IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i8p2700-d161317.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/8/2700/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/8/2700/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    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, January.
    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.
    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. Ioannis-Dimosthenis Ramandanis & Ioannis Politis & Socrates Basbas, 2020. "Assessing the Environmental and Economic Footprint of Electronic Toll Collection Lanes: A Simulation Study," Sustainability, MDPI, vol. 12(22), pages 1-25, November.
    2. Haiyan Zhu & Hongzhi Guan & Yan Han & Wanying Li, 2019. "A Study of Tourists’ Holiday Rush-Hour Avoidance Travel Behavior Considering Psychographic Segmentation," Sustainability, MDPI, vol. 11(13), pages 1-20, July.
    3. 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.
    4. Haiyan Zhu & Hongzhi Guan & Yan Han & Wanying Li, 2020. "Can Road Toll Convince Car Travelers to Adjust Their Departure Times? Accounting for the Effect of Choice Behavior under Long and Short Holidays," Sustainability, MDPI, vol. 12(24), pages 1-29, December.
    5. Pan, Haixiao & Gao, Ya & Shen, Qing & Moudon, Anne Vernez & Tuo, Jianyi & Habib, Khandker Nurul, 2023. "Does high-speed rail mitigate peak vacation car traffic to tourist city? Evidence from China," Transport Policy, Elsevier, vol. 143(C), pages 93-105.
    6. Haiyan Zhu & Hongzhi Guan & Yan Han & Wanying Li, 2022. "Study on Peak Travel Avoidance Behavior of Car Travelers during Holidays," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    7. Irawan, Muhammad Zudhy & Simanjuntak, Nurvita I.M. & Bastarianto, Faza Fawzan & Dwitasari, Reslyana & Herawati,, 2020. "Predicting the impact of Trans Java Toll Roads on demand for intercity air travel in Indonesia," Journal of Air Transport Management, Elsevier, vol. 87(C).
    8. Maurizio Faccio & Serena Finco & Ilenia Zennaro, 2021. "Sustainable People Home-Work Logistics: An Integrated Model of Circular Economy in the Chiampo Valley," Sustainability, MDPI, vol. 13(21), pages 1-13, October.
    9. Rong Cao & Xuehui Chen & Jianmin Jia & Hui Zhang, 2023. "Uncovering Equity and Travelers’ Behavior on the Expressway: A Case Study of Shandong, China," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
    10. Guang Yang & Yan Han & Hao Gong & Tiantian Zhang, 2020. "Spatial-Temporal Response Patterns of Tourist Flow under Real-Time Tourist Flow Diversion Scheme," Sustainability, MDPI, vol. 12(8), pages 1-28, April.

    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. Haiyan Zhu & Hongzhi Guan & Yan Han & Wanying Li, 2020. "Can Road Toll Convince Car Travelers to Adjust Their Departure Times? Accounting for the Effect of Choice Behavior under Long and Short Holidays," Sustainability, MDPI, vol. 12(24), pages 1-29, December.
    2. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    3. Azari, Kian Ahmadi & Arintono, Sulistyo & Hamid, Hussain & Davoodi, Seyed Rasoul, 2013. "Evaluation of demand for different trip purposes under various congestion pricing scenarios," Journal of Transport Geography, Elsevier, vol. 29(C), pages 43-51.
    4. Bhat, Chandra R. & Sen, Sudeshna, 2006. "Household vehicle type holdings and usage: an application of the multiple discrete-continuous extreme value (MDCEV) model," Transportation Research Part B: Methodological, Elsevier, vol. 40(1), pages 35-53, January.
    5. Wen Yang & Quanliang Chen & Jing Yang, 2022. "Factors Affecting Travel Mode Choice between High-Speed Railway and Road Passenger Transport—Evidence from China," Sustainability, MDPI, vol. 14(23), pages 1-17, November.
    6. Min Su & Weixin Luan & Liuyan Yuan & Rui Zhang & Zhenchao Zhang, 2019. "Sustainability Development of High-Speed Rail and Airline—Understanding Passengers’ Preferences: A Case Study of the Beijing–Shanghai Corridor," Sustainability, MDPI, vol. 11(5), pages 1-19, March.
    7. Wang, Hai & Yang, Hai, 2019. "Ridesourcing systems: A framework and review," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 122-155.
    8. Lingjuan Chen & Yijing Zhao & Zupeng Liu & Xinran Yang, 2022. "Construction of Commuters’ Multi-Mode Choice Model Based on Public Transport Operation Data," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    9. Cherchi, Elisabetta & Guevara, Cristian Angelo, 2012. "A Monte Carlo experiment to analyze the curse of dimensionality in estimating random coefficients models with a full variance–covariance matrix," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 321-332.
    10. Espinosa-Aranda, José Luis & García-Ródenas, Ricardo & Ramírez-Flores, María del Carmen & López-García, María Luz & Angulo, Eusebio, 2015. "High-speed railway scheduling based on user preferences," European Journal of Operational Research, Elsevier, vol. 246(3), pages 772-786.
    11. Stephane Hess, 2014. "Latent class structures: taste heterogeneity and beyond," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 14, pages 311-330, Edward Elgar Publishing.
    12. Zhang, Anming & Wan, Yulai & Yang, Hangjun, 2019. "Impacts of high-speed rail on airlines, airports and regional economies: A survey of recent research," Transport Policy, Elsevier, vol. 81(C), pages 1-19.
    13. Heiss, Florian & Hetzenecker, Stephan & Osterhaus, Maximilian, 2022. "Nonparametric estimation of the random coefficients model: An elastic net approach," Journal of Econometrics, Elsevier, vol. 229(2), pages 299-321.
    14. Mahmud, Asif & Gayah, Vikash V. & Paleti, Rajesh, 2022. "A latent choice model to analyze the role of preliminary preferences in shaping observed choices," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 95-108.
    15. Meng, Xuechen & Lin, Shanlang & Zhu, Xiaochuan, 2018. "The resource redistribution effect of high-speed rail stations on the economic growth of neighbouring regions: Evidence from China," Transport Policy, Elsevier, vol. 68(C), pages 178-191.
    16. 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.
    17. Weiss, Adam & Habib, Khandker Nurul, 2017. "Examining the difference between park and ride and kiss and ride station choices using a spatially weighted error correlation (SWEC) discrete choice model," Journal of Transport Geography, Elsevier, vol. 59(C), pages 111-119.
    18. Román, Concepción & Martín, Juan Carlos, 2014. "Integration of HSR and air transport: Understanding passengers’ preferences," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 129-141.
    19. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
    20. Florian Heiss & Stephan Hetzenecker & Maximilian Osterhaus, 2019. "Nonparametric Estimation of the Random Coefficients Model: An Elastic Net Approach," Papers 1909.08434, arXiv.org, revised Sep 2019.

    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:gam:jsusta:v:10:y:2018:i:8:p:2700-:d:161317. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.