Integrated demand-side management and timetabling for an urban transit system

Abstract

The intelligent upgrading of metropolitan rail transit systems has enabled the implementation of demand-side management policies with integrated multiple operational strategies in real-world applications. However, the strong interdependence between supply and demand requires a coordinated approach that combines demand-side management policies with supply-side resource allocations to optimize the urban rail transit ecosystem. This study introduces a mathematical and computational framework to optimize train timetables, passenger flow control strategies, and trip-shifting plans via pricing policies. An integer linear programming model is proposed for this problem. To enhance computational efficiency, a Benders decomposition-based algorithm within the branch-and-cut method is developed. Computational results demonstrate that the proposed method outperforms the state-of-the-art commercial solver in computational efficiency. High-quality solutions, including optimal ones, are obtained at the root node with minimal branching, thanks to the novel decomposition framework and valid inequalities.