A disaggregate model of auto-type choice |
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Affiliation: | 1. Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Av. do Café s/n, 14040-903 Ribeirão Preto, SP, Brazil;2. Departamento de Biologia, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Av. Bandeirantes 3900, 14040-901 Ribeirão Preto, SP, Brazil;3. Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun, Mengla, Yunnan 666303, China;1. Deparmtnet of Mechanical Engineering, Sri Krishna College of Technology, Coimbatore 641 042, India;2. Technology Development Department, Summits Hygronics Pvt. Ltd., Coimbatore 641 402, India;1. Department of Oral and Maxillofacial Surgery and Diagnostics, Faculty of Dentistry of Araraquara, São Paulo State University – UNESP, Araraquara, São Paulo, Brazil;2. Department of Prosthesis, Faculty of Dentistry of Araraquara, São Paulo State University – UNESP, Araraquara, São Paulo, Brazil;1. Civil & Environmental Engineering Department, Old Dominion University, Norfolk, VA 23529, USA;2. Department of Civil Engineering, University of Toronto, Canada;3. Department of Civil, Environmental and Construction Engineering, University of Central Florida, 12800 Pegasus Drive, Room 301D, Orlando, FL 32816, USA;4. Southern California Association of Governments, 818 W. Seventh Street, 12th Floor, Los Angeles, CA 90017, USA |
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Abstract: | Previous models of auto-type choice have not been able to disentangle very much of the structure of the household's auto-choice decision: the models assumed that very few auto characteristics affect choice, and often these few parameters were estimated with low precision. Hence the models had only limited use in forecasting the effects of government policies to influence transportation energy consumption. The present paper introduces a multinomial logit model for the type of car that households will choose to buy. The model includes a large variety of auto characteristics as explanatory variables, as well as a large number of characteristics of the household and the driving environment. The model fits the data quite well, and all of the variables enter with the correct signs and plausible magnitudes. |
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