Determinations of alignments and station locations are major tasks in railway design, which jointly and fundamentally influence the construction and operation of the whole project. Currently, although considerable efforts have been invested into the separate optimization of alignments or station locations, the complex interactions between alignments and stations, as well as their concurrent impacts on railways still need further investigations. Therefore, this study focuses on modeling and optimization of passenger railway alignments and station locations simultaneously. A novel optimization model is formulated for maximizing net present value (NPV) considering comprehensive costs and revenue from riders over the railway's design life. Specifically, the comprehensive cost includes construction and operation costs of alignments and stations. The railway ridership is quantified by combining travel time and trip coverage using a logit function. Then, the railway revenue is estimated and combined with costs through an economic growth model to obtain the NPV. To solve this model, two customized and mutually exclusive methods, that is, the station-alignment-integration (SAI) and alignment-station-integration (ASI) methods, are developed based on a particle swarm algorithm. The SAI primarily searches for possible stations in the landscape and then generates alignments to connect adjacent stations. The ASI directly yields the main alignment, on which potential stations are located by handling specific constraints. The model and methods are applied to an actual high-speed railway. Results show that both SAI- and ASI-generated railways have higher NPV values compared to the corresponding railway designed by experienced designers. Sensitivity analysis reveals that SAI is more flexible than ASI in solving actual cases. The convergence of the proposed methods is also discussed. With some changes in input parameters based on locally applicable design standards, the proposed methods are generally applicable to other cases or countries.

中文翻译：

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专用高速铁路的 3D 路线和车站位置的同时优化

定线和站址的确定是铁路设计的主要工作，对整个工程的建设和运营有着共同的、根本性的影响。目前，虽然在线形或车站位置的单独优化上投入了大量精力，但线形和车站之间复杂的相互作用以及它们对铁路的共同影响仍有待进一步研究。因此，本研究的重点是同时对客运铁路线形和车站位置进行建模和优化。考虑到铁路设计寿命期间乘客的综合成本和收入，制定了一种新的优化模型，以最大化净现值（NPV）。具体而言，综合成本包括线路和车站的建设和运营成本。通过使用 logit 函数结合旅行时间和旅行覆盖率来量化铁路乘客。然后，通过经济增长模型估计铁路收入并与成本相结合，得到净现值。为求解该模型，基于粒子群算法开发了两种定制且互斥的方法，即站位对齐积分（SAI）和对齐站位积分（ASI）方法。SAI 主要在景观中搜索可能的站点，然后生成路线以连接相邻站点。ASI 直接产生主路线，通过处理特定的约束条件来定位潜在站点。该模型和方法应用于实际的高速铁路。结果表明，与经验丰富的设计师设计的相应铁路相比，SAI 和 ASI 生成的铁路都具有更高的 NPV 值。敏感性分析表明，在解决实际案例时，SAI 比 ASI 更灵活。还讨论了所提出方法的收敛性。随着基于当地适用的设计标准的输入参数的一些变化，所提出的方法通常适用于其他情况或国家。