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Latent class nested logit model for analyzing high-speed rail access mode choice

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  • Wen, Chieh-Hua
  • Wang, Wei-Chung
  • Fu, Chiang

Abstract

This paper explores access mode choice behavior, using a survey data collected in Taiwan. The latent class nested logit model is used to capture flexible substitution patterns among alternatives and preference heterogeneity across individuals while simultaneously identifying the number, sizes, and characteristics of market segments. The results indicate that a four-segment latent class nested logit model with individual characteristics in segment membership functions is the most preferred specification. Most high-speed rail travelers were cost-sensitive to access modes, and thus strategies that reduce the access costs can be more effective than reducing the access times.

Suggested Citation

  • Wen, Chieh-Hua & Wang, Wei-Chung & Fu, Chiang, 2012. "Latent class nested logit model for analyzing high-speed rail access mode choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(2), pages 545-554.
    • Handle: RePEc:eee:transe:v:48:y:2012:i:2:p:545-554
    DOI: 10.1016/j.tre.2011.09.002
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    1. Shiftan, Yoram & Outwater, Maren L. & Zhou, Yushuang, 2008. "Transit market research using structural equation modeling and attitudinal market segmentation," Transport Policy, Elsevier, vol. 15(3), pages 186-195, May.
    2. 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.
    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    4. Debrezion, Ghebreegziabiher & Pels, Eric & Rietveld, Piet, 2009. "Modelling the joint access mode and railway station choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(1), pages 270-283, January.
    5. Arunotayanun, Kriangkrai & Polak, John W., 2011. "Taste heterogeneity and market segmentation in freight shippers' mode choice behaviour," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(2), pages 138-148, March.
    6. Wen, Chieh-Hua & Lai, Shan-Ching, 2010. "Latent class models of international air carrier choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(2), pages 211-221, March.
    7. Swait, Joffre, 2003. "Flexible Covariance Structures for Categorical Dependent Variables through Finite Mixtures of Generalized Extreme Value Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 80-87, January.
    8. Psaraki, Voula & Abacoumkin, Costas, 2002. "Access mode choice for relocated airports: the new Athens International Airport," Journal of Air Transport Management, Elsevier, vol. 8(2), pages 89-98.
    9. Joan Walker & Jieping Li, 2007. "Latent lifestyle preferences and household location decisions," Journal of Geographical Systems, Springer, vol. 9(1), pages 77-101, April.
    10. Chandra R. Bhat, 1997. "An Endogenous Segmentation Mode Choice Model with an Application to Intercity Travel," Transportation Science, INFORMS, vol. 31(1), pages 34-48, February.
    11. Wagner A. Kamakura & Byung-Do Kim & Jonathan Lee, 1996. "Modeling Preference and Structural Heterogeneity in Consumer Choice," Marketing Science, INFORMS, vol. 15(2), pages 152-172.
    12. Zhang, Junyi & Kuwano, Masashi & Lee, Backjin & Fujiwara, Akimasa, 2009. "Modeling household discrete choice behavior incorporating heterogeneous group decision-making mechanisms," Transportation Research Part B: Methodological, Elsevier, vol. 43(2), pages 230-250, February.
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