Optimization of Metro Feeder Lines in the Monterrey Metropolitan Area Using Multi-objective Genetic Algorithms: Preliminary Evaluation Chapter in Scopus uri icon

abstract

  • Public transportation plays a vital role in urban mobility, yet rapid urbanization has strained existing systems, leading to congestion, service inefficiencies, and environmental degradation. This study addresses transportation challenges in the Monterrey Metropolitan Area (MMA) in Nuevo León, Mexico, where increasing demand for public transport has resulted in overcrowding, limited accessibility, and increased dependence on private vehicles. Despite metro expansion projects, concerns persist regarding their coverage, affordability, and capacity to meet actual mobility needs. To address these issues, we propose a data-driven optimization framework based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to design metro feeder lines. The approach integrates socioeconomic data, georeferenced origin-destination matrices, and geographic accessibility constraints derived from AGEB-level analysis to model user demand and spatial distribution. Multiple population initialization strategies and problem-specific genetic operators were incorporated to enhance solution feasibility and diversity. The algorithm was applied to a representative instance of the MMA using real-world data, with results indicating the potential to reduce total travel time and increase user coverage while maintaining operational efficiency. A comparative analysis of initialization strategies revealed trade-offs between the quality of the solution and the computational cost, supporting the adaptability of the method to different planning scenarios. By optimizing integration between residential areas and the metro system, this approach contributes to the development of sustainable, accessible, and efficient public transportation. The proposed methodology can serve as a foundation for smart mobility planning in metropolitan regions facing similar urban challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

publication date

  • January 1, 2025