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Introducing synthetic pseudo panels: application to transport behaviour dynamics

Author

Listed:
  • Stanislav S. Borysov

    (Technical University of Denmark (DTU))

  • Jeppe Rich

    (Technical University of Denmark (DTU))

Abstract

In this paper, a method to study travel behaviour dynamics by constructing detailed synthetic pseudo panels from repeated cross-sectional data is presented. The method is based on the modelling of a high-dimensional joint distribution of travel preferences conditional on detailed socio-economic profiles by using a conditional variational autoencoder (CVAE). The CVAE is a neural-network-based generative model which allows the modelling of very detailed joint and conditional distributions, potentially defined by dozens or even hundreds of attributes in a flexible non-parametric form. The proposed method is used to rank detailed cohorts of individuals into slow and fast movers with respect to the speed at which their travel behaviour change over time. This gives an interesting insight into the types of individuals who are easily motivated to change their behaviour as opposed to those who are less flexible. Specifically, we investigate the dynamics of transport preferences for a fixed pseudo panel of individuals from a large Danish cross-sectional data set covering the period from 2006 to 2016. The comparison of the travel preference distributions from 2006 and 2016 shows that the prototypical fast mover is a single young woman who lives in a large city, whereas the typical slow mover is a middle-aged man with high income from a nuclear family who lives in a detached house outside a city. However, given that it is possible to rank individuals across very detailed socio-economic classifications, many other relationships can be explored. Finally, the CVAE can be directly applied to the population synthesis problem in microsimulation by modelling the distribution of socio-economic profiles conditional on other variables.

Suggested Citation

  • Stanislav S. Borysov & Jeppe Rich, 2021. "Introducing synthetic pseudo panels: application to transport behaviour dynamics," Transportation, Springer, vol. 48(5), pages 2493-2520, October.
    • Handle: RePEc:kap:transp:v:48:y:2021:i:5:d:10.1007_s11116-020-10137-5
    DOI: 10.1007/s11116-020-10137-5
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    1. Müggenburg, Hannah & Busch-Geertsema, Annika & Lanzendorf, Martin, 2015. "Mobility biographies: A review of achievements and challenges of the mobility biographies approach and a framework for further research," Journal of Transport Geography, Elsevier, vol. 46(C), pages 151-163.
    2. Dargay, Joyce M., 2002. "Determinants of car ownership in rural and urban areas: a pseudo-panel analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 38(5), pages 351-366, September.
    3. Goulias, Konstadinos G., 1999. "Longitudinal analysis of activity and travel pattern dynamics using generalized mixed Markov latent class models," Transportation Research Part B: Methodological, Elsevier, vol. 33(8), pages 535-558, November.
    4. McFadden, Daniel, 2007. "The behavioral science of transportation," Transport Policy, Elsevier, vol. 14(4), pages 269-274, July.
    5. Huang, Biao, 2007. "The Use of Pseudo Panel Data for Forecasting Car Ownership," MPRA Paper 7086, University Library of Munich, Germany.
    6. François Gardes & Greg J, Duncan & Patrice Gaubert & Christophe Starzec, 2002. "Panel and Pseudo-Panel Estimation of Cross-Sectional and Time Series Elasticities of Food Consumption : The Case of American and Polish Data," Working Papers 2002-02, Center for Research in Economics and Statistics.
    7. Cinzia Cirillo & Renting Xu & Fabian Bastin, 2016. "A Dynamic Formulation for Car Ownership Modeling," Transportation Science, INFORMS, vol. 50(1), pages 322-335, February.
    8. Saadi, Ismaïl & Mustafa, Ahmed & Teller, Jacques & Farooq, Bilal & Cools, Mario, 2016. "Hidden Markov Model-based population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 1-21.
    9. Schoenduwe, Robert & Mueller, Michel G. & Peters, Anja & Lanzendorf, Martin, 2015. "Analysing mobility biographies with the life course calendar: a retrospective survey methodology for longitudinal data collection," Journal of Transport Geography, Elsevier, vol. 42(C), pages 98-109.
    10. Francisca Antman & David J. McKenzie, 2007. "Earnings Mobility and Measurement Error: A Pseudo-Panel Approach," Economic Development and Cultural Change, University of Chicago Press, vol. 56(1), pages 125-161, October.
    11. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    12. Deaton, Angus, 1985. "Panel data from time series of cross-sections," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 109-126.
    13. Metz, D. H., 2000. "Mobility of older people and their quality of life," Transport Policy, Elsevier, vol. 7(2), pages 149-152, April.
    14. Farooq, Bilal & Bierlaire, Michel & Hurtubia, Ricardo & Flötteröd, Gunnar, 2013. "Simulation based population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 243-263.
    15. Sun, Lijun & Erath, Alexander & Cai, Ming, 2018. "A hierarchical mixture modeling framework for population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 199-212.
    16. Verbeek, Marno & Nijman, Theo, 1993. "Minimum MSE estimation of a regression model with fixed effects from a series of cross-sections," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 125-136, September.
    17. Dellas, Harris & Koubi, Vally, 2003. "Business cycles and schooling," European Journal of Political Economy, Elsevier, vol. 19(4), pages 843-859, November.
    18. Nolan, Anne, 2010. "A dynamic analysis of household car ownership," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(6), pages 446-455, July.
    19. Gardes, Francois & Duncan, Greg J. & Gaubert, Patrice & Gurgand, Marc & Starzec, Christophe, 2005. "Panel and Pseudo-Panel Estimation of Cross-Sectional and Time Series Elasticities of Food Consumption: The Case of U.S. and Polish Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 242-253, April.
    20. Mau, Paulus & Eyzaguirre, Jimena & Jaccard, Mark & Collins-Dodd, Colleen & Tiedemann, Kenneth, 2008. "The 'neighbor effect': Simulating dynamics in consumer preferences for new vehicle technologies," Ecological Economics, Elsevier, vol. 68(1-2), pages 504-516, December.
    21. Kitamura, Ryuichi, 1990. "Panel Analysis in Transportation Planning: An Overview," University of California Transportation Center, Working Papers qt86v0f7zh, University of California Transportation Center.
    22. de Haas, M.C. & Scheepers, C.E. & Harms, L.W.J. & Kroesen, M., 2018. "Travel pattern transitions: Applying latent transition analysis within the mobility biographies framework," Transportation Research Part A: Policy and Practice, Elsevier, vol. 107(C), pages 140-151.
    23. Sonja Haustein & Anu Siren, 2015. "Older People's Mobility: Segments, Factors, Trends," Transport Reviews, Taylor & Francis Journals, vol. 35(4), pages 466-487, July.
    24. Rich, Jeppe & Vandet, Christian Anker, 2019. "Is the value of travel time savings increasing? Analysis throughout a financial crisis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 145-168.
    25. Chenfeng Xiong & Xiqun Chen & Xiang He & Wei Guo & Lei Zhang, 2015. "The analysis of dynamic travel mode choice: a heterogeneous hidden Markov approach," Transportation, Springer, vol. 42(6), pages 985-1002, November.
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