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Walking,cycling, and public transport for commuting and non-commuting travels across 5 European urban regions: Modal choice correlates and motivations
Institution:1. Univ. Paris Est Creteil, Lab''Urba, UPEC, Créteil, France;2. Sorbonne Paris Cité Epidemiology and Statistics Research Center (CRESS), Inserm U1153, Inra U1125, Cnam, Paris 13 University, Nutritional Epidemiology Research Team (EREN), Bobigny, France;3. Université de Paris, CRESS, INSERM, INRAE, HERA team (Health Environmental Risk Assessment), F-75004 Paris, France;4. Université Paris 8 Vincennes Saint-Denis, LADYSS UMR 7533 CNRS, France;5. Laboratoire Image, Ville, Environnement, UMR 7362 CNRS, Strasbourg University, Strasbourg, France;6. Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary;7. Department of Social and Policy Sciences, University of Bath, Bath BA2 7AY, UK;8. Department of Movement and Sport Sciences, Ghent University, Ghent, Belgium;9. Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands;10. Upstream Team, Amsterdam UMC, De Boelelaan 1089a, 1081 HV Amsterdam, the Netherlands;11. Sorbonne University, Department of Nutrition, Pitié-Salpêtrière university hospital (AP-HP), Paris, France;1. School of Urban & Regional Planning, Ryerson University, Toronto, ON M5B1Y3, Canada;2. TransLAB (Transportation Research Lab), School of Earth, Environment & Society, McMaster University Hamilton, ON L8S4K1, Canada;1. College of Urban and Environmental Sciences, Peking University, No.5 Yiheyuan Road, Beijing 100871, China;2. Institute of Beijing Studies, Beijing Union University, 197 Bei-Tu-Cheng West Road, Beijing 100191, China
Abstract:The objective of this study was to explore individual and contextual-level characteristics associated with active (walking and cycling) and public transport as main travel modes for both non-commuting and commuting purposes, in residents of five European urban regions. We also described participant-reported motivations for modal choice for each journey purpose. The study used multilevel models to investigate cross-sectional associations of individual (i.e. age, gender, educational level) and contextual (defined by a combination of residential neighbourhood characteristics in typologies) characteristics with the choice of active and public transport as outcome. Based on an online survey of 6037 residents of Ghent and suburbs (Belgium), Paris and inner suburbs (France), Budapest and suburbs (Hungary), the Randstad (including the cities of Amsterdam, Rotterdam, The Hague and Utrecht in the Netherlands) and Greater London (United Kingdom), we observed associations with both individual and contextual characteristics.Results of the multilevel modelling show that the probability of using active or public transport as main mode varies depending on both individual and contextual characteristics. At individual level, relations with gender, age, education, weight status and having at least one child varied according to main transport mode and/or purpose. For example, overweight participants reported lower level of cycling for commuting and non-commuting travels than normal-weight participants. In the context of non-commuting travels, participants with one or more child reported less public transport use and more walking (vs participants without children). Among contextual-level variables, urban characteristics of the residential neighbourhood defined by four clusters (according to food environment, recreational facilities and active mobility opportunities) were associated with public transport and walking but not with cycling. For active transport the most important reasons were “I like to travel (on foot or by bike)” and “I want to be physically active” for both travel purposes. “Public transport facilities nearby” was indicated as the most important reason for public transport (for both trip purposes) – the second was “Journey time”.Our findings highlight the importance of exploring a combination of multiple correlates at individual and contextual level according to journey purposes and suggest that the role of health-related individual characteristics such as weight status need further exploration.
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