Auxiliary Road Design and Optimization for Railway Construction in Mountainous Areas
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 12
Abstract
Designing and optimizing auxiliary roads to support railway construction in complex mountainous areas is challenging. This paper presents a two-stage optimization model for auxiliary road design. The first stage designs the network layout of auxiliary roads based on the minimum spanning tree method to reduce link costs among various railway auxiliary construction projects. The second stage involves designing and optimizing the alignment of the auxiliary roads based on the deep reinforcement learning approach to minimize construction costs. In addition, the different logistical relationships among various railway auxiliary construction projects are considered to optimize the total turnover volume of the auxiliary road. Finally, a real-world case study of auxiliary road design for a railway construction project in mountainous areas was conducted to verify the proposed method.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. The design data is proprietary, and in order to avoid unnecessary conflicts of interest, the design data requires permission for distribution.
Acknowledgments
This study was supported by the National Natural Science Foundation of China (U1934214 and 51878576), Sichuan Nature and Science Foundation Innovation Research Group Project NO. 2023NSFSC1975, the Science and Technology Program of Shandong Department of Transportation under award number 2022B30, and the Fundamental Research Funds for the Central Universities NO. A0920502052301-369.
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Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 12 • December 2023
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© 2023 American Society of Civil Engineers.
History
Received: Dec 6, 2022
Accepted: Aug 2, 2023
Published online: Sep 29, 2023
Published in print: Dec 1, 2023
Discussion open until: Feb 29, 2024
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