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

Hanlin Gao, Meiqing Zhang and Anne Goodchild
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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

Sustainability, 2020, vol. 12, issue 23, 1-16

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.

Keywords: freight on transit; influential factor; flow volume forecast; segment occupancy; mode adoption (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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