Sampling‐based modified ant colony optimization method for high‐speed rail alignment development

Abstract

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.

Biobjective optimization for railway alignment fine‐grained designs with parallel existing railways

“…In a related vein, the study by Hasany and Shafahi (2017) introduced an optimization approach using ant colony algorithms to tackle the primary challenge and achieve a favorable solution. Complementing this, the research by Roy and Maji (2022) introduced a modified ant colony optimization algorithm, known as sampling‐based modified ant colony optimization (SMACO), that employed low‐discrepancy point sampling. The algorithm sought to achieve optimized HAs for high speed railway, prioritizing reduced costs and minimal impacts, especially in terms of noise and vibration.…”

Weekly line planning to improve the match between rail supply and passenger demand

“…Both biology-and physics-based algorithms have been used in railway engineering and planning scenarios, thus demonstrating their effectiveness. Examples include optimizing rail alignment with the ant colony algorithm (Roy & Maji, 2022) and deep learning (Gao et al, 2022), determining rail maintenance by designing an evolution strategy and greedy metaheuristic (Oudshoorn et al, 2022), optimizing rail alignments and station locations using particle swarm-based algorithm (Song et al, 2022), and performing rail surface detection and estimating railway track longitudinal irregularities by applying deep learning (Li et al, 2022;Wu et al, 2022). With regard to relevant studies of line planning, Szeto and Jiang ( 2014 The widely applied GA shows excellent adaptability to the LPP, in that, the crossover and mutation process of chromosomes is a good formulation of the updating process of trains in line plans or timetables.…”

Collaborative passenger flow control for an urban rail transit network

“…The literature on train operation has mainly focused on optimization models for train schedules to address the mismatch between travel demand and transport capacity (Wang et al., 2023). Although urban rail transit alignment (Roy & Maji, 2022; Song et al., 2022) is considered during construction to expand the range of services and maintenance plans are made in advance to ensure the stability of transportation services (Chang et al., 2023; Oudshoorn et al., 2022), it is difficult to optimize train operations after the train capacity reaches a given threshold during peak hours. Therefore, to reduce passenger flow congestion, it is necessary to conduct further research on passenger flow control.…”