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The Implication of Road Toll Discount for Mode Choice: Intercity Travel during the Chinese Spring Festival Holiday

Author

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  • Xiaomei Lin

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Yusak O. Susilo

    (Department of Urban Planning and Environment, School of Architecture and the Built Environment, KTH Royal Institute of Technology, Stockholm 10044, Sweden)

  • Chunfu Shao

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Chengxi Liu

    (Division of Traffic Analysis and Logistics, Swedish National Road and Transport Research Institute, Stockholm 10044, Sweden)

Abstract

Intercity travel congestion during the main national holidays takes place every year at different places around the world. Charge reduction measurements on existing toll roads have been implemented to promote an efficient use of the expressways and to reduce congestion on the public transit networks. However, some of these policies have had negative effects. A more comprehensive understanding of the determinants of holiday intercity travel patterns is critical for better policymaking. This paper aims to investigate the effectiveness of the road toll discount policy on mode choice behavior for intercity travel. A mixed logit model is developed to model the mode choices of intercity travelers, which is estimated based on survey data about intercity journeys from Beijing during the 2017 Chinese Spring Festival holiday. The policy impact is further discussed by elasticity and scenario simulations. The results indicate that the expressway toll discount does increase the car use and decrease the public transit usage. Given the decreased toll on expressways, the demand tends to shift from car to public transit, in an order of coach, high-speed rail, conventional rail, and airplane. When it comes to its effect on socio-demographic groups, men and lower-income travelers are identified to be more likely to change mode in response to variation of road toll. Finally, policy effectiveness is found to vary for travelers in different travel distance groups. Conclusions provide useful insights on road pricing management.

Suggested Citation

  • Xiaomei Lin & Yusak O. Susilo & Chunfu Shao & Chengxi Liu, 2018. "The Implication of Road Toll Discount for Mode Choice: Intercity Travel during the Chinese Spring Festival Holiday," Sustainability, MDPI, vol. 10(8), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2700-:d:161317
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    as
    1. Wang, Kun & Xia, Wenyi & Zhang, Anming & Zhang, Qiong, 2018. "Effects of train speed on airline demand and price: Theory and empirical evidence from a natural experiment," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 99-130.
    2. Lei Zhang & Frank Southworth & Chenfeng Xiong & Anthon Sonnenberg, 2012. "Methodological Options and Data Sources for the Development of Long-Distance Passenger Travel Demand Models: A Comprehensive Review," Transport Reviews, Taylor & Francis Journals, vol. 32(4), pages 399-433, April.
    3. Rojo, Marta & Gonzalo-Orden, Hernán & dell’Olio, Luigi & Ibeas, Ángel, 2012. "Relationship between service quality and demand for inter-urban buses," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1716-1729.
    4. Xiaowen Fu & Tae H. Oum & Jia Yan, 2014. "An Analysis of Travel Demand in Japan's Intercity Market Empirical Estimation and Policy Simulation," Journal of Transport Economics and Policy, University of Bath, vol. 48(1), pages 97-113, January.
    5. Langbroek, Joram H.M. & Franklin, Joel P. & Susilo, Yusak O., 2016. "The effect of policy incentives on electric vehicle adoption," Energy Policy, Elsevier, vol. 94(C), pages 94-103.
    6. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    7. Xiao-Yong Yan & Wen-Xu Wang & Zi-You Gao & Ying-Cheng Lai, 2017. "Universal model of individual and population mobility on diverse spatial scales," Nature Communications, Nature, vol. 8(1), pages 1-9, December.
    8. Bhat, Chandra R., 1995. "A heteroscedastic extreme value model of intercity travel mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 29(6), pages 471-483, December.
    9. Dickinson, Janet E. & Robbins, Derek & Lumsdon, Les, 2010. "Holiday travel discourses and climate change," Journal of Transport Geography, Elsevier, vol. 18(3), pages 482-489.
    10. Benedikt Mandel & Marc Gaudry & Werner Rothengatter, 1997. "A disaggregate Box-Cox Logit mode choice model of intercity passenger travel in Germany and its implications for high-speed rail demand forecasts," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 31(2), pages 99-120.
    11. Li, Weibo & Kamargianni, Maria, 2018. "Providing quantified evidence to policy makers for promoting bike-sharing in heavily air-polluted cities: A mode choice model and policy simulation for Taiyuan-China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 277-291.
    12. Yun Wang & Xuedong Yan & Yu Zhou & Qingwan Xue, 2017. "Influencing Mechanism of Potential Factors on Passengers’ Long-Distance Travel Mode Choices Based on Structural Equation Modeling," Sustainability, MDPI, vol. 9(11), pages 1-22, October.
    13. 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.
    14. Steiner, T. J. & Bristow, A. L., 2000. "Road pricing in National Parks: a case study in the Yorkshire Dales National Park," Transport Policy, Elsevier, vol. 7(2), pages 93-103, April.
    15. Bao, Yue & Xiao, Feng & Gao, Zaihan & Gao, Ziyou, 2017. "Investigation of the traffic congestion during public holiday and the impact of the toll-exemption policy," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 58-81.
    16. Moshe Givoni, 2006. "Development and Impact of the Modern High‐speed Train: A Review," Transport Reviews, Taylor & Francis Journals, vol. 26(5), pages 593-611, January.
    17. Bhat, Chandra R., 2003. "Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 837-855, November.
    18. Nuzzolo, Agostino & Crisalli, Umberto & Gangemi, Francesca, 2000. "A behavioural choice model for the evaluation of railway supply and pricing policies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(5), pages 395-404, June.
    19. Fu, Shihe & Gu, Yizhen, 2017. "Highway toll and air pollution: Evidence from Chinese cities," Journal of Environmental Economics and Management, Elsevier, vol. 83(C), pages 32-49.
    20. 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.
    21. Albert, Gila & Mahalel, David, 2006. "Congestion tolls and parking fees: A comparison of the potential effect on travel behavior," Transport Policy, Elsevier, vol. 13(6), pages 496-502, November.
    22. Eliasson, Jonas & Mattsson, Lars-Göran, 2006. "Equity effects of congestion pricing: Quantitative methodology and a case study for Stockholm," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(7), pages 602-620, August.
    23. Monzón, Andrés & Rodriguez-Dapena, Alvaro, 2006. "Choice of mode of transport for long-distance trips: Solving the problem of sparse data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(7), pages 587-601, August.
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