Journal of Transport and Land Use

A data-driven recalibration framework for spatially transferring the vehicle ownership module of an agent-based integrated urban model

Ifratul Hoque

The University of British Columbia Okanagan

Mahmudur Fatmi

The University of British Columbia Okanagan

Muntahith Mehadil Orvin

City of Edmonton

Mohamad Ali Khalil

The University of British Columbia Okanagan

DOI: https://doi.org/10.5198/jtlu.2026.2867

Keywords: Integrated urban models, Vehicle ownership modeling, Spatial transferability, Data-driven modeling, Recalibration

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Abstract

Traditional transferability approaches can update only a single model at a time and are not designed for large systems. This constrains the spatial transferability of Integrated Urban Models, which comprise multiple micro-models. Recalibrating each micro-model independently with existing techniques would require detailed disaggregate data, undermining the practical objective of enhancing transferability. This study, therefore,  proposes a recalibration framework that leverages aggregate data to update all micro-models within a complex modeling system. The framework combines a Steady-State Genetic Algorithm with a Random Forest surrogate model to identify optimal parameter sets. It was applied to the vehicle ownership module of the STELARS model, which represents vehicle transaction and type choice through six micro-models. The module, originally estimated for the Okanagan region, was transferred to the Greater Vancouver Area using only aggregate data sources. The recalibrated model reproduced observed household vehicle ownership distributions within about four percentage points, and 71.6% of the recalibrated parameters remained within 75% of their original estimates, indicating substantial preservation of the underlying behavioral structure. Parameter deviations reflected consistent behavioral differences between the regions rather than model misspecification. The proposed framework offers a scalable and resource-efficient approach to enhancing the spatial transferability of complex urban models.

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References

Afzal, A., Kim, K. Y., & Seo, J. W. (2017). Effects of Latin hypercube sampling on surrogate modeling and optimization. International Journal of Fluid Machinery and Systems, 10(3), 240–253. https://doi.org/10.5293/IJFMS.2017.10.3.240

Agyemang-Duah, K., & Hall, F. L. (1997). Spatial transferability of an ordered response model of trip generation. Transportation Research Part A: Policy and Practice, 31(5), 389–402. https://doi.org/10.1016/S0965-8564(96)00035-3

Arentze, T., Hofman, F., Van Mourik, H., & Timmermans, H. (2002). Spatial transferability of the Albatross model system: Empirical evidence from two case studies. Transportation Research Record, 1805, 1–7. https://doi.org/10.3141/1805-01

Atherton, T. J., & Ben-Akiva, M. (1976). Transferability and updating of disaggregate travel demand models. Transportation Research Record, 610, 12–18. https://trid.trb.org/View/53666

Badoe, D. A., & Miller, E. J. (1995). Analysis of temporal transferability of disaggregate work trip mode choice models. Transportation Research Record, 1493, 1–11. https://trid.trb.org/View/452588

Badoe, D. A., & Miller, E. J. (1998). Modelling mode choice with data from two in-dependent cross-sectional surveys: An investigation. Transportation Planning and Technology, 21(4), 235–261. https://doi.org/10.1080/03081069808717611

BC Assessment. (2025). BC assessment data advice and inventory extracts, 2016–2022 (Version 6) [Data set]. Abacus Data Network.

Ben-Akiva, M., & Bolduc, D. (1987). Approaches to model transferability and updating: The combined transfer estimator. Transportation Research Record, 1139, 1–7.

Ben-Akiva, M., & Morikawa, T. (1990). Estimation of switching models from revealed preferences and stated intentions. Transportation Research Part A: General, 24(6), 485–495. https://doi.org/10.1016/0191-2607(90)90037-7

Bowman, J. L., & Bradley, M. (2017). Testing spatial transferability of activity-based travel forecasting models. Transportation Research Record, 2669, 62–71. https://doi.org/10.3141/2669-07

Bowman, J. L., Bradley, M., Castiglione, J., & Yoder, S. L. (2014). Making advanced travel forecasting models affordable through model transferability. Paper presented at the 93rd Annual Meeting of the Transportation Research Board, Washing-ton, DC.

Bradley, M. A., & Daly, A. J. (1991, May). Estimation of logit choice models using mixed stated preference and revealed preference information. Paper presented at the 6th International Conference on Travel Behaviour, Quebec, Canada.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

Deb, K., & Agrawal, R. B. (1995). Simulated binary crossover for continuous search space. Complex Systems, 9(2), 115–148.

Fatmi, M. R., & Ahmed, M. (2025). Investigating activity, travel and time use patterns by work arrangements: Evidence from the BC ATUS – Wave 1. Paper presented at the 104th Annual Meeting of the Transportation Research Board, Washington, DC.

Fatmi, M. R., & Habib, M. A. (2015). Spatial transferability of a microresidential mobility model in the Integrated Land Use, Transportation, and Environment modeling system. Transportation Research Record, 2494, 29–36. https://doi.org/10.3141/2494-04

Gunn, H. F., Ben-Akiva, M., & Bradley, M. A. (1985). Tests of the scaling approach to transferring disaggregate travel demand models. Transportation Research Record, 1037, 21–30. https://trid.trb.org/view/272223

Hoque, I. (2025). Data-driven recalibration methodology for spatially transferring the vehicle ownership module of an agent-based integrated urban model [Master’s thesis, University of British Columbia]. UBC Theses and Dissertations. https://doi.org/10.14288/1.0451067

Hossain, M. S., Fatmi, M. R., & Enam, A. (2026). What type of vehicles do house-holds own? A joint model for vehicle body, vintage, fuel, and advanced driving assistive systems. Transportation Letters, 1–23. https://doi.org/10.1080/19427867.2026.2669759

Hossain, M. S., Fatmi, M. R., & Khalil, M. A. (2026). Microsimulation of vehicle ownership decisions within an agent-based integrated urban model: An event-based hybrid of continuous and discrete simulation approach. Journal of Transport and Land Use, 19(1), 79–106. https://doi.org/10.5198/jtlu.2026.2665

Insurance Corporation of British Columbia. (2025). Vehicle population dataset [Data set]. https://public.tableau.com/app/profile/icbc/viz/VehiclePopulationIntroPage/VehiclePopulationData

Jin, Y. (2011). Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm and Evolutionary Computation, 1(2), 61–70. https://doi.org/10.1016/j.swevo.2011.05.001

Karasmaa, N. (2007). Evaluation of transfer methods for spatial travel demand models. Transportation Research Part A: Policy and Practice, 41(5), 411–427. https://doi.org/10.1016/j.tra.2006.09.009

Karasmaa, N., & Pursula, M. (1997). Empirical studies of transferability of Helsinki metropolitan area travel forecasting models. Transportation Research Record, 1607, 38–44. https://doi.org/10.3141/1607-06

Kavta, K., Bösehans, G., Bell, M. C., Liao, F., de Almeida Correia, G. H., & Dissanayake, D. (2024). Assessing the spatial transferability of mode choice models: A case of shared electric mobility hubs (eHUBS) in Amsterdam and Manchester. Transport Policy, 156, 101–111. https://doi.org/10.1016/j.tranpol.2024.07.017

Khaddar, S., Varghese, V., Fatmi, M. R., & Chikaraishi, M. (2024). Jointly modelling activity start time, travel mode, companionship, and destination location choices. Transportmetrica A: Transport Science, 22(1). https://doi.org/10.1080/23249935.2024.2372025

Khalil, M. A. (2025). Towards a next generation of integrated urban models: Advanced machine learning and microsimulation modelling for transportation and urban systems [Doctoral dissertation, University of British Columbia]. UBC Theses and Dissertations. https://doi.org/10.14288/1.0449896

Khalil, M. A., & Fatmi, M. R. (2023). Modeling daily in-home activities using machine learning techniques. Travel Behaviour and Society, 31, 374–385. https://doi.org/10.1016/j.tbs.2023.02.001

Khalil, M. A., Fatmi, M. R., & Orvin, M. M. (2025). Developing and microsimulating demographic dynamics for an integrated urban model: A comparison between logistic regression and machine learning techniques. Transportation, 52(4), 1621–1655. https://doi.org/10.1007/s11116-024-10468-7

Koppelman, F. S., Kuah, G.-K., & Wilmot, C. G. (1985). Transfer model updating with disaggregate data. Transportation Research Record, 1037, 102–107.

Koppelman, F. S., & Rose, G. (1985). Geographic transfer of travel choice models: Evaluation and procedures. In B. G. Hutchinson, P. Nijkamp, & M. Batty (Eds.), Optimization and discrete choice in urban systems (pp. 272–309). Springer. https://doi.org/10.1007/978-3-642-51020-5_16

Koppelman, F. S., & Wilmot, C. G. (1982). Transferability analysis of disaggregate choice models. Transportation Research Record, 895, 18–24. https://trid.trb.org/View/189355

Koppelman, F. S., & Wilmot, C. G. (1986). The effect of omission of variables on choice model transferability. Transportation Research Part B: Methodological, 20(3), 205–213. https://doi.org/10.1016/0191-2615(86)90017-2

Linh, H. T., Adnan, M., Ectors, W., Kochan, B., Bellemans, T., & 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. https://doi.org/10.1016/j.procs.2019.04.033

McKay, M. (1992). Latin hypercube sampling as a tool in uncertainty analysis of computer models. Proceedings of the 24th Conference on Winter Simulation (pp. 557–564). Association for Computing Machinery. https://www.osti.gov/biblio/7196246

Mohammadian, A., & Miller, E. J. (2003). Dynamic modeling of household automobile transactions. Transportation Research Record, 1831, 98–105. https://doi.org/10.3141/1831-11

Nohekhan, A., Samimi, A., & Zahedian, S. (2022). Spatial transferability of a daily activity type and duration MDCEV model. Civil Engineering Infrastructures Journal, 55(1), 161–181. https://doi.org/10.22059/CEIJ.2021.313889.1721

Orvin, M. M., Enam, A., & Fatmi, M. R. (2024). A joint model of residential mobility and location choice decisions. International Journal of Urban Sciences. https://doi.org/10.1080/12265934.2024.2358415

Orvin, M. M., & Fatmi, M. R. (2024). A residential location search model based on the reasons for moving out. Transportation Letters, 16(6), 566–580. https://doi.org/10.1080/19427867.2023.2222990

Orvin, M. M., Fatmi, M. R., & Hoque, I. (2025). Housing price dynamics within an integrated urban model: Testing the spatial and temporal transferability. International Journal of Urban Sciences. https://doi.org/10.1080/12265934.2025.2517097

Rahman, M. N., & Fatmi, M. R. (2023). Population synthesis accommodating heterogeneity: A Bayesian network and generalized raking technique. Transportation Research Record, 2677(6), 41–57. https://doi.org/10.1177/03611981221144289

Rashidi, T. H., Auld, J., & Mohammadian, A. (2013). Effectiveness of Bayesian up-dating attributes in data transferability applications. Transportation Research Record, 2344, 1–9. https://doi.org/10.3141/2344-01

Roorda, M. J., Mohammadian, A., & Miller, E. J. (2000). Toronto area car ownership study: A retrospective interview and its applications. Transportation Research Record, 1719, 69–76. https://doi.org/10.3141/1719-09

Salvini, P., & Miller, E. J. (2005). ILUTE: An operational prototype of a comprehensive microsimulation model of urban systems. Networks and Spatial Economics, 5(2), 217–234. https://doi.org/10.1007/s11067-005-2630-5

Santoso, D. S., & Tsunokawa, K. (2005). Transferability and updating analysis of mode choice model between two developing countries: Predictive performance point of view. Journal of the Eastern Asia Society for Transportation Studies, 6, 173–185. https://doi.org/10.11175/easts.6.173

Santoso, D. S., & Tsunokawa, K. (2010). Comparison of updating techniques in transferability analysis of work trip mode choice models in developing countries. Journal of Advanced Transportation, 44(2), 89–102. https://doi.org/10.1002/atr.112

Sikder, S., Augustin, B., Pinjari, A. R., & Eluru, N. (2014). Spatial transferability of tour-based time-of-day choice models. Transportation Research Record, 2429, 99–109. https://doi.org/10.3141/2429-11

Sikder, S., & Pinjari, A. R. (2013). Spatial transferability of person-level daily activity generation and time use models. Transportation Research Record, 2343, 95–104. https://doi.org/10.3141/2343-12

Sikder, S., Pinjari, A. R., Srinivasan, S., & 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), 104–128. https://doi.org/10.1007/s12572-013-0090-6

Tang, L., Xiong, C., & Zhang, L. (2018). Spatial transferability of neural network models in travel demand modeling. Journal of Computing in Civil Engineering, 32(3), 04018010. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000752

UBC integrated Transportation Research Laboratory. (2022). Travel technology and mobility survey (TTMS). https://uitr.ok.ubc.ca/2022/04/28/travel-technology-and-mobility-survey-ttms/

UBC integrated Transportation Research Laboratory. (2024). British Columbia activity time use survey [Data set]. https://survey.uitrlab.ok.ubc.ca/v2.0/

Waddell, P. (2002). UrbanSim: Modeling urban development for land use, transportation, and environmental planning. Journal of the American Planning Association, 68(3), 297–314. https://doi.org/10.1080/01944360208976274

Walker, W. T., Brady, S. H., & Taylor, C. (1997). Updating existing travel simulation models with small-sample survey data using parameter scaling methods. Transportation Research Record, 1607, 55–61. https://doi.org/10.3141/1607-08

Wang, H., & Jin, Y. (2020). A random forest-assisted evolutionary algorithm for data-driven constrained multiobjective combinatorial optimization of trauma systems. IEEE Transactions on Cybernetics, 50(2), 536–549. https://doi.org/10.1109/TCYB.2018.2869674

Whitley, D. (1994). A genetic algorithm tutorial. Statistics and Computing, 4(2), 65–85. https://doi.org/10.1007/BF00175354

Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82. https://doi.org/10.3354/cr030079

Xu, C., Wang, W., Liu, P., Guo, R., & Li, Z. (2014). Using the Bayesian updating approach to improve the spatial and temporal transferability of real-time crash risk prediction models. Transportation Research Part C: Emerging Technologies, 38, 167–176. https://doi.org/10.1016/j.trc.2013.11.020

Yasmin, F., Morency, C., & Roorda, M. J. (2015). Assessment of spatial transferability of an activity-based model, TASHA. Transportation Research Part A: Policy and Practice, 78, 200–213. https://doi.org/10.1016/j.tra.2015.05.008

Zhang, Y., & Mohammadian, A. (2008). Bayesian updating of transferred household travel data. Transportation Research Record, 2049, 111–118. https://doi.org/10.3141/2049-13

Zheng, Y., Fu, X., & Xuan, Y. (2019). Data-driven optimization based on random forest surrogate. 2019 6th International Conference on Systems and Informatics, (pp. 487–491). ICSAI. https://doi.org/10.1109/ICSAI48974.2019.9010547