A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand |
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| Institution: | 1. Department of Applied Economics III (Econometrics and Statistics), University of the Basque Country, Avda. Lehendakari Aguirre, 83, E48015 Bilbao, Spain;2. Institute for Landscape Architecture and Environmental Planning, Technical University of Berlin, D-10623 Berlin, Germany;3. Institute for Transport Studies, University of Leeds, UK;1. Energy, Transportation and Environment Department, Deutsches Institut für Wirtschaftsforschung, Berlin, Germany;2. Technische Universität Berlin, Germany;3. Center for Tax Policy and Administration, OECD, Paris, France;4. Institut für Höhere Studien, Wien, Austria;1. Expert Advisor in Transport Modeling, exo, 700, rue de la Gauchetière West, Office 2600, Montréal (Québec) H3B 5M2, Canada;2. Head of Laboratory of Innovations in Transportation (LITrans), Department of Civil Engineering, Ryerson University, 350 Victoria Street, Toronto, Canada;3. Department of Civil, Geological and Mining Engineering, Polytechnique Montréal, C.P. 6079, succ. Centre-Ville, Montréal (Québec) H3C 3A7, Canada |
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| Abstract: | In this paper we analyze demand for cycling using a discrete choice model with latent variables and a discrete heterogeneity distribution for the taste parameters. More specifically, we use a hybrid choice model where latent variables not only enter into utility but also inform assignment to latent classes. Using a discrete choice experiment we analyze the effects of weather (temperature, rain, and snow), cycling time, slope, cycling facilities (bike lanes), and traffic on cycling decisions by members of Cornell University (in an area with cold and snowy winters and hilly topography). We show that cyclists can be separated into two segments based on a latent factor that summarizes cycling skills and experience. Specifically, cyclists with more skills and experience are less affected by adverse weather conditions. By deriving the median of the ratio of the marginal rate of substitution for the two classes, we show that rain deters cyclists with lower skills from bicycling 2.5 times more strongly than those with better cycling skills. The median effects also show that snow is almost 4 times more deterrent to the class of less experienced cyclists. We also model the effect of external restrictions (accidents, crime, mechanical problems) and physical condition as latent factors affecting cycling choices. |
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| Keywords: | Discrete choice models Discrete heterogeneity Latent attributes |
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