The vertical alignment optimization is about developing a minimum cost curvilinear vertical profile of constrained grade sections and appropriate non‐overlapping vertical curves passing through fixed control points with elevation constraints. Variations in ground profile and discreteness in unit cutting and filling costs make it a non‐convex, noisy, constrained optimization problem with many local minima. Further, the gradient related constraints and vertical curvature are non‐linear. This paper presents an innovative exploring and exploiting ant colony optimization (E&E‐ACO) algorithm with an appropriate point sampling, vertical curve fitting strategies, and an intuitive feasible region identification approach for solving the vertical alignment optimization problem. The E&E‐ACO algorithm extensively explores the feasible search space to generate a set of potential solutions and effectively exploit the space around the potential solutions for developing the optimized vertical alignment. The efficacy of the proposed method is demonstrated using two case studies. In one case study, the optimized solution by the proposed method had a marginally better objective function value and about three times lesser computational time than the solution by the mesh adaptive direct search method. The optimized alignment satisfied the elevation constraints of fixed control points and imitated the manually designed real‐world vertical alignment. The linearly varying exploration and exploitation parameters had better convergence rate than the other tested variations. Further, the proposed method at the end of 1000 iterations yielded about six times better result than the traditional ACO algorithm.

High-speed railway (HSR) alignment development is a complex and tedious problem due to an infinite number of possible solutions, the existence of nonlinear costs and impacts, and complex location and geometric design constraints. In this study, a low-discrepancy point sampling-based modified ant colony optimization (SMACO) algorithm for obtaining horizontal alignments with optimized HSR-specific cost and impact, including noise and vibration impacts, is proposed. The low-discrepancy sampling approach is used to identify the potential points of intersection (𝐻𝑃𝐼𝑠), from which appropriate intermediate 𝐻𝑃𝐼s are selected to develop feasible alignments using the SMACO algorithm.It effectively avoids restricted land parcels and satisfies HSR-related geometric design requirements. A real-world case study demonstrated that the HSR alignment obtained using the proposed method was marginally better than the path planner method-based alignment and the constructed alignment. The sensitivity analysis highlighted the impact of two key parameters, that is, the right of way widths and noise and vibration screening distances on the HSR alignment development. This study advances the alignment development automation, particularly the HSR horizontal alignment for design speeds over 180 km/h. It facilitates extensive search space exploration independent of infeasible regions, identifies and selects 𝐻𝑃𝐼 without being constrained to prespecified locations and a userdefined number, and proposes a suitably modified ACO algorithm for the HSR alignment development.

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High speed railway (HSR) planners aim to select locations that optimize the overall utility or benefit of HSR stations by satisfying various desirable requirements. Among other factors, accessibility and environmental impact are important considerations for HSR station location selection. The desirable requirements of these two factors include improved access to, and intermodal integration with, existing transportation facilities and services (like airports, train stations, and bus stops); avoidance of environmentally sensitive areas (such as water bodies, wetlands, and forest) and land with higher right-of-way costs; and accommodation of strategic necessities (for example, proximity to city centers and socioeconomic development hubs). This study quantifies the overall utility of an HSR station by analyzing the extent to which a location satisfies these desirable requirements. For this, suitable utility functions were developed and evaluated. To obtain individual utility scores, appropriate weights were assigned based on relative importance. The overall utility of a location was then estimated as the weighted summation of these utility scores. A GIS-based analytical framework was specifically developed for geo-processing, mapping, and visualization of the geospatial data analysis and result representation. This utility-based quantification and identification process would be useful to planners in assessing an area and determining the most suitable station locations for an HSR project. The proposed model was used to identify the potential station locations along the Mumbai-Ahmedabad HSR corridor in India and to compare the obtained results with the planned locations of the project.