Research on Differential Pricing of High-Speed Railway Combination Considering Price Fairness Perception

Research on Differential Pricing of High-Speed Railway Combination Considering Price Fairness Perception | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on Differential Pricing of High-Speed Railway Combination Considering Price Fairness Perception Siye GUO, Yun JING, Siyuan WANG, Hengzhi LIU, Yiao LIU This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7015113/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract To address the issue of uneven daily passenger flow distribution within the same origin-destination pair on high-speed railways, characterized by significant disparities in peak and off-peak traffic intensity, this paper proposes a combined differential pricing method for high-speed railways that considers price fairness perception. Firstly, an improved gradient boosting decision tree (GBDT) feature extraction model is constructed, which, in conjunction with the Fuzzy C-Means clustering algorithm (FCM), achieves fine-grained segmentation of passenger load periods. Secondly, by incorporating the Extended Gini Coefficient, a bi-level programming model for combined differential pricing is developed. The upper-level objective maximizes transportation benefits while ensuring pricing fairness, and the lower-level objective minimizes the difference in passenger load intensity between peak and off-peak periods. Thirdly, an Improved Shuffled Frog Leaping Algorithm ISFLA-BIPM is designed to solve the model. Finally, a Bootstrap-based method is proposed to determine the update timing of the combined differential pricing nodes, dynamically reflecting the adaptability of the passenger transportation market to the pricing system. Using the Beijing South to Shanghai Hongqiao section of the Beijing-Shanghai High-Speed Railway as a case study, the proposed combined differential pricing strategy effectively balances the passenger load pressure across different periods, ensuring both the fairness of pricing strategies and the maximization of railway transportation enterprise revenue, while achieving a dynamic equilibrium between railway revenue and its transportation value. High-speed railway Differential pricing Pricing fairness BiLevel Programming Shuffled Frog Leaping Algorithm Bootstrap Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7015113","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499505416,"identity":"17babe62-958d-4219-9965-2152624d3404","order_by":0,"name":"Siye GUO","email":"","orcid":"","institution":"China Academy of Transportation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Siye","middleName":"","lastName":"GUO","suffix":""},{"id":499505417,"identity":"d164e65b-e217-4c11-84c5-d23d6b4d91a1","order_by":1,"name":"Yun 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Firstly, an improved gradient boosting decision tree (GBDT) feature extraction model is constructed, which, in conjunction with the Fuzzy C-Means clustering algorithm (FCM), achieves fine-grained segmentation of passenger load periods. Secondly, by incorporating the Extended Gini Coefficient, a bi-level programming model for combined differential pricing is developed. The upper-level objective maximizes transportation benefits while ensuring pricing fairness, and the lower-level objective minimizes the difference in passenger load intensity between peak and off-peak periods. Thirdly, an Improved Shuffled Frog Leaping Algorithm ISFLA-BIPM is designed to solve the model. Finally, a Bootstrap-based method is proposed to determine the update timing of the combined differential pricing nodes, dynamically reflecting the adaptability of the passenger transportation market to the pricing system. 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