Railway Alignment Optimization Based on Multiobjective Bi-Level Programming Considering Ecological Impact

Railway projects frequently pass through ecologically fragile regions and result in various ecological damages. The railway alignment determining the layout of structures plays an important role in ecological protection. This paper presents a railway alignment optimization model based on multiobject...

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Veröffentlicht in:IEEE transactions on intelligent transportation systems 2023-02, Vol.24 (2), p.1712-1726
Hauptverfasser: Yang, Dongying, Yi, Sirong, He, Qing, Liu, Dewei, Wang, Yifeng
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Sprache:eng
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Zusammenfassung:Railway projects frequently pass through ecologically fragile regions and result in various ecological damages. The railway alignment determining the layout of structures plays an important role in ecological protection. This paper presents a railway alignment optimization model based on multiobjective bi-level programming (MBRAO). The upper level of MBRAO is a horizontal alignment optimization for the minimization of investment and ecological impacts from tunnel drainage and train noise. A multiobjective evolutionary algorithm based on decomposition (MOEA/D) solves the horizontal alignment optimization and naturally maintains population diversity. The lower level of MBRAO is a vertical alignment optimization for investment minimization. A self-adaptive differential evolution (DE) algorithm adjusting essential parameters based on optimization status solves the vertical alignment optimization efficiently. MBRAO is applied in multistage at both levels to find the suitable numbers of horizontal and vertical points of intersection. This paper introduces three kinds of vertical feature data generated corresponding to horizontal feature data to define different site modification costs on the bridge, subgrade, and tunnel structures. A real-world case study at Wolong Reserve is studied to verify the effectiveness of MBRAO based on a customized geographic information system (GIS).
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3222445