Data-Driven Feeder-Bus Network Design with Multiobjective Optimization

Here you can find detailed results about the article "Data-Driven Feeder-Bus Network Design with Multiobjective Optimization".Abstract: Transportation systems increasingly rely on data-driven decision support tools to optimize multimodal transit operations. This paper presents an integrated framework for feeder-bus network design that combines machine learning-based demand synthesis with multiobjective evolutionary optimization. The methodology addresses a critical challenge in metropolitan transit planning: designing feeder routes that efficiently connect peripheral areas to high-capacity rail systems while balancing competing objectives of service coverage and operational efficiency. Using publicly available data (census indicators, socioeconomic indices, origin--destination surveys, and transit infrastructure) we develop two neural network models to predict trip destinations and public transport usage. These models power a Monte Carlo demand generator that produces temporally resolved origin--destination matrices at metropolitan scale. A tailored NSGA-II algorithm with station-preserving encoding and capacity-aware operators then optimizes feeder routes under vehicle capacity and duty-time constraints. Applied to the Monterrey Metropolitan Area (Mexico), the framework generates feeder networks that increase passengers served by approximately 28\% and reduce total travel time by 17\% compared to the current configuration, using the same fleet size. The approach demonstrates how accessible data sources and intelligent optimization techniques can substantially improve transit connectivity and accessibility in growing metropolitan areas with limited real-time data infrastructure.<br>Git Hub with NSGA-II code: https://github.com/Sam126RV/Optimization-of-Metro-Feeder-Lines-NSGAii.git