IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v48y2021i1d10.1007_s11116-019-10049-z.html
   My bibliography  Save this article

Psychological items: a useful addition in modeling travel behavior on managed lanes

Author

Listed:
  • Lisa L. Green

    (Texas A&M University System)

  • Mark W. Burris

    (Texas A&M University System)

  • David Florence

    (Texas A&M University System)

  • Winfred Arthur

    (Texas A&M University)

Abstract

Managed lanes (MLs) are a tool to more efficiently operate segments of a freeway. As ML prevalence increases in the United States of America, it is important to understand travel behavior in a ML setting (i.e., lane choices and carpooling decisions). Socio-demographic and trip data, along with travel time and toll, have been commonly used in this endeavor. However, there are some travelers who pay to use the ML despite there being little to no improvement in travel time over the adjacent general purpose lanes. This gives rise to the possibility that psychological traits and characteristics are a greater influence on ML use than even travel time savings for some travelers. This research examined this issue through a set of largely transportation-framed psychological items. After an initial creation and refining process, 25 psychological items were included in a survey advertised in five cities with MLs. In addition to psychological items, trip and demographic questions, and three SP questions were included in the online survey. Mixed logit models were estimated based on survey responses obtained from three study areas. Models that included psychological items performed better (in terms of adjusted rho squared value and percent correctly predicted values) than models with only trip and demographic variables. Likewise, models including psychological items plus trip and demographic data performed best. This information may be useful for traffic and revenue estimating firms interested in potentially including psychological items in future ML surveys intended to facilitate better estimation of ML use.

Suggested Citation

  • Lisa L. Green & Mark W. Burris & David Florence & Winfred Arthur, 2021. "Psychological items: a useful addition in modeling travel behavior on managed lanes," Transportation, Springer, vol. 48(1), pages 215-237, February.
    • Handle: RePEc:kap:transp:v:48:y:2021:i:1:d:10.1007_s11116-019-10049-z
    DOI: 10.1007/s11116-019-10049-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11116-019-10049-z
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11116-019-10049-z?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. Vredin Johansson, Maria & Heldt, Tobias & Johansson, Per, 2006. "The effects of attitudes and personality traits on mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(6), pages 507-525, July.
    2. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    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. Salvatore Cafiso & Alessandro Di Graziano & Tullio Giuffrè & Giuseppina Pappalardo & Alessandro Severino, 2022. "Managed Lane as Strategy for Traffic Flow and Safety: A Case Study of Catania Ring Road," Sustainability, MDPI, vol. 14(5), pages 1-16, March.

    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. Bouscasse, Hélène & de Lapparent, Matthieu, 2019. "Perceived comfort and values of travel time savings in the Rhône-Alpes Region," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 370-387.
    2. 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.
    3. Choi, Andy S. & Ritchie, Brent W. & Papandrea, Franco & Bennett, Jeff, 2010. "Economic valuation of cultural heritage sites: A choice modeling approach," Tourism Management, Elsevier, vol. 31(2), pages 213-220.
    4. Bouscasse, H., 2018. "Integrated choice and latent variable models: A literature review on mode choice," Working Papers 2018-07, Grenoble Applied Economics Laboratory (GAEL).
    5. Di Ciommo, Floridea & Monzón, Andrés & Fernandez-Heredia, Alvaro, 2013. "Improving the analysis of road pricing acceptability surveys by using hybrid models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 302-316.
    6. Macea, Luis F. & Cantillo, Victor & Arellana, Julian, 2018. "Influence of attitudes and perceptions on deprivation cost functions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 112(C), pages 125-141.
    7. Choi, Andy S., 2011. "Implicit prices for longer temporary exhibitions in a heritage site and a test of preference heterogeneity: A segmentation-based approach," Tourism Management, Elsevier, vol. 32(3), pages 511-519.
    8. Andani, I Gusti Ayu & La Paix Puello, Lissy & Geurs, Karst, 2021. "Modelling effects of changes in travel time and costs of toll road usage on choices for residential location, route and travel mode across population segments in the Jakarta-Bandung region, Indonesia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 145(C), pages 81-102.
    9. Yan Han & Wanying Li & Shanshan Wei & Tiantian Zhang, 2018. "Research on Passenger’s Travel Mode Choice Behavior Waiting at Bus Station Based on SEM-Logit Integration Model," Sustainability, MDPI, vol. 10(6), pages 1-23, June.
    10. Yang, Yuan & Wang, Can & Liu, Wenling & Zhou, Peng, 2018. "Understanding the determinants of travel mode choice of residents and its carbon mitigation potential," Energy Policy, Elsevier, vol. 115(C), pages 486-493.
    11. Tran, Yen & Yamamoto, Toshiyuki & Sato, Hitomi & Miwa, Tomio & Morikawa, Takayuki, 2020. "The analysis of influences of attitudes on mode choice under highly unbalanced mode share patterns," Journal of choice modelling, Elsevier, vol. 36(C).
    12. Luis Márquez & Víctor Cantillo & Julián Arellana, 2020. "Assessing the influence of indicators’ complexity on hybrid discrete choice model estimates," Transportation, Springer, vol. 47(1), pages 373-396, February.
    13. Min, Jihoon & Azevedo, Inês L. & Michalek, Jeremy & de Bruin, Wändi Bruine, 2014. "Labeling energy cost on light bulbs lowers implicit discount rates," Ecological Economics, Elsevier, vol. 97(C), pages 42-50.
    14. Vedel, Suzanne Elizabeth & Jacobsen, Jette Bredahl & Skov-Petersen, Hans, 2017. "Bicyclists’ preferences for route characteristics and crowding in Copenhagen – A choice experiment study of commuters," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 53-64.
    15. 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).
    16. Alireza Ermagun & David Levinson, 2015. "Physical Activity in School Travel: A Cross-Nested Logit Approach," Working Papers 000130, University of Minnesota: Nexus Research Group.
    17. Wiktor Budziński & Mikołaj Czajkowski, 2022. "Endogeneity and Measurement Bias of the Indicator Variables in Hybrid Choice Models: A Monte Carlo Investigation," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 83(3), pages 605-629, November.
    18. Saxena, Shobhit & Bhat, Chandra R. & Pinjari, Abdul Rawoof, 2023. "Separation-based parameterization strategies for estimation of restricted covariance matrices in multivariate model systems," Journal of choice modelling, Elsevier, vol. 47(C).
    19. Li, Xiaowei & Tang, Junqing & Hu, Xiaojiao & Wang, Wei, 2020. "Assessing intercity multimodal choice behavior in a Touristy City: A factor analysis," Journal of Transport Geography, Elsevier, vol. 86(C).
    20. Joanna Mazur & Katarzyna Śledziewska & Damian Zieba, 2018. "Regulation of Geo-blocking: does it address the problem of low intraEU iTrade?," Working Papers 2018-20, Faculty of Economic Sciences, University of Warsaw.

    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:kap:transp:v:48:y:2021:i:1:d:10.1007_s11116-019-10049-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.