IDEAS home Printed from https://ideas.repec.org/a/eee/eejocm/v56y2025ics1755534525000260.html

A method to integrate strategic alignment in freight transportation behavioral models

Author

Listed:
  • Stinson, Monique
  • Mohammadian, Abolfazl (Kouros)

Abstract

Companies use high-level strategies to guide their decision-making and maintain strategic alignment in their actions. For example, companies may adopt a strategy of providing excellent customer service and own a private truck fleet, giving the company complete control over delivery. Despite its relevance, the concept of strategic alignment is a major omission in existing freight transportation models. In this study, we develop a methodology to integrate strategic alignment into agent-based, freight transportation models. We first identify a suitable modification to the typical agent-based structure, then outline a conceptual model relating strategy to strategic decisions. We develop a mathematical formulation to operationalize the conceptual model by introducing latent variables, which represent strategies, into the Seemingly Unrelated Regression (SUR) formulation, permitting a mix of continuous and Tobit equations. The new method is named SURTLV (Seemingly Unrelated Regression of Tobit Equations with Latent Variables). Our methodology offers many powerful features for forecasting. Binary, continuous, and contingent decisions are modeled. Choice set generation parameters are modeled as strategic decisions. Strategic decisions are modeled jointly, which acknowledges their interrelationships. Bayesian estimation with Gibbs sampling supports rich model specifications. In an empirical demonstration, we apply SURTLV to simulate a nationwide network of distribution centers and private fleets using real-world data of Fortune 500 companies. Our latent strategy measurement data come from parallel work, featuring the first real-world implementation of a novel, Natural Language Processing-based measurement generation method.

Suggested Citation

  • Stinson, Monique & Mohammadian, Abolfazl (Kouros), 2025. "A method to integrate strategic alignment in freight transportation behavioral models," Journal of choice modelling, Elsevier, vol. 56(C).
    • Handle: RePEc:eee:eejocm:v:56:y:2025:i:c:s1755534525000260
    DOI: 10.1016/j.jocm.2025.100563
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1755534525000260
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jocm.2025.100563?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Pellegrini, Andrea & Rose, John Matthew, 2025. "On allowing endogenous minimum consumption bounds in the multiple discrete continuous choice model: An application to expenditure patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 193(C).
    2. Bhat, Chandra R., 2015. "A comprehensive dwelling unit choice model accommodating psychological constructs within a search strategy for consideration set formation," Transportation Research Part B: Methodological, Elsevier, vol. 79(C), pages 161-188.
    3. Fang, Hao Audrey, 2008. "A discrete-continuous model of households' vehicle choice and usage, with an application to the effects of residential density," Transportation Research Part B: Methodological, Elsevier, vol. 42(9), pages 736-758, November.
    4. Bhat, Chandra R., 2015. "A new generalized heterogeneous data model (GHDM) to jointly model mixed types of dependent variables," Transportation Research Part B: Methodological, Elsevier, vol. 79(C), pages 50-77.
    5. Astroza, Sebastian & Pinjari, Abdul & Bhat, Chandra & Jara-Diaz, Sergio, 2017. "A Microeconomic Theory–Based Latent Class Multiple Discrete–Continuous Choice Model of Time Use and Goods Consumption," MPRA Paper 92574, University Library of Munich, Germany.
    6. Bhat, Chandra R., 2011. "The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 923-939, August.
    7. Palma, David & Hess, Stephane, 2022. "Extending the Multiple Discrete Continuous (MDC) modelling framework to consider complementarity, substitution, and an unobserved budget," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 13-35.
    8. Rashidi, Taha Hossein & Auld, Joshua & Mohammadian, Abolfazl (Kouros), 2012. "A behavioral housing search model: Two-stage hazard-based and multinomial logit approach to choice-set formation and location selection," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(7), pages 1097-1107.
    9. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    10. Crawford, Gregory S. & Griffith, Rachel & Iaria, Alessandro, 2021. "A survey of preference estimation with unobserved choice set heterogeneity," Journal of Econometrics, Elsevier, vol. 222(1), pages 4-43.
    11. Shobhit Saxena & Abdul Rawoof Pinjari & Chandra R. Bhat & Aupal Mondal, 2024. "A flexible multiple discrete–continuous probit (MDCP) model: application to analysis of expenditure patterns of domestic tourists in India," Transportation, Springer, vol. 51(4), pages 1299-1326, August.
    12. 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.
    13. Hanemann, W Michael, 1984. "Discrete-Continuous Models of Consumer Demand," Econometrica, Econometric Society, vol. 52(3), pages 541-561, May.
    14. Wales, T. J. & Woodland, A. D., 1983. "Estimation of consumer demand systems with binding non-negativity constraints," Journal of Econometrics, Elsevier, vol. 21(3), pages 263-285, April.
    15. Auld, Joshua & Mohammadian, Abolfazl(Kouros), 2012. "Activity planning processes in the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1386-1403.
    16. Sakai, Takanori & Romano Alho, André & Bhavathrathan, B.K. & Chiara, Giacomo Dalla & Gopalakrishnan, Raja & Jing, Peiyu & Hyodo, Tetsuro & Cheah, Lynette & Ben-Akiva, Moshe, 2020. "SimMobility Freight: An agent-based urban freight simulator for evaluating logistics solutions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    17. Kaplan, Sigal & Shiftan, Yoram & Bekhor, Shlomo, 2012. "Development and estimation of a semi-compensatory model with a flexible error structure," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 291-304.
    18. Lai, Kee-hung & Ngai, E. W. T. & Cheng, T. C. E., 2002. "Measures for evaluating supply chain performance in transport logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 38(6), pages 439-456, November.
    19. Bhat, Chandra 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, Elsevier, vol. 42(3), pages 274-303, March.
    20. Bhat, Chandra R. & Pinjari, Abdul R. & Dubey, Subodh K. & Hamdi, Amin S., 2016. "On accommodating spatial interactions in a Generalized Heterogeneous Data Model (GHDM) of mixed types of dependent variables," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 240-263.
    21. Annesha Enam & Karthik C. Konduri & Naveen Eluru & Srinath Ravulaparthy, 2018. "Relationship between well-being and daily time use of elderly: evidence from the disabilities and use of time survey," Transportation, Springer, vol. 45(6), pages 1783-1810, November.
    22. Bhat, Chandra R. & Mondal, Aupal, 2022. "A New Flexible Generalized Heterogeneous Data Model (GHDM) with an Application to Examine the Effect of High Density Neighborhood Living on Bicycling Frequency," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 244-266.
    23. Ben-Akiva, Moshe & McFadden, Daniel & Train, Kenneth & Börsch-Supan, Axel, 2002. "Hybrid Choice Models: Progress and Challenges," Sonderforschungsbereich 504 Publications 02-29, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    24. Taha Hossein Rashidi & Matthew J. Roorda, 2018. "A business establishment fleet ownership and composition model," Transportation, Springer, vol. 45(3), pages 971-987, May.
    25. Daziano, Ricardo A., 2015. "Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 1-26.
    26. repec:xrs:meawpa:02009 is not listed on IDEAS
    27. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, Enero-Abr.
    Full references (including those not matched with items on IDEAS)

    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. Rodrigo J. Tapia & Gerard Jong & Ana M. Larranaga & Helena B. Bettella Cybis, 2021. "Exploring Multiple‐discreteness in Freight Transport. A Multiple Discrete Extreme Value Model Application for Grain Consolidators in Argentina," Networks and Spatial Economics, Springer, vol. 21(3), pages 581-608, September.
    2. Dapeng Zhang & Xiaokun Wang & José Holguín-Veras & Wei Zou, 2019. "Investigation of carriers’ ability to transfer toll increases: an empirical analysis of freight agents’ relative market power," Transportation, Springer, vol. 46(6), pages 2291-2308, December.
    3. Enam, Annesha & Konduri, Karthik C. & Pinjari, Abdul R. & Eluru, Naveen, 2018. "An integrated choice and latent variable model for multiple discrete continuous choice kernels: Application exploring the association between day level moods and discretionary activity engagement choices," Journal of choice modelling, Elsevier, vol. 26(C), pages 80-100.
    4. Pinjari, Abdul Rawoof & Bhat, Chandra, 2021. "Computationally efficient forecasting procedures for Kuhn-Tucker consumer demand model systems: Application to residential energy consumption analysis," Journal of choice modelling, Elsevier, vol. 39(C).
    5. Fang, Hao Audrey, 2008. "A discrete-continuous model of households' vehicle choice and usage, with an application to the effects of residential density," Transportation Research Part B: Methodological, Elsevier, vol. 42(9), pages 736-758, November.
    6. Palma, David & Hess, Stephane, 2022. "Extending the Multiple Discrete Continuous (MDC) modelling framework to consider complementarity, substitution, and an unobserved budget," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 13-35.
    7. Sikder, Sujan & Pinjari, Abdul Rawoof, 2013. "The benefits of allowing heteroscedastic stochastic distributions in multiple discrete-continuous choice models," Journal of choice modelling, Elsevier, vol. 9(C), pages 39-56.
    8. Bhat, Chandra R. & Mondal, Aupal, 2022. "A New Flexible Generalized Heterogeneous Data Model (GHDM) with an Application to Examine the Effect of High Density Neighborhood Living on Bicycling Frequency," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 244-266.
    9. Dekker, Thijs & Koster, Paul & Mouter, Niek, 2024. "A micro-econometric framework for Participatory Value Evaluation," Journal of choice modelling, Elsevier, vol. 52(C).
    10. Bhat, Chandra R. & Pinjari, Abdul R. & Dubey, Subodh K. & Hamdi, Amin S., 2016. "On accommodating spatial interactions in a Generalized Heterogeneous Data Model (GHDM) of mixed types of dependent variables," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 240-263.
    11. Kuriyama, Koichi & Shoji, Yasushi & Tsuge, Takahiro, 2020. "The value of leisure time of weekends and long holidays: The multiple discrete–continuous extreme value (MDCEV) choice model with triple constraints," Journal of choice modelling, Elsevier, vol. 37(C).
    12. Richards, Timothy J. & Gómez, Miguel I. & Pofahl, Geoffrey, 2012. "A Multiple-discrete/Continuous Model of Price Promotion," Journal of Retailing, Elsevier, vol. 88(2), pages 206-225.
    13. Giubergia, Daniele & Haddad, Angela J. & Piras, Francesco & Bhat, Chandra R. & Meloni, Italo, 2025. "Modeling spatial and social interdependency effects on commuting mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 196(C).
    14. Bonnet, Céline & Richards, Timothy J., 2016. "Models of Consumer Demand for Differentiated Products," TSE Working Papers 16-741, Toulouse School of Economics (TSE).
    15. Carson, Richard T. & Eagle, Thomas C. & Islam, Towhidul & Louviere, Jordan J., 2022. "Volumetric choice experiments (VCEs)," Journal of choice modelling, Elsevier, vol. 42(C).
    16. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    17. Samani, Ali Riahi & Talebian, Ahmadreza & Mishra, Sabyasachee & Golias, Mihalis, 2025. "Evaluating consumer shopping, delivery demands, and last-mile preferences: An integrated MDCEV-HCM approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 197(C).
    18. Dong, Chunjiao & Shao, Chunfu & Clarke, David B. & Nambisan, Shashi S., 2018. "An innovative approach for traffic crash estimation and prediction on accommodating unobserved heterogeneities," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 407-428.
    19. Kidokoro, Yukihiro, 2016. "A micro foundation for discrete choice models with multiple categories of goods," Journal of choice modelling, Elsevier, vol. 19(C), pages 54-72.
    20. Kim, Chul & Smith, Adam N. & Kim, Jaehwan & Allenby, Greg M., 2023. "Outside good utility and substitution patterns in direct utility models," Journal of choice modelling, Elsevier, vol. 49(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eejocm:v:56:y:2025:i:c:s1755534525000260. 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.journals.elsevier.com/journal-of-choice-modelling .

    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.