Pricing Strategy for High-Speed Rail Freight Services: Considering Perspectives of High-Speed Rail and Logistics Companies

Guoyong Yue, Mingxuan Zhao, Su Zhao, Liwei Xie and Jia Feng ()
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Guoyong Yue: School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Mingxuan Zhao: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Su Zhao: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Liwei Xie: Henan Railway Construction & Investment Logistics Group Co., Ltd., Zhengzhou 450046, China
Jia Feng: Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China

Sustainability, 2025, vol. 17, issue 14, 1-16

Abstract: It is well known that there is a significant conflict of interest between high-speed rail (HSR) operators and logistics companies. This study proposes an HSR freight pricing strategy based on a multi-objective optimization framework and a freight mode splitting model based on the Logit model. A utility function was constructed to quantify the comprehensive utility of different modes of transportation by integrating five key influencing factors: economy, speed, convenience, stability, and environmental sustainability. A bi-objective optimization model was developed to balance the cost of the logistics with the benefits of high-speed rail operators to achieve a win–win situation. The model is solved by the TOPSIS method, and its effectiveness is verified by the freight case of the Zhengzhou–Chongqing high-speed railway in China. The results of this study showed that (1) HSR has advantages in medium-distance freight transportation; (2) increasing government subsidies can help improve the market competitiveness of high-speed rail in freight transportation. This research provides theoretical foundations and methodological support for optimizing HSR freight pricing mechanisms and improving multimodal transport efficiency.

Keywords: multi-objective optimization model; high-speed rail freight; pricing optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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