IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v111y2018icp168-184.html
   My bibliography  Save this article

Estimating panel effects in probabilistic representations of dynamic decision trees using bayesian generalized linear mixture models

Author

Listed:
  • Kim, Seheon
  • Rasouli, Soora
  • Timmermans, Harry
  • Yang, Dujuan

Abstract

When collecting panel data, we need to acknowledge that responses do not represent independent measurements. The known apparatus in transportation research offers several opportunities to estimate panel effects for well-known and widely applied models such as hazard and dynamic logit models. However, the transportation research community is not endowed with a rich set of methods to account for panel effects in dynamic probabilistic decision trees, which have been used as a formalism for the representation of decision heuristics. Building on scarce prior work in statistics, we elaborate an approach to estimate panel effects in dynamic probabilistic decision trees with multinomial action states. Given that panel data naturally have a hierarchical structure with repeated measures nested within individuals, we implement a mixed-effects model that simultaneously accounts for population-level effects (fixed effects), between-individual variances (random effects), and within-individual variances (autocorrelations). The approach uses an iterative estimation procedure between CHAID-based probabilistic tree induction and Bayesian generalized linear mixture modeling (GLMM). When extracting the dynamic probabilistic decision trees, it is assumed that the random effects are known, while it is assumed that the fixed effects are known when estimating the Bayesian GLMM. This iterative process continues until convergence is reached. A Monte Carlo technique is used to navigate between aggregate choice probabilities and individual level multinomial choices. We also test the significance of temporal autocorrelation within individuals. The suggested approach is illustrated using charging station choice of users of Plug-in Electric Vehicles (PEV). Results support the potential value of the suggested approach.

Suggested Citation

  • Kim, Seheon & Rasouli, Soora & Timmermans, Harry & Yang, Dujuan, 2018. "Estimating panel effects in probabilistic representations of dynamic decision trees using bayesian generalized linear mixture models," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 168-184.
    • Handle: RePEc:eee:transb:v:111:y:2018:i:c:p:168-184
    DOI: 10.1016/j.trb.2018.03.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191261517310275
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.trb.2018.03.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jianchuan Xianyu & Soora Rasouli & Harry Timmermans, 2017. "Analysis of variability in multi-day GPS imputed activity-travel diaries using multi-dimensional sequence alignment and panel effects regression models," Transportation, Springer, vol. 44(3), pages 533-553, May.
    2. Hadfield, Jarrod D., 2010. "MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i02).
    3. Arentze, Theo A. & Timmermans, Harry J. P., 2004. "A learning-based transportation oriented simulation system," Transportation Research Part B: Methodological, Elsevier, vol. 38(7), pages 613-633, August.
    4. Taha Rashidi & Abolfazl Mohammadian, 2011. "Household travel attributes transferability analysis: application of a hierarchical rule based approach," Transportation, Springer, vol. 38(4), pages 697-714, July.
    5. Cinzia Cirillo & Kay Axhausen, 2010. "Dynamic model of activity-type choice and scheduling," Transportation, Springer, vol. 37(1), pages 15-38, January.
    6. Vij, Akshay, 2013. "Incorporating the Influence of Latent Modal Preferences in Travel Demand Models," University of California Transportation Center, Working Papers qt7ng2z24q, University of California Transportation Center.
    7. Hajjem, Ahlem & Bellavance, François & Larocque, Denis, 2011. "Mixed effects regression trees for clustered data," Statistics & Probability Letters, Elsevier, vol. 81(4), pages 451-459, April.
    8. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, vol. 30(2), pages 133-176, May.
    9. Patrick Sevestre & Laszlo Matyas, 2008. "The Econometrics of Panel Data," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00279977, HAL.
    10. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    11. Rasouli, Soora & Timmermans, Harry, 2013. "Assessment of model uncertainty in destinations and travel forecasts of models of complex spatial shopping behaviour," Journal of Retailing and Consumer Services, Elsevier, vol. 20(2), pages 139-146.
    12. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    13. Kiron Chatterjee, 2011. "Modelling the dynamics of bus use in a changing travel environment using panel data," Transportation, Springer, vol. 38(3), pages 487-509, May.
    14. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    15. Fu, Wei & Simonoff, Jeffrey S., 2015. "Unbiased regression trees for longitudinal and clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 53-74.
    16. Arentze, Theo A. & Timmermans, Harry J.P., 2009. "A need-based model of multi-day, multi-person activity generation," Transportation Research Part B: Methodological, Elsevier, vol. 43(2), pages 251-265, February.
    17. Vij, Akshay, 2013. "Incorporating the Influence of Latent Modal Preferences in Travel Demand Models," University of California Transportation Center, Working Papers qt7nq9p0cv, University of California Transportation Center.
    18. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    19. László Mátyás & Patrick Sevestre (ed.), 2008. "The Econometrics of Panel Data," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75892-1, July-Dece.
    20. Cherchi, Elisabetta & Cirillo, Cinzia & Ortúzar, Juan de Dios, 2017. "Modelling correlation patterns in mode choice models estimated on multiday travel data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 96(C), pages 146-153.
    21. Yang, Dujuan & Timmermans, Harry & Grigolon, Anna, 2013. "Exploring heterogeneity in travel time expenditure of aging populations in the Netherlands: results of a CHAID analysis," Journal of Transport Geography, Elsevier, vol. 33(C), pages 170-179.
    22. Farhana Yasmin & Catherine Morency & Matthew J. Roorda, 2017. "Trend analysis of activity generation attributes over time," Transportation, Springer, vol. 44(1), pages 69-89, January.
    23. Vij, Akshay & Carrel, André & Walker, Joan L., 2013. "Incorporating the influence of latent modal preferences on travel mode choice behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 54(C), pages 164-178.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Su, Shiliang & Wang, Zhuolun & Li, Bozhao & Kang, Mengjun, 2022. "Deciphering the influence of TOD on metro ridership: An integrated approach of extended node-place model and interpretable machine learning with planning implications," Journal of Transport Geography, Elsevier, vol. 104(C).
    2. Yuan, Yalong & Yang, Min & Feng, Tao & Rasouli, Soora & Li, Dawei & Ruan, Xinpei, 2021. "Heterogeneity in passenger satisfaction with air-rail integration services: Results of a finite mixture partial least squares model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 133-158.
    3. Jiajia Zhang & Tao Feng & Harry Timmermans & Zhengkui Lin, 2023. "Improved imputation of rule sets in class association rule modeling: application to transportation mode choice," Transportation, Springer, vol. 50(1), pages 63-106, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Chenfeng Xiong & Di Yang & Jiaqi Ma & Xiqun Chen & Lei Zhang, 2020. "Measuring and enhancing the transferability of hidden Markov models for dynamic travel behavioral analysis," Transportation, Springer, vol. 47(2), pages 585-605, April.
    3. La Paix Puello, Lissy & Chowdhury, Saidul & Geurs, Karst, 2019. "Using panel data for modelling duration dynamics of outdoor leisure activities," Journal of choice modelling, Elsevier, vol. 31(C), pages 141-155.
    4. Chenfeng Xiong & Lei Zhang, 2017. "Dynamic travel mode searching and switching analysis considering hidden model preference and behavioral decision processes," Transportation, Springer, vol. 44(3), pages 511-532, May.
    5. Cherchi, Elisabetta & Cirillo, Cinzia & Ortúzar, Juan de Dios, 2017. "Modelling correlation patterns in mode choice models estimated on multiday travel data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 96(C), pages 146-153.
    6. Wang, Bobin & Shao, Chunfu & Ji, Xun, 2017. "Dynamic analysis of holiday travel behaviour with integrated multimodal travel information usage: A life-oriented approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 104(C), pages 255-280.
    7. Zhou, Heng & Norman, Richard & Xia, Jianhong(Cecilia) & Hughes, Brett & Kelobonye, Keone & Nikolova, Gabi & Falkmer, Torbjorn, 2020. "Analysing travel mode and airline choice using latent class modelling: A case study in Western Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 187-205.
    8. Heinen, Eva & Chatterjee, Kiron, 2015. "The same mode again? An exploration of mode choice variability in Great Britain using the National Travel Survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 266-282.
    9. Saxena, N. & Rashidi, T.H. & Dixit, V.V. & Waller, S.T., 2019. "Modelling the route choice behaviour under stop-&-go traffic for different car driver segments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 62-72.
    10. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.
    11. Sfeir, Georges & Abou-Zeid, Maya & Kaysi, Isam, 2020. "Multivariate count data models for adoption of new transport modes in an organization-based context," Transport Policy, Elsevier, vol. 91(C), pages 59-75.
    12. Ton, Danique & Bekhor, Shlomo & Cats, Oded & Duives, Dorine C. & Hoogendoorn-Lanser, Sascha & Hoogendoorn, Serge P., 2020. "The experienced mode choice set and its determinants: Commuting trips in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 744-758.
    13. Kim, Seheon & Rasouli, Soora, 2022. "The influence of latent lifestyle on acceptance of Mobility-as-a-Service (MaaS): A hierarchical latent variable and latent class approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 304-319.
    14. Li, Zili & Washington, Simon P. & Zheng, Zuduo & Prato, Carlo G., 2023. "A Bayesian hierarchical approach to the joint modelling of Revealed and stated choices," Journal of choice modelling, Elsevier, vol. 47(C).
    15. Große, Juliane & Olafsson, Anton Stahl & Carstensen, Trine Agervig & Fertner, Christian, 2018. "Exploring the role of daily “modality styles” and urban structure in holidays and longer weekend trips: Travel behaviour of urban and peri-urban residents in Greater Copenhagen," Journal of Transport Geography, Elsevier, vol. 69(C), pages 138-149.
    16. Sanjana Hossain & Md. Sami Hasnine & Khandker Nurul Habib, 2021. "A latent class joint mode and departure time choice model for the Greater Toronto and Hamilton Area," Transportation, Springer, vol. 48(3), pages 1217-1239, June.
    17. Lu, Ying & Prato, Carlo G. & Sipe, Neil & Kimpton, Anthony & Corcoran, Jonathan, 2022. "The role of household modality style in first and last mile travel mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 95-109.
    18. Nash, Sean & Mitra, Raktim, 2019. "University students' transportation patterns, and the role of neighbourhood types and attitudes," Journal of Transport Geography, Elsevier, vol. 76(C), pages 200-211.
    19. Olde Kalter, Marie-José & La Paix Puello, Lissy & Geurs, Karst T., 2020. "Do changes in travellers’ attitudes towards car use and ownership over time affect travel mode choice? A latent transition approach in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 1-17.
    20. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transb:v:111:y:2018:i:c:p:168-184. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.