Data-driven Timetable Design and Passenger Flow Control Optimization in Metro Lines

As travel demands in metro systems continue to grow rapidly, the mismatch between passenger demand and metro capacity has become a critical challenge in metro operations. To address this issue, this paper investigates the collaborative optimization of train timetables and station-based passenger flow control under stochastic demand, which aims to minimize the total system cost while ensuring an adequate service level to each station. We formulate the research problem as a stochastic mixed-integer programming model with expected travel time cost constraints for each station and translate it into a multi-objective attainability problem by imposing a target on the objective value. We develop an efficient operation policy that determines the timetable and flow control decisions in response to each demand scenario, satisfying the objective and service level targets in the long term when feasible. We conduct extensive numerical experiments on both synthetic and real-world transit data to evaluate the performance of our approach. The results demonstrate that our approach outperforms the benchmark first-come-first-served policy in terms of efficiency and service fairness under both exogenous and endogenous demand distributions. The improvement achieved by our approach is attributed to the prioritization of short trips over long ones, effectively exploiting the reusable nature of train capacity.