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Transportation Letters
The International Journal of Transportation Research
Volume 13, 2021 - Issue 1
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Articles

Investigating college students’ choice of train trips for homecoming during the Spring Festival travel rush in China: results from a stated preference approach

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References

  • Aizaki, H. 2012. “Basic Functions for Supporting an Implementation of Choice Experiments in R.” Journal of Statistical Software 50: 1–24. doi: 10.18637/jss.v050.c02.
  • Behrens, C., and E. Pels. 2012. “Intermodal Competition in the London-Paris Passenger Market: High-Speed Rail and Air Transport.” Journal of Urban Economics 71 (3): 278–288. doi:10.1016/j.jue.2011.12.005.
  • Chang, J. S., and D. Jung. 2017. “Valuations on Quality of Service for Intercity Travels Using High-Speed Rail.” Transportation Letters: the International Journal of Transportation Research 9 (4): 228–242. doi:10.1080/19427867.2016.1226726.
  • ChoiceMetrics. 2018. Ngene 1.2 User Manual & Reference Guide: The Cutting Edge in Experimental Design, http://www.choice-metrics.com.
  • Flügel, S., A. H. Halse, J. D. D. Ortúzar, and L. I. Rizzi. 2015. “Methodological Challenges in Modelling the Choice of Mode for a New Travel Alternative Using Binary Stated Choice Data - the Case of High Speed Rail in Norway.” Transportation Research Part A: Policy and Practice 78: 438–451.
  • Greene, W. H., and D. A. Hensher. 2003. “A Latent Class Model for Discrete Choice Analysis: Contrasts with Mixed Logit.” Transportation Research Part B: Methodological 37 (8): 681–698. doi:10.1016/S0191-2615(02)00046-2.
  • Hensher, D. A., J. M. Rose, and W. H. Greene. 2015. Applied Choice Analysis. 2nd ed. Cambridge University Press, UK.
  • Hess, S., and D. Palma. 2019. “Apollo: A Flexible, Powerful and Customisable Freeware Package for Choice Model Estimation and Application.” Journal of Choice Modelling 32: 100170. doi:10.1016/j.jocm.2019.100170.
  • Koppelman, F. S., and C. R. Bhat. 2006. A Self Instructing Course in Mode Choice Modeling: Multinomial and Nested Logit Models, Prepared for U.S. Department of Transportation Federal Transit Administration.
  • Lee, J.-K., K.-E. Yoo, and K.-H. Song. 2016. “A Study on Travelers’ Transport Mode Choice Behavior Using the Mixed Logit Model: A Case Study of the Seoul-Jeju Route.” Journal of Air Transport Management 56: 131–137. doi:10.1016/j.jairtraman.2016.04.020.
  • Li, J., Y. Qingqing, X. Deng, Y. Liu, and Y. Liu. 2016. “Spatial-Temporal Analysis on Spring Festival Travel Rush in China Based on Multisource Big Data.” Sustainability 8: 11. doi:10.3390/su8111184.
  • Li, Z., and D. Sheng. 2016. “Forecasting Passenger Travel Demand for Air and High-Speed Rail Integration Service: A Case Study of Beijing-Guangzhou Corridor, China.” Transportation Research Part A: Policy and Practice 94: 397–410.
  • Lin, X., Y. O. Susilo, C. Shao, and C. Liu. 2018. “The Implication of Road Toll Discount for Mode Choice: Intercity Travel during the Chinese Spring Festival Holiday.” Sustainability 10: 8.
  • Louviere, J. J., D. A. Hensher, and J. Swait. 2000. Stated Choice Methods: Analysis and Applications. Cambridge University Press, UK.
  • Moeckel, R., R. Fussell, and R. Donnelly. 2014. “Mode Choice Modeling for Long-Distance Travel.” Transportation Letters: the International Journal of Transportation Research 7 (1): 35–46. doi:10.1179/1942787514Y.0000000031.
  • Rose, J. M., and C. J. M. Bliemer. 2005. “Constructing Efficient Choice Experiments.” ITLS-WP-05-07. Institute of Transport and Logistics Studies Working Paper.
  • Rose, J. M., C. J. Michiel, D. A. Bliemer, and A. T. Collins. 2008. “Designing Efficient Stated Choice Experiments in the Presence of Reference Alternatives.” Transportation Research Part B: Methodological 42 (4): 395–406. doi:10.1016/j.trb.2007.09.002.
  • Shen, J. 2009. “Latent Class Model or Mixed Logit Model? A Comparison by Transport Mode Choice Data.” Applied Economics 41 (22): 2915–2924. doi:10.1080/00036840801964633.
  • Train, K. E. 2009. Discrete Choice Methods with Simulation. 2nd ed. Cambridge University Press, UK.
  • van Cranenburgh, S., J. M. Rose, and C. G. Chorus. 2018. “On the Robustness of Efficient Experimental Designs Towards the Underlying Decision Rule1.” Transportation Research Part A: Policy and Practice 109: 50–64. Elsevier.
  • Walker, J. L., Y. Wang, M. Thorhauge, and M. Ben-Akiva. 2018. “D-Efficient or Deficient? A Robustness Analysis of Stated Choice Experimental Designs.” Theory and Decision 84 (2): 215–238. doi:10.1007/s11238-017-9647-3.
  • Wang, L., Q. Zhang, Y. Cai, J. Zhang, and M. Qingguo. 2013. “Simulation Study of Pedestrian Flow in a Station Hall during the Spring Festival Travel Rush.” Physica A: Statistical Mechanics and Its Applications 392 (10): 2470–2478. doi:10.1016/j.physa.2013.01.044.
  • Wang, Y., L. Lei, L. Wang, A. Moore, S. Staley, and L. Zongzhi. 2014. “Modeling Traveler Mode Choice Behavior of a New High-Speed Rail Corridor in China.” Transportation Planning and Technology 37 (5): 466–483. doi:10.1080/03081060.2014.912420.
  • Xinhua. 2019. “China’s High-Speed Railway Length to Top 30,000 Km in 2019.” Accessed April 9. http://www.xinhuanet.com/english/2019-01/03/c_137715444.htm
  • Yang, C., and C. Chang. 2011. “Applying Price and Time Differentiation to Modeling Cabin Choice in High-Speed Rail.” Transportation Research Part E: Logistics and Transportation Review 47: 1. doi:10.1016/j.tre.2010.07.003.
  • Yang, C., M. Tsai, and C. Chang. 2015. “Investigations of Interactions between Bus Rapid Transit and General Traffic Flows.” Journal of Advanced Transportation 49 (1): 326–340. doi:10.1002/atr.1268.