Spatial Transferability of a Daily Activity Type and Duration MDCEV Model

Document Type : Research Papers


1 Ph.D. Candidate, Department of Civil and Environmental Engineering, University of Maryland, Maryland, United States of America.

2 Associate Professor, Department of Civil Engineering, Sharif University of Technology, Tehran, Iran.


This paper explores the transferability of the Multiple Discrete-Continuous Extreme Value (MDCEV) model for activity type and duration using various transfer methods and sample sizes. This study employs the data of travel demand studies in two major cities, Shiraz and Mashhad in Iran. The model is first developed for Shiraz and then transferred to Mashhad. The adopted transfer methods are transfer scaling, Bayesian updating, combined transfer estimation, and joint context estimation. Aggregate and disaggregate transfer measures are adopted to examine the transferred models' general prediction and policy predictability. The results indicate the joint context estimation method's superiority in terms of estimation and policy prediction powers. The available massive data to the authors enabled measuring the value of sample size in this study. The sample size sensitivity analysis revealed a decrease in the marginal gain of the transferred model's performance as the sample size increases. Remarkably, the transferred model outperforms even the locally estimated model when 1) advanced transfer techniques are applied (i.e., the combined transfer estimation and the joint context estimation), and 2) the application context sample size is large enough (i.e., more than 30 percent).


Abdelwahab, W.M. (1991). “Transferability of intercity disaggregate mode choice models in Canada”, Canadian Journal of Civil Engineering, 18(1), 20-26.
Arentze, T., Hofman, F., Van Mourik, H. and Timmermans, H. (2002). “Spatial transferability of the Albatross model system: Empirical evidence from two case studies”, Transportation Research Record, 1805, 1-7.
Atherton, T.J. and Ben-Akiva, M.E. (1976). “Transferability and updating of disaggregate travel demand models”, Transportation Research Record, 610, 12-18.
Badoe, D.A. and Miller, E.J. (1995). “Comparison of alternative methods for updating disaggregate logit mode choice models”, Transportation Research Record, 1493, 90-100.
Barkhordari, K. and Entezari Zarch, H. (2015). “Prediction of permanent earthquake-induced deformation in earth dams and embankments using artificial neural networks”, Civil Engineering Infrastructures Journal, 48(2), 271-283.
Ben-Akiva, M. and Morikawa, T. (1990). “Estimation of switching models from revealed preferences and stated intentions”, Transportation Research Part A: General, 24(6), 485-495.
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: Methodological, 42(3), 274-303.
Bhat, C.R. (2018). “A new flexible multiple discrete-continuous extreme value (MDCEV) choice model”, Transportation Research Part B: Methodological, 110, 261-279.
Bowman, J.L. and Bradley, M. (2017). “Testing spatial transferability of activity-based travel forecasting models”, Transportation Research Record, 2669(1), 62-71.
Bowman, J.L., Bradley, M., Castiglione, J. and Yoder, S.L. (2014). “Making advanced travel forecasting models affordable through model transferability”, In: Preseted at the 93rd Annual Meeting of Transportation Research Board, Washington, DC.
Karasmaa, N. (2003). “The transferability of travel demand models: An analysis of transfer methods, data quality and model estimation”, Ph.D. Thesis, Helsinki University of Technology.
Karimaee Tabarestani, M. and Zarrati, A.R. (2015). “Design of riprap stone around bridge piers using empirical and neural network method”, Civil Engineering Infrastructures Journal, 48(1), 175-188.
Koppelman, F.S. and Wilmot, C.G. (1982). “Transferability analysis of disaggregate choice models”, Transportation Research Record, 895, 18-24.
Lefebvre-Ropars, G., Morency, C., Singleton, P.A. and Clifton, K.J. (2017). “Spatial transferability assessment of a composite walkability index: The pedestrian index of the environment (PIE)”, Transportation Research Part D: Transport and Environment, 57, 378-391.
Linh, H.T., Adnan, M., Ectors, W., Kochan, B., Bellemans, T. and Tuan, V.A. (2019). “Exploring the spatial transferability of FEATHERS, An activity based travel demand model for Ho Chi Minh City, Vietnam”, Procedia Computer Science, 151, 226-233.
Mondal, A. and Bhat, C.R. (2021). “A new closed form multiple discrete-continuous extreme value (MDCEV) choice model with multiple linear constraints”, Transportation Research Part B: Methodological, 147, 42-66.
Nohekhan, A., Zahedian, S. and Haghani, A. (2021). A deep learning model for off-ramp hourly traffic volume estimation, Transportation Research Record, 03611981211027151.
Nowrouzian, R. and Srinivasan, S. (2012). “Empirical analysis of spatial transferability of tour-generation models”, Transportation Research Record: Journal of the Transportation Research Board, (2302), 14-22.
Pinjari, A.R. and Bhat, C. (2010). “An efficient forecasting procedure for Kuhn-Tucker consumer demand model systems”, Technical Paper, Department of Civil and Environmental Engineering, University of South Florida.
Rossi, T.F. and Bhat, C.R. (2014). Guide for travel model transfer, Report No. FHWA-HEP-15-006, Federal Highway Administration: Washington, DC, USA.
Salem, S. and Nurul Habib, K.M. (2015). “Use of repeated cross-sectional travel surveys to develop a meta model of activity-travel generation process models: Accounting for changing preference in time expenditure choices”, Transportmetrica A: Transport Science, 11(8), 729-749.
Shabanpour, R., Golshani, N., Stephens, T.S., Auld, J. and Mohammadian, A. (2019). “Developing a spatial transferability platform to analyze national-level impacts of connected automated vehicles”, In: The Practice of Spatial Analysis, pp. 253-272, Springer, Cham.
Shamshiripour, A. and Samimi, A. (2019). “Estimating a mixed-profile MDCEV: Case of daily activity type and duration”, Transportation Letters, 11(6), 289-302.
Sikder, S. and Pinjari, A. (2013). “Spatial transferability of person-level daily activity generation and time use models: Empirical assessment”, Transportation Research Record: Journal of the Transportation Research Board, (2343), 95-104.
Sikder, S., Pinjari, A.R., Srinivasan, S. and Nowrouzian, R. (2013). “Spatial transferability of travel forecasting models: A review and synthesis”, International Journal of Advances in Engineering Sciences and Applied Mathematics, 5(2-3), 104-128.
Tang, L., Xiong, C. and Zhang, L. (2018). “Spatial transferability of neural network models in travel demand modeling”, Journal of Computing in Civil Engineering, 32(3), 04018010.
Train, K.E. (2009). Discrete choice methods with simulation, Cambridge University Press.
Wafa, Z., Bhat, C.R., Pendyala, R.M. and Garikapati, V.M. (2015). “Latent-segmentation-based approach to investigating spatial transferability of activity-travel models”, Transportation Research Record: Journal of the Transportation Research Board, (2493), 136-144.
Xiong, C., Yang, D., Ma, J., Chen, X. and Zhang, L. (2020). “Measuring and enhancing the transferability of hidden Markov models for dynamic travel behavioral analysis”, Transportation, 47(2), 585-605.
Yasmin, F., Morency, C. and Roorda, M.J. (2015). “Assessment of spatial transferability of an activity-based model, TASHA”, Transportation Research Part A: Policy and Practice, 78, 200-213.
Zahedian, S., SekuĊ‚a, P., Nohekhan, A. and Vander Laan, Z. (2020). “Estimating hourly traffic volumes using artificial neural network with additional inputs from automatic traffic recorders”, Transportation Research Record, 2674(3), 272-282.
Ziemke, D., Nagel, K. and Bhat, C. (2015). “Integrating CEMDAP and MATSim to increase the transferability of transport demand models”, Transportation Research Record, 2493(1), 117-125.