IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2510.08366.html
   My bibliography  Save this paper

A data fusion approach for mobility hub impact assessment and location selection: integrating hub usage data into a large-scale mode choice model

Author

Listed:
  • Xiyuan Ren
  • Joseph Y. J. Chow

Abstract

As cities grapple with traffic congestion and service inequities, mobility hubs offer a scalable solution to align increasing travel demand with sustainability goals. However, evaluating their impacts remains challenging due to the lack of behavioral models that integrate large-scale travel patterns with real-world hub usage. This study presents a novel data fusion approach that incorporates observed mobility hub usage into a mode choice model estimated with synthetic trip data. We identify trips potentially affected by mobility hubs and construct a multimodal sub-choice set, then calibrate hub-specific parameters using on-site survey data and ground truth trip counts. The enhanced model is used to evaluate mobility hub impacts on potential demand, mode shift, reduced vehicle miles traveled (VMT), and increased consumer surplus (CS). We apply this method to a case study in the Capital District, NY, using data from a survey conducted by the Capital District Transportation Authority (CDTA) and a mode choice model estimated using Replica Inc. synthetic data. The two implemented hubs located near UAlbany Downtown Campus and in Downtown Cohoes are projected to generate 8.83 and 6.17 multimodal trips per day, reduce annual VMT by 20.37 and 13.16 thousand miles, and increase daily CS by $4,000 and $1,742, respectively. An evaluation of potential hub candidates in the Albany-Schenectady-Troy metropolitan area with the estimated models demonstrates that hubs located along intercity corridors and at urban peripheries, supporting park-and-ride P+R patterns, yield the most significant behavioral impacts.

Suggested Citation

  • Xiyuan Ren & Joseph Y. J. Chow, 2025. "A data fusion approach for mobility hub impact assessment and location selection: integrating hub usage data into a large-scale mode choice model," Papers 2510.08366, arXiv.org.
  • Handle: RePEc:arx:papers:2510.08366
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2510.08366
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Small, Kenneth A & Rosen, Harvey S, 1981. "Applied Welfare Economics with Discrete Choice Models," Econometrica, Econometric Society, vol. 49(1), pages 105-130, January.
    2. Rongen, Tibor & Tillema, Taede & Arts, Jos & Alonso-González, María J. & Witte, Jan-Jelle, 2022. "An analysis of the mobility hub concept in the Netherlands: Historical lessons for its implementation," Journal of Transport Geography, Elsevier, vol. 104(C).
    3. Montserrat Miramontes & Maximilian Pfertner & Hema Sharanya Rayaprolu & Martin Schreiner & Gebhard Wulfhorst, 2017. "Impacts of a multimodal mobility service on travel behavior and preferences: user insights from Munich’s first Mobility Station," Transportation, Springer, vol. 44(6), pages 1325-1342, November.
    4. Arnold, Thomas & Dale, Simon & Timmis, Andrew & Frost, Matthew & Ison, Stephen, 2023. "An exploratory study of Mobility Hub implementation," Research in Transportation Economics, Elsevier, vol. 101(C).
    5. McConnell K. E., 1995. "Consumer Surplus from Discrete Choice Models," Journal of Environmental Economics and Management, Elsevier, vol. 29(3), pages 263-270, November.
    6. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    7. Shin, Eun Jin, 2020. "Commuter benefits programs: Impacts on mode choice, VMT, and spillover effects," Transport Policy, Elsevier, vol. 94(C), pages 11-22.
    8. Greene, William H. & Hensher, David A., 2003. "A latent class model for discrete choice analysis: contrasts with mixed logit," Transportation Research Part B: Methodological, Elsevier, vol. 37(8), pages 681-698, September.
    9. Train, Kenneth, 2016. "Mixed logit with a flexible mixing distribution," Journal of choice modelling, Elsevier, vol. 19(C), pages 40-53.
    10. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    11. Frank, Laura & Dirks, Nicolas & Walther, Grit, 2021. "Improving rural accessibility by locating multimodal mobility hubs," Journal of Transport Geography, Elsevier, vol. 94(C).
    12. McHardy, Jolian & Reynolds, Michael & Trotter, Stephen, 2023. "A consumer surplus, welfare and profit enhancing strategy for improving urban public transport networks," Regional Science and Urban Economics, Elsevier, vol. 100(C).
    13. Ren, Xiyuan & Chow, Joseph Y.J., 2022. "A random-utility-consistent machine learning method to estimate agents’ joint activity scheduling choice from a ubiquitous data set," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 396-418.
    14. Arias-Molinares, Daniela & Xu, Yihan & Büttner, Benjamin & Duran-Rodas, David, 2023. "Exploring key spatial determinants for mobility hub placement based on micromobility ridership," Journal of Transport Geography, Elsevier, vol. 110(C).
    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. Ren, Xiyuan & Chow, Joseph Y.J. & Bansal, Prateek, 2025. "Nonparametric mixed logit model with market-level parameters estimated from market share data," Transportation Research Part B: Methodological, Elsevier, vol. 196(C).
    2. Patrick Bigler & Doina Maria Radulescu, 2022. "Environmental, Redistributive and Revenue Effects of Policies Promoting Fuel Efficient and Electric Vehicles," CESifo Working Paper Series 9645, CESifo.
    3. Haghani, Milad & Bliemer, Michiel C.J. & Hensher, David A., 2021. "The landscape of econometric discrete choice modelling research," Journal of choice modelling, Elsevier, vol. 40(C).
    4. Pereira, Pedro & Ribeiro, Tiago, 2011. "The impact on broadband access to the Internet of the dual ownership of telephone and cable networks," International Journal of Industrial Organization, Elsevier, vol. 29(2), pages 283-293, March.
    5. Allais, Olivier & Etilé, Fabrice & Lecocq, Sébastien, 2015. "Mandatory labels, taxes and market forces: An empirical evaluation of fat policies," Journal of Health Economics, Elsevier, vol. 43(C), pages 27-44.
    6. Pradeep Chintagunta & Jean-Pierre Dubé & Vishal Singh, 2003. "Balancing Profitability and Customer Welfare in a Supermarket Chain," Quantitative Marketing and Economics (QME), Springer, vol. 1(1), pages 111-147, March.
    7. de Ayala, Amaia & Hoyos, David & Mariel, Petr, 2015. "Suitability of discrete choice experiments for landscape management under the European Landscape Convention," Journal of Forest Economics, Elsevier, vol. 21(2), pages 79-96.
    8. Rachel Griffith & Lars Nesheim & Martin O'Connell, 2018. "Income effects and the welfare consequences of tax in differentiated product oligopoly," Quantitative Economics, Econometric Society, vol. 9(1), pages 305-341, March.
    9. Walter Beckert & Elaine Kelly, 2021. "Divided by choice? For‐profit providers, patient choice and mechanisms of patient sorting in the English National Health Service," Health Economics, John Wiley & Sons, Ltd., vol. 30(4), pages 820-839, April.
    10. 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.
    11. Robert Donnelly & Francisco J.R. Ruiz & David Blei & Susan Athey, 2021. "Counterfactual inference for consumer choice across many product categories," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 369-407, December.
    12. Sofia Berto Villas‐Boas, 2009. "An empirical investigation of the welfare effects of banning wholesale price discrimination," RAND Journal of Economics, RAND Corporation, vol. 40(1), pages 20-46, March.
    13. repec:cdl:agrebk:qt5593m46q is not listed on IDEAS
    14. Tinessa, Fiore & Marzano, Vittorio & Papola, Andrea, 2020. "Mixing distributions of tastes with a Combination of Nested Logit (CoNL) kernel: Formulation and performance analysis," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 1-23.
    15. Bansal, Prateek & Daziano, Ricardo A. & Achtnicht, Martin, 2018. "Comparison of parametric and semiparametric representations of unobserved preference heterogeneity in logit models," Journal of choice modelling, Elsevier, vol. 27(C), pages 97-113.
    16. Caroline Löffler & Harald Hecking, 2017. "Greenhouse Gas Abatement Cost Curves of the Residential Heating Market: A Microeconomic Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 68(4), pages 915-947, December.
    17. repec:cdl:agrebk:qt7vg17026 is not listed on IDEAS
    18. Xiyuan Ren & Zhenglei Ji & Joseph Y. J. Chow, 2025. "Distributional welfare impacts and compensatory transit strategies under NYC congestion pricing," Papers 2510.06416, arXiv.org.
    19. Xiyuan Ren & Joseph Y. J. Chow & Prateek Bansal, 2023. "Nonparametric mixed logit model with market-level parameters estimated from market share data," Papers 2309.13159, arXiv.org, revised Apr 2025.
    20. Maria Polyakova & Stephen P. Ryan, 2019. "Subsidy Targeting with Market Power," NBER Working Papers 26367, National Bureau of Economic Research, Inc.
    21. Rico Krueger & Akshay Vij & Taha H. Rashidi, 2018. "A Dirichlet Process Mixture Model of Discrete Choice," Papers 1801.06296, arXiv.org.
    22. Robert Donnelly & Francisco J.R. Ruiz & David Blei & Susan Athey, 2021. "Counterfactual inference for consumer choice across many product categories," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 369-407, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2510.08366. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.