IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i23p9918-d452105.html
   My bibliography  Save this article

Empirical Analysis of Relieving High-Speed Rail Freight Congestion in China

Author

Listed:
  • Hanlin Gao

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Meiqing Zhang

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Anne Goodchild

    (Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA)

Abstract

This paper discusses how to promote high-speed rail (HSR) freight business by solving the congestion problem. First, we define the existing operation modes in China and propose the idea of relieving congestion by reserving more carriages of HSR passenger trains for freight between cities with large potential volume or small capacity. Second, we take one HSR corridor as a case to study, and use predictive regression and integrated time series methods to forecast the growth of HSR freight volume along the corridor. Finally, combined with forecast results and available capacity during the peak month of 2018, we offer suggestions on the mode adoption in each segment during the peak month from 2019 to 2022. Results demonstrate: (1) Among all 84 Origin-Destination (OD) city flows, the percentage of those monthly volumes over 1 ton increases from 17.9% in 2018 to 84.6% in 2022, and those over 30 tons rise from 3.6% to 26.2%. (2) Among the segments between seven main cities in the HSR corridor, T-J should be given priority to operate trains with reserved mode; the segment between X and J deserves to reserve most carriages during the peak month in the future. Specifically, our model suggests reserving 5.3–10.1 carriages/day for J-X, and 4.8–16.3 carriages/day for X-J during the peak month from 2019 to 2022.

Suggested Citation

  • Hanlin Gao & Meiqing Zhang & Anne Goodchild, 2020. "Empirical Analysis of Relieving High-Speed Rail Freight Congestion in China," Sustainability, MDPI, vol. 12(23), pages 1-16, November.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:23:p:9918-:d:452105
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/23/9918/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/23/9918/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Xueqiao Yu & Maoxiang Lang & Yang Gao & Kai Wang & Ching-Hsia Su & Sang-Bing Tsai & Mingkun Huo & Xiao Yu & Shiqi Li, 2018. "An Empirical Study on the Design of China High-Speed Rail Express Train Operation Plan—From a Sustainable Transport Perspective," Sustainability, MDPI, vol. 10(7), pages 1-19, July.
    3. Rotem-Mindali, Orit & Salomon, Ilan, 2007. "The impacts of E-retail on the choice of shopping trips and delivery: Some preliminary findings," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(2), pages 176-189, February.
    4. Pazour, Jennifer A. & Meller, Russell D. & Pohl, Letitia M., 2010. "A model to design a national high-speed rail network for freight distribution," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(3), pages 119-135, March.
    5. Rowangould, Gregory, 2013. "Public financing of private freight rail infrastructure to reduce highway congestion: A case study of public policy and decision making in the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 57(C), pages 25-36.
    6. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    7. Jeong, Seung-Ju & Lee, Chi-Guhn & Bookbinder, James H., 2007. "The European freight railway system as a hub-and-spoke network," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(6), pages 523-536, July.
    8. Zhao, Jian & Zhao, Yunyi & Li, Ying, 2015. "The variation in the value of travel-time savings and the dilemma of high-speed rail in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 130-140.
    9. Behiri, Walid & Belmokhtar-Berraf, Sana & Chu, Chengbin, 2018. "Urban freight transport using passenger rail network: Scientific issues and quantitative analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 227-245.
    10. Bi, Mingkai & He, Shiwei & Xu, Wangtu (Ato), 2019. "Express delivery with high-speed railway: Definitely feasible or just a publicity stunt," Transportation Research Part A: Policy and Practice, Elsevier, vol. 120(C), pages 165-187.
    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. Li, Siqiao & Zhu, Xiaoning & Shang, Pan & Li, Tianqi & Liu, Wenqian, 2023. "Optimizing a shared freight and passenger high-speed railway system: A multi-commodity flow formulation with Benders decomposition solution approach," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 1-31.
    2. Cartenì, Armando & Pariota, Luigi & Henke, Ilaria, 2017. "Hedonic value of high-speed rail services: Quantitative analysis of the students’ domestic tourist attractiveness of the main Italian cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 348-365.
    3. Zhenni Ding & Huayou Chen & Ligang Zhou, 2023. "Using shapely values to define subgroups of forecasts for combining," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 905-923, July.
    4. Nieto, María Rosa & Carmona-Benítez, Rafael Bernardo, 2018. "ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 1-8.
    5. Sabaj, Ernil & Kahveci, Mustafa, 2018. "Forecasting tax revenues in an emerging economy: The case of Albania," MPRA Paper 84404, University Library of Munich, Germany.
    6. Hu, Wanjie & Dong, Jianjun & Hwang, Bon-Gang & Ren, Rui & Chen, Zhilong, 2022. "Is mass rapid transit applicable for deep integration of freight-passenger transport? A multi-perspective analysis from urban China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 490-510.
    7. Sijing Liu & Jiuping Xu & Xiaoyuan Shi & Guoqi Li & Dinglong Liu, 2018. "Sustainable Distribution Organization Based on the Supply–Demand Coordination in Large Chinese Cities," Sustainability, MDPI, vol. 10(9), pages 1-25, August.
    8. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    9. Steinbuks, Jevgenijs, 2019. "Assessing the accuracy of electricity production forecasts in developing countries," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1175-1185.
    10. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    11. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    12. Xueqiao Yu & Maoxiang Lang & Yang Gao & Kai Wang & Ching-Hsia Su & Sang-Bing Tsai & Mingkun Huo & Xiao Yu & Shiqi Li, 2018. "An Empirical Study on the Design of China High-Speed Rail Express Train Operation Plan—From a Sustainable Transport Perspective," Sustainability, MDPI, vol. 10(7), pages 1-19, July.
    13. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    14. Carlo Altavilla & Paul De Grauwe, 2010. "Forecasting and combining competing models of exchange rate determination," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3455-3480.
    15. M. Hashem Pesaran & Paolo Zaffaroni, 2004. "Model Averaging and Value-at-Risk Based Evaluation of Large Multi Asset Volatility Models for Risk Management," CESifo Working Paper Series 1358, CESifo.
    16. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    17. Fernando M. Duarte & Carlo Rosa, 2015. "The equity risk premium: a review of models," Economic Policy Review, Federal Reserve Bank of New York, issue 2, pages 39-57.
    18. Jakub Nowotarski, 2013. "Short-term forecasting of electricity spot prices using model averaging (Krótkoterminowe prognozowanie spotowych cen energii elektrycznej z wykorzystaniem uśredniania modeli)," HSC Research Reports HSC/13/17, Hugo Steinhaus Center, Wroclaw University of Technology.
    19. Chen, Shaopei & Claramunt, Christophe & Ray, Cyril, 2014. "A spatio-temporal modelling approach for the study of the connectivity and accessibility of the Guangzhou metropolitan network," Journal of Transport Geography, Elsevier, vol. 36(C), pages 12-23.
    20. Fifić, Mario & Gigerenzer, Gerd, 2014. "Are two interviewers better than one?," Journal of Business Research, Elsevier, vol. 67(8), pages 1771-1779.

    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:12:y:2020:i:23:p:9918-:d:452105. 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.