A latent segmentation based multiple discrete continuous extreme value model

Transportation Research Part B: Methodological

Volumn 58, Issue , 2013, Pages 154-169

  Sobhani, Anae ^a   Eluru, Naveen ^a   Faghih Imani, Ahmadreza ^a  

^a MCGILL UNIVERSITY   (Canada)

Author keywords

Accompaniment type; Activity type; Daily vehicle type and use decisions; Latent segmentation approaches; Mileage; Multiple discrete continuous models

Indexed keywords

DECISION MAKING; IMAGE SEGMENTATION; MAXIMUM PRINCIPLE; MOTOR TRANSPORTATION; TRANSPORTATION;

ACCOMPANIMENT TYPE; CONTINUOUS MODELS; LATENT SEGMENTATION APPROACHES; MILEAGE; VEHICLE TYPES;

FORECASTING;

DECISION MAKING; ESTIMATION METHOD; FORECASTING METHOD; MODEL TEST; PARAMETERIZATION; PLANNING METHOD; SEGMENTATION;

NEW YORK [UNITED STATES]; UNITED STATES;

EID: 84894420670     PISSN: 01912615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trb.2013.07.009     Document Type: Article

References (38)

1. Akaike, H., 1977. On entropy maximization principle. In: Krishnaiah, P.R. (Ed.), Applications of Statistics. North-Holland, Amsterdam, pp. 27-41.
2. Anowar, S., Yasmin, S., Eluru, N., Miranda-Moreno, L., 2012. Analyzing Car Ownership in Two Quebec Metropolitan Regions: A Comparison of Latent Ordered and Unordered Response Models. Technical Paper, Department of Civil Engineering and Applied Mechanics, McGill University.
3. Bhat, C.R., 1997. An endogenous segmentation mode choice model with an application to intercity travel. Transportation Science 31 (1), 34-48.
4. Bhat, C.R., 2005. A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions. Transportation Research Part B 39 (8), 679-707.
5. Bhat, C.R., 2008. The multiple discrete-continuous extreme value (MDCEV) model: role of utility function parameters, identification considerations, and model extensions. Transportation Research Part B 42 (3), 274-303.
6. Bhat, C.R., Eluru, N., 2009. A copula-based approach to accommodate residential self-selection effects in travel behavior modeling. Transportation Research Part B 43 (7), 749-765.
7. Bhat, C.R., Eluru, N., 2010. The Multiple Discrete-Continuous Extreme Value (MDCEV) model: formulation and applications. choice modelling: the state-ofthe-art and the state-of-practice. In: Proceedings from the inaugural International Choice Modelling Conference, pp. 71-100.
8. Burnham, K.P., Anderson, D.R., 2004. Multi-model inference: understanding AIC and BIC in model selection. Sociological Methods and Research 33, 261-304.
9. Cao, X., Mokhtarian, P.L., Handy, S.L., 2006. Neighborhood design and vehicle type choice: evidence from Northern California. Transportation Research Part D 11 (2), 133-145.
10. Carrasco, J.A., Miller, E.J., 2009. The social dimension in action: a multilevel, personal networks model of social activity frequency between individuals. Transportation Research Part A 43 (1), 90-104.
11. Castro, M., Eluru, N., Bhat, C.R., Pendyala, R.M., 2011. A joint model of participation in non-work activities and time-of-day choice set formation for workers. Transportation Research Record 2254, 140-150.
12. Cherchi, E., Guevara, C.A., 2012. A Monte Carlo experiment to analyze the curse of dimensionality in estimating random coefficients models with a full variance-covariance matrix. Transportation Research Part B 46 (2), 321-332.
13. Dube, J.P., 2004. Multiple discreteness and product differentiation: strategy and demand for carbonated soft drinks. Marketing Science 23 (1), 66-81.
14. Edwards, Y.D., Allenby, G.M., 2003. Multivariate analysis of multiple response data. Marketing Research 40, 321-334.
15. Eluru, N., Bhat, C.R., Pendyala, R.M., Konduri, K.C., 2010. A joint flexible econometric model system of household residential location and vehicle fleet composition/usage choices. Transportation: TRB Special Issue 37(4), 603-662.
16. Eluru, N., Bagheri, M., Miranda-Moreno, L., Fu, L., 2012. A latent class modelling approach for identifying vehicle driver injury severity factors at highwayrailway crossings. Accident Analysis & Prevention 47 (1), 119-127.
17. Ferdous, N., Eluru, N., Bhat, C.R., Meloni, I., 2010. A multivariate ordered response model system for adults' weekday activity episode generation by activity purpose and social context. Transportation Research Part B 44 (8-9), 922-943.
18. Greene, W.H., Hensher, D.A., 2003. A latent class model for discrete choice analysis: contrasts with mixed logit. Transportation Research Part B 37 (8), 681- 698.
19. Hanemann, W.M., 1978. A Methodological and Empirical Study of the Recreation Benefits from Water Quality Improvement. Ph.D. Dissertation, Department of Economics, Harvard University.
20. Hendel, I., 1999. Estimating multiple-discrete choice models: an application to computerization returns. Review of Economic Studies 66 (2), 423-446.
21. Kapur, A., Bhat, C.R., 2007. On modeling adults' daily time use by activity purpose and accompaniment arrangement. Transportation Research Record 2021, 18-27.
22. Konduri, K.C., Ye, X., Sana, B., Pendyala, R.M., 2011. A joint tour-based model of vehicle type choice and tour length. Transportation Research Record: Journal of the Transportation Research Board, 28-37.
23. Kuriyama, K., Hanemann, W.M., Hilger, J.R., 2010. A latent segmentation approach to a Kuhn-Tucker model: an application to recreation demand. Environmental Economics and Management 60 (3), 209-220.
24. Manchanda, P., Ansari, A., Gupta, S., 1999. The shopping basket: a model for multi-category purchase incidence decisions. Marketing Science 18, 95-114.
25. Mohammadian, A., Miller, E.J., 2003. Empirical investigation of household vehicle type decisions. Transportation Research Record 1854, 99-106.
26. Munger, D., L'ecuyer, P., Bastin, F., Cirillo, C., Tuffin, B., 2012. Estimation of the mixed logit likelihood function by randomized Quasi-monte Carlo. Transportation Research Part B 46 (2), 305-320.
27. Paleti, R., Eluru, N., Bhat, C.R., Pendyala, R.M., Adler, T.J., Goulias, K.G., 2011. Design of comprehensive microsimulator of household vehicle fleet composition, utilization, and evolution. Transportation Research Record 2254, 44-57.
28. Paleti, R., Pendyala, R.M., Bhat, C.R., Konduri, K.C., 2012. A joint tour-based model of tour complexity, passenger accompaniment, vehicle type choice, and tour length. In: Proceedings of 91st Annual Meeting of the Transportation Research Board, National Research Council, DC.
29. Phaneuf, D.J., Smith, V.K., 2005. Recreation demand models. In: Handbook of Environmental Economics. K. North-Holland, 2.
30. Phaneuf, D.J., Kling, C.L., Herriges, J.A., 2000. Estimation and welfare calculations in a generalized corner solution model with an application to recreation demand. Review of Economics and Statistics 82, 83-92.
31. Pinjari, A.R., 2011. Generalized Extreme Value (GEV)-based error structures for multiple discrete-continuous choice models. Transportation Research Part B 45 (3), 474-489.
32. Pinjari, A.R., Bhat, C.R., 2010. A Multiple Discrete-Continuous Nested Extreme Value (MDCNEV) model: formulation and application to non-worker activity time-use and timing behavior on weekdays. Transportation Research Part B 44 (4), 562-583.
33. Santos, A., McGuckin, N., Nakamoto, H.Y., Gray, D., Liss, S., 2011. Summary of Travel Trends: 2009 National Household Travel Survey. Publication FHWA-PLll-022. FHWA, U.S. Department of Transportation.
34. Schwarz, Gideon.E., 1978. Estimating the dimension of a model. Annals of Statistics 6 (2), 461-464.
35. Srinivasan, S., Bhat, C.R., 2005. Modeling household interactions in daily in-home and out-of-home maintenance activity participation. Transportation 32 (5), 523-544.
36. Von Haefen, R.H., 2003. Incorporating observed choice into the construction of welfare measures from random utility models. Environmental Economics & Management 45 (2), 145-165.
37. Von Haefen, R.H., Phaneuf, D.J., 2005. Continuous demand system approaches to nonmarket valuation. In: Applications of Simulation Methods in Environmental & Resource Economics. Springer, Dordrecht.
38. Wales, T.J., Woodland, A.D., 1983. Estimation of consumer demand systems with binding non-negativity constraints. Journal of Econometrics. Elsevier 21 (3), 263-285.