Multitask learning deep neural networks to combine revealed and stated preference data
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DOI: 10.1016/j.jocm.2020.100236
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- Patricia K. Lyon, 1984. "Time-Dependent Structural Equations Modeling: A Methodology for Analyzing the Dynamic Attitude-Behavior Relationship," Transportation Science, INFORMS, vol. 18(4), pages 395-414, November.
- Yann LeCun & Yoshua Bengio & Geoffrey Hinton, 2015. "Deep learning," Nature, Nature, vol. 521(7553), pages 436-444, May.
- Golob, Thomas F. & McNally, Michael G., 1997. "A Model of Activity Participation Between Household Heads," University of California Transportation Center, Working Papers qt4dj8f1gg, University of California Transportation Center.
- Golob, Thomas F. & Bunch, David S. & Brownstone, David, 1997. "A Vehicle Use Forecasting Model Based on Revealed and Stated Vehicle Type Choice and Utilisation Data," University of California Transportation Center, Working Papers qt2x86k20c, University of California Transportation Center.
- Jerry Hausman, 2001. "Mismeasured Variables in Econometric Analysis: Problems from the Right and Problems from the Left," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 57-67, Fall.
- Golob, Thomas F., 2003. "Structural equation modeling for travel behavior research," Transportation Research Part B: Methodological, Elsevier, vol. 37(1), pages 1-25, January.
- Golob, Thomas F & Bunch, David S & Brownstone, David, 1997. "A Vehicle Use Forecasting Model Based on Revealed and Stated Vehicle Type Choice and Utilisation Data," University of California Transportation Center, Working Papers qt2bz335vw, University of California Transportation Center.
- Jonathan Cohen & Keith Marzilli Ericson & David Laibson & John Myles White, 2020.
"Measuring Time Preferences,"
Journal of Economic Literature, American Economic Association, vol. 58(2), pages 299-347, June.
- Jonathan D. Cohen & Keith Marzilli Ericson & David Laibson & John Myles White, 2016. "Measuring Time Preferences," NBER Working Papers 22455, National Bureau of Economic Research, Inc.
- Yves Bentz & Dwight Merunka, 2000. "Neural networks and the multinomial logit for brand choice modelling: a hybrid approach," Post-Print hal-01822273, HAL.
- Train,Kenneth E., 2009.
"Discrete Choice Methods with Simulation,"
Cambridge Books,
Cambridge University Press, number 9780521747387, October.
- Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, October.
- Kenneth Train, 2003. "Discrete Choice Methods with Simulation," Online economics textbooks, SUNY-Oswego, Department of Economics, number emetr2.
- Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998.
"Misclassification of the dependent variable in a discrete-response setting,"
Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
- Hausman, J.A. & Morton, F.M.S., 1994. "Misclassification of Dependent Variable in a Discrete Response Setting," Working papers 94-19, Massachusetts Institute of Technology (MIT), Department of Economics.
- Kenneth Train, 1980. "A Structured Logit Model of Auto Ownership and Mode Choice," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(2), pages 357-370.
- Golob, Thomas F. & McNally, Michael G., 1997. "A model of activity participation and travel interactions between household heads," Transportation Research Part B: Methodological, Elsevier, vol. 31(3), pages 177-194, June.
- Mozolin, M. & Thill, J. -C. & Lynn Usery, E., 2000. "Trip distribution forecasting with multilayer perceptron neural networks: A critical evaluation," Transportation Research Part B: Methodological, Elsevier, vol. 34(1), pages 53-73, January.
- Stephane Hess & John Rose, 2009. "Should Reference Alternatives in Pivot Design SC Surveys be Treated Differently?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 42(3), pages 297-317, March.
- Helveston, John Paul & Feit, Elea McDonnell & Michalek, Jeremy J., 2018. "Pooling stated and revealed preference data in the presence of RP endogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 70-89.
- Train, Kenneth & Wilson, Wesley W., 2008. "Estimation on stated-preference experiments constructed from revealed-preference choices," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 191-203, March.
- Small, K. & Winston, C., 1998. ""The Demand for Transportation: Models and Applications"," Papers 98-99-6, California Irvine - School of Social Sciences.
- Ye, Xin & Pendyala, Ram M. & Gottardi, Giovanni, 2007. "An exploration of the relationship between mode choice and complexity of trip chaining patterns," Transportation Research Part B: Methodological, Elsevier, vol. 41(1), pages 96-113, January.
- Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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- Shenhao Wang & Baichuan Mo & Jinhua Zhao, 2020. "Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks," Papers 2010.11644, arXiv.org.
- Wang, Shenhao & Mo, Baichuan & Zhao, Jinhua, 2021. "Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 333-358.
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Keywords
Multitask learning deep neural network; Machine learning; Revealed preference; Stated preference; Autonomous vehicles;All these keywords.
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