A framework for urban railway transit system planning with demographic distribution and travel efficiency

This study introduces a modeling framework for urban railway transit system planning, addressing the station location determination, network design, and line planning optimization problems. The framework relies solely on gridded population and employment data, which can be sourced from mobile signaling data or open-access datasets by aggregating commuters’ residential and workplace counts within predefined geometric grids. It also incorporates origin-destination passenger demand for the generated stations. By maximizing service coverage and passenger efficiency and minimizing construction costs, the framework successively generates high-quality schemes for stations, networks, and line configurations. The proposed iterative procedures, governed by modified Artificial Bee Colony and Tabu Search algorithms with diversified neighborhood construction, solve these three optimization problems. Real-world instances and experimental results validate the framework's correctness and effectiveness. Case studies in Guangzhou demonstrate the practical application of the proposed framework, providing valuable insights for planners to obtain high-quality urban rail transit schemes with minimal data.