Urban high-speed railway construction is complex due to limited land resources, high population density, and potential construction risks, especially when new tracks are parallelly aligned to operational railways. Addressing a gap in current literature on fine optimization of manual alignment in such scenarios, this paper introduces a biobjective approximate fine-grained optimization model for railway alignments (BA-FORA). Utilizing an approximate dynamic programming (ADP) method, BA-FORA effectively searches the feasible region to approach a global optimum, overcoming the dimensionality challenges inherent in standard dynamic programming (DP). This paper presents a biobjective optimization framework that takes into account both construction cost and construction risk adjacent to existing operating railways (CRAEOR), offering a method for the fine-grained design of new railways adjacent to existing railways. Finally, the proposed BA-FORA framework is applied to practical cases, demonstrating its superior optimization performance. The findings indicate that the BA-FORA model can autonomously investigate and enhance railway alignment. It generates cost-effective and low-risk solutions exceeding manual efforts, ensuring alignment constraint compliance.

中文翻译：

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平行现有铁路的铁路线形细粒度设计的双目标优化

由于土地资源有限、人口密度高以及潜在的建设风险，城市高速铁路建设十分复杂，特别是当新线与运营铁路平行时。针对当前此类场景下手动线形精细优化文献中的空白，本文提出了一种铁路线形双目标近似细粒度优化模型（BA-FORA）。BA-FORA 利用近似动态规划 (ADP) 方法，有效地搜索可行区域以接近全局最优值，克服了标准动态规划 (DP) 固有的维数挑战。本文提出了一种同时考虑现有运营铁路邻近建设成本和建设风险的双目标优化框架（CRAEOR），为现有铁路邻近新铁路的细粒度设计提供了方法。最后，将所提出的BA-FORA框架应用于实际案例，展示了其优越的优化性能。研究结果表明 BA-FORA 模型可以自主研究和增强铁路线形。它生成的解决方案具有成本效益且风险低，超出了手动工作，确保了对齐约束合规性。