Multinomial probit with structured covariance for route choice behavior

Transportation Research Part B: Methodological

Volumn 31, Issue 3 PART B, 1997, Pages 195-207

  Yai, Tetsuo ^a   Iwakura, Seiji ^b   Morichi, Shigeru ^c  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

^b Japan Center for Economic Research   (Japan)

^c UNIVERSITY OF TOKYO   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; MATHEMATICAL MODELS; MATRIX ALGEBRA; OPERATIONS RESEARCH; TREES (MATHEMATICS);

MULTINOMIAL PROBIT MODELS; STRUCTURED COVARIANCE MATRIX;

TRANSPORTATION ROUTES;

METHODOLOGICAL EVALUATION; MODELLING APPROACH; MULTINOMIAL PROBIT MODEL; PLANNING TECHNIQUE; ROUTE CHOICE; STRUCTURED COVARIANCE; TRANSPORT PLANNING; TRAVEL BEHAVIOUR;

EID: 0030615384     PISSN: 01912615     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0191-2615(96)00025-2     Document Type: Article

References (18)

1. Bolduc, D. (1992) Generalized autoregressive errors in the multinomial probit model. Transportation Research B 26, 155-170.
2. Bolduc, D. and Ben-Akiva, M. E. (1991) A multinomial probit formulation for large choice set. 6th International Conference on Travel Behavior, pp. 244-258.
3. Bunch, D. S. (1991) Estimability in the multinomial probit model. Transportation Research B 25, 1-12.
4. Daganzo, C. (1977) Multinomial probit and qualitative choice: a computationally efficient algorithm. Transportation Science 11, 338-358.
5. Daganzo, C. (1979) Multinomial Probit. Academic Press, New York.
6. Daganzo, C. F. and Sheffi, Y. (1982) Multinomial probit with time-series data: unifying state dependence and serial correlation models. Environment and Planning A 14, 1377-1388.
7. Geweke, L., Keane, M. and Runkle, D. (1994) Alternative computational approaches to inference in the multinomial probit model. The Review of Economics and Statistics, 609-632.
8. Hausman, J. A. and Wise, D. A. (1978) A conditional probit model for qualitative choice: discrete decisions recognizing interdependence and heterogeneous preferences. Econometrica 46, 403-426.
9. Horowitz, J., L (1991) Reconsidering the multinomial probit model. Transportation Research B 25, 433-438.
10. Horowitz, J., L, Sparmann, J. M. and Daganzo, C. F. (1982) An investigation of the accuracy of the Clark approximation for the multinomial probit model. Transportation Science 16, 382-401.
11. Lerman, S. and Manski, C. (1981) On the use of simulated frequencies to approximate choice probabilities. In Structural Analysis of Discrete Data with Econometric Applications, eds C. Manski and D. McFadden. MIT Press, Cambridge.
12. McFadden, D. (1989) A method of simulated moments for estimation of discrete response models without numerical integration. Econometrica 57, 995-1026.
13. Pakes, A. and Pollard, D. (1989) Simulation and the asymptotics of optimization estimators. Econometrica 57, 1027-1057.
14. Sheffi, Y., Hall, R. and Daganzo, C. (1982) On the estimation of the multinomial probit model. Transportation Research A 16, 447-456.
15. Williams, H. C. W. L. (1977) On the formation of travel demand models and economic evaluation measures of user benefit. Environment and Planning A 9, 285-344.
16. Yai, T. (1989) Disaggregate behavioral models and their applications in Japan. Transportation Research A 23, 45-52.
17. Yai, T. and Iwakura, S. (1994) Route choice modeling and investment effects upon a metropolitan rail network. Preprints of the 7th International Conference on Travel Behavior 1, 363-374.
18. Yai, T., Iwakura, S. and Ito, M. (1993) Alternative approaches in the estimation of user demand and surplus of rail network (in Japanese). Infrastructure Planning Review 11, 81-88.