline variability on behaviour change, both independently and as an interaction effect with exposure to the intervention. RESULTS: All three measures of variability predicted changes in mode share in most models. The effect size for the intervention was slightly strengthened after including variability. Commuters with higher baseline variability were more likely to increase their active mode share (e.g. for HHI: relative risk ratio [RRR] for interaction 3.34, 95% CI 1.41, 7.89) and decrease their car mode share in response to the intervention (e.g. for HHI: RRR 7.50, 95% CI 2.52, 22.34). CONCLUSIONS: People reporting a higher level of variability in mode choice were more likely to change their travel behaviour following an intervention. Future research should consider such variability as a potential predictor and effect modifier of travel and physical activity behaviour change, and its significance for the design and targeting of interventions.

Description

Keywords

Active travel, Behaviour change, Car use, Longitudinal, Mode choice variability, Public transport

Journal Title

J Transp Health

Conference Name

Journal ISSN

2214-1405
2214-1413

Volume Title

3

Publisher

Elsevier BV
Sponsorship
Medical Research Council (MC_UU_12015/6)
null (unknown)
Medical Research Council (MR/K023187/1)
Economic and Social Research Council (ES/G007462/1)
Wellcome Trust (087636/Z/08/Z)
NETSCC (None)
TCC (None)
NIHR Evaluation Trials and Studies Coordinating Centre (09/3001/06)
The Commuting and Health in Cambridge study was developed by David Ogilvie, Simon Griffin, Andy Jones and Roger Mackett and initially funded under the auspices of the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence. Funding from the British Heart Foundation, Economic and Social Research Council, Medical Research Council, National Institute for Health Research and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. The study is now funded by the National Institute for Health Research Public Health Research programme (project number 09/3001/06: see http://www.phr.nihr.ac.uk/funded_projects). David Ogilvie is supported by the Medical Research Council [Unit Programme number MC_UP_12015/6] and Eva Heinen is also supported by a VENI fellowship of the Netherlands Organisation for Scientific Research (project number 016.145.073). The views and opinions expressed herein are those of the authors and do not necessarily reflect those of the NIHR PHR programme or the Department of Health. The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript. We thank all staff from the MRC Epidemiology Unit Functional Group Team, in particular for study coordination and data collection (led by Cheryl Chapman and Fiona Whittle) and data management. We also thank Alice Dalton for computing the proximity measures used in this analysis, Louise Foley for her contribution to preparing the questionnaire data for analysis, and Jenna Panter for the insights gained from previous collaborative analyses of travel behaviour change in this study.