The dynamics of household travel time expenditures and car ownership decisions |
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| Institution: | 1. School of Pharmacy, Nantong University, Nantong, China;2. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China;3. Department of Orthopaedics, Orthopaedic Hospital of Guizhou Province, Guiyang, China;4. School of Pharmacy, Hebei University, Baoding, China;1. Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands;2. Department of Neurology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands;3. Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands;1. MTA Centre for Ecological Research, Klebelsberg K. u. 3, H-8237 Tihany, Hungary;2. Fabis Consulting Ltd, Barton In Fabis, Nottingham NG11 0AE, UK;3. Department of Climatology and Landscape Ecology, University of Szeged, Egyetem u. 2., H-6722 Szeged, Hungary;1. Institute of Geography and Spatial Management, Jagiellonian University, Gronostajowa 7, 30-387 Krakow, Poland;2. Institute for Natural Resource Conservation, Kiel University, Olshausenstr. 40, 24098 Kiel, Germany;1. National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands;2. Natural Capital Project, Stanford University, Stanford, USA;3. Radboud University, Nijmegen, the Netherlands;4. Netherlands Environmental Assessment Agency (PBL), The Hague, the Netherlands;5. Wageningen University & Research (WUR), Wageningen, the Netherlands |
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| Abstract: | A dynamic (panel data) structural equations model is developed that links four dependent travel behavior variables at two points in time, one year apart. The four dependent variables are: car ownership, travel time per week by car, travel time by public transit, and travel time by nonmotorized modes. Exogenous variables include 13 household characteristics and variables accounting for period effects over the 1985 to 1987 time frame in the Netherlands. The model treats car ownership as ordered-response probit variables and all travel times as censored (tobit) continuous variables. The model accounts for serially-correlated errors and panel conditioning biases. Results are interpreted in terms of recommendations for forecasting procedures. |
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