A latent segmentation based multiple discrete continuous extreme value model |
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| Affiliation: | 1. Department of Civil Engineering & Applied Mechanics, McGill University, Suite 483, 817 Sherbrooke St. W., Montréal, Canada;2. Department of Civil and Environmental Engineering, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL 33620, United States;3. Faculty of Business, Economics and Law, La Trobe University, Melbourne, Victoria 3086, Australia;1. Department of Transportation Research, The Seoul Institute, 57 Nambusunhwan-ro Seocho-gu, Seoul 06756, South Korea;2. School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Dr NW, Atlanta, GA 30318, United States;1. Queensland University of Technology (QUT), Centre for Accident Research & Road Safety – Queensland (CARRS-Q), Australia & Research Affiliate, Department of Civil, Environmental & Construction Engineering, University of Central Florida, USA;2. Department of Civil, Environmental & Construction Engineering, University of Central Florida, USA;3. Marlin Engineering, USA;4. Department of Civil, Environmental & Construction Engineering, University of Central Florida, USA;1. Department of Management and Accounting, Shahid Beheshti University, Tehran, Iran;2. Erivan K. Haub School of Business, Saint Joseph’s University, Philadelphia, PA, 19131, United States;3. Department of Human Geography and Planning, Utrecht University, Utrecht, 3584 CB, The Netherlands |
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| Abstract: | We examine an alternative method to incorporate potential presence of population heterogeneity within the Multiple Discrete Continuous Extreme Value (MDCEV) model structure. Towards this end, an endogenous segmentation approach is proposed that allocates decision makers probabilistically to various segments as a function of exogenous variables. Within each endogenously determined segment, a segment specific MDCEV model is estimated. This approach provides insights on the various population segments present while evaluating distinct choice regimes for each of these segments. The segmentation approach addresses two concerns: (1) ensures that the parameters are estimated employing the full sample for each segment while using all the population records for model estimation, and (2) provides valuable insights on how the exogenous variables affect segmentation. An Expectation–Maximization algorithm is proposed to address the challenges of estimating the resulting endogenous segmentation based econometric model. A prediction procedure to employ the estimated latent MDCEV models for forecasting is also developed. The proposed model is estimated using data from 2009 National Household Travel Survey (NHTS) for the New York region. The results of the model estimates and prediction exercises illustrate the benefits of employing an endogenous segmentation based MDCEV model. The challenges associated with the estimation of latent MDCEV models are also documented. |
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| Keywords: | Multiple discrete continuous models Latent segmentation approaches Daily vehicle type and use decisions Activity type Accompaniment type Mileage |
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