IDEAS home Printed from https://ideas.repec.org/a/hin/jnddns/136010.html
   My bibliography  Save this article

Holiday Destination Choice Behavior Analysis Based on AFC Data of Urban Rail Transit

Author

Listed:
  • Chang-jun Cai
  • En-jian Yao
  • Sha-sha Liu
  • Yong-sheng Zhang
  • Jun Liu

Abstract

For urban rail transit, the spatial distribution of passenger flow in holiday usually differs from weekdays. Holiday destination choice behavior analysis is the key to analyze passengers’ destination choice preference and then obtain the OD (origin-destination) distribution of passenger flow. This paper aims to propose a holiday destination choice model based on AFC (automatic fare collection) data of urban rail transit system, which is highly expected to provide theoretic support to holiday travel demand analysis for urban rail transit. First, based on Guangzhou Metro AFC data collected on New Year’s day, the characteristics of holiday destination choice behavior for urban rail transit passengers is analyzed. Second, holiday destination choice models based on MNL (Multinomial Logit) structure are established for each New Year’s days respectively, which takes into account some novel explanatory variables (such as attractiveness of destination). Then, the proposed models are calibrated with AFC data from Guangzhou Metro using WESML (weighted exogenous sample maximum likelihood) estimation and compared with the base models in which attractiveness of destination is not considered. The results show that the values are improved by 0.060, 0.045, and 0.040 for January 1, January 2, and January 3, respectively, with the consideration of destination attractiveness.

Suggested Citation

  • Chang-jun Cai & En-jian Yao & Sha-sha Liu & Yong-sheng Zhang & Jun Liu, 2015. "Holiday Destination Choice Behavior Analysis Based on AFC Data of Urban Rail Transit," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-7, February.
    • Handle: RePEc:hin:jnddns:136010
    DOI: 10.1155/2015/136010
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/DDNS/2015/136010.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/DDNS/2015/136010.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2015/136010?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
    ---><---

    References listed on IDEAS

    as
    1. Vega, Amaya & Reynolds-Feighan, Aisling, 2009. "A methodological framework for the study of residential location and travel-to-work mode choice under central and suburban employment destination patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 401-419, May.
    2. Asakura, Yasuo & Iryo, Takamasa, 2007. "Analysis of tourist behaviour based on the tracking data collected using a mobile communication instrument," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(7), pages 684-690, August.
    3. de Jong, Gerard & Daly, Andrew & Pieters, Marits & Vellay, Carine & Bradley, Mark & Hofman, Frank, 2003. "A model for time of day and mode choice using error components logit," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 39(3), pages 245-268, May.
    4. Yao, Enjian & Morikawa, Takayuki, 2005. "A study of on integrated intercity travel demand model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(4), pages 367-381, May.
    5. van Cranenburgh, S. & Chorus, C.G. & van Wee, B., 2014. "Vacation behaviour under high travel cost conditions – A stated preference of revealed preference approach," Tourism Management, Elsevier, vol. 43(C), pages 105-118.
    6. Manski, Charles F & Lerman, Steven R, 1977. "The Estimation of Choice Probabilities from Choice Based Samples," Econometrica, Econometric Society, vol. 45(8), pages 1977-1988, November.
    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. Wang, Bobin & Shao, Chunfu & Li, Juan & Weng, Jinxian & Ji, Xun, 2015. "Holiday travel behavior analysis and empirical study under integrated multimodal travel information service," Transport Policy, Elsevier, vol. 39(C), pages 21-36.
    2. Steimetz, Seiji S.C. & Brownstone, David, 2005. "Estimating commuters' "value of time" with noisy data: a multiple imputation approach," Transportation Research Part B: Methodological, Elsevier, vol. 39(10), pages 865-889, December.
    3. Iain M. Cockburn & Megan J. MacGarvie, 2011. "Entry and Patenting in the Software Industry," Management Science, INFORMS, vol. 57(5), pages 915-933, May.
    4. Lurkin, Virginie & Garrow, Laurie A. & Higgins, Matthew J. & Newman, Jeffrey P. & Schyns, Michael, 2017. "Accounting for price endogeneity in airline itinerary choice models: An application to Continental U.S. markets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 228-246.
    5. Hee Chung Chung & Namho Chung & Yoonjae Nam, 2017. "A Social Network Analysis of Tourist Movement Patterns in Blogs: Korean Backpackers in Europe," Sustainability, MDPI, vol. 9(12), pages 1-19, December.
    6. Seiji S. C. Steimetz, 2009. "White‐Knuckle Externalities," Economic Inquiry, Western Economic Association International, vol. 47(2), pages 304-316, April.
    7. Esmeralda Ramalho, 2004. "Covariate Measurement Error in Endogenous Stratified Samples," Economics Working Papers 2_2004, University of Évora, Department of Economics (Portugal).
    8. Richard Disney & Eleonora Fischera & Trudy Owens, 2010. "Has the Introduction of Microfinance Crowded-out Informal Loans in Malawi?," Discussion Papers 10/08, University of Nottingham, CREDIT.
    9. Lois, David & López-Sáez, Mercedes, 2009. "The relationship between instrumental, symbolic and affective factors as predictors of car use: A structural equation modeling approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(9-10), pages 790-799, November.
    10. de Jong, Gerard & Kouwenhoven, Marco & Ruijs, Kim & van Houwe, Pieter & Borremans, Dana, 2016. "A time-period choice model for road freight transport in Flanders based on stated preference data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 86(C), pages 20-31.
    11. Trent Geisler & Herman Ray & Ying Xie, 2023. "Finding the Proverbial Needle: Improving Minority Class Identification Under Extreme Class Imbalance," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 192-212, April.
    12. Steven Berry & James Levinsohn & Ariel Pakes, 2004. "Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market," Journal of Political Economy, University of Chicago Press, vol. 112(1), pages 68-105, February.
    13. Keith Head & Yao Amber Li & Asier Minondo, 2019. "Geography, Ties, and Knowledge Flows: Evidence from Citations in Mathematics," The Review of Economics and Statistics, MIT Press, vol. 101(4), pages 713-727, October.
    14. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    15. Lewe, J.-H. & Hivin, L.F. & Mavris, D.N., 2014. "A multi-paradigm approach to system dynamics modeling of intercity transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 188-202.
    16. Sarlin, Peter & von Schweinitz, Gregor, 2021. "Optimizing Policymakers’ Loss Functions In Crisis Prediction: Before, Within Or After?," Macroeconomic Dynamics, Cambridge University Press, vol. 25(1), pages 100-123, January.
    17. Esmerelda A. Ramalho & Richard Smith, 2003. "Discrete choice non-response," CeMMAP working papers 07/03, Institute for Fiscal Studies.
    18. Ignacio A. Inoa & Nathalie Picard & Andr� de Palma, 2015. "Effect of an Accessibility Measure in a Model for Choice of Residential Location, Workplace, and Type of Employment," Mathematical Population Studies, Taylor & Francis Journals, vol. 22(1), pages 4-36, March.
    19. Stefano Usai & Emanuela Marrocu & Raffaele Paci, 2017. "Networks, Proximities, and Interfirm Knowledge Exchanges," International Regional Science Review, , vol. 40(4), pages 377-404, July.
    20. James Hansen & James McDonald & Panayiotis Theodossiou & Brad Larsen, 2010. "Partially Adaptive Econometric Methods For Regression and Classification," Computational Economics, Springer;Society for Computational Economics, vol. 36(2), pages 153-169, August.

    More about this item

    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:hin:jnddns:136010. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.