The timetable of a metro system is essential for trains running safely and efficiently. For the design of a timetable, the potential power interruptions of energy supplies from the substations to the trains should be borne in mind. In case potential power interruptions occur, the backup running plan should be completed responsively to accommodate the passenger and energy demands In this study, we propose an energy supplementation strategy utilizing regenerative energy from decelerating trains in the event of power interruptions and develop a tri-objective optimization model incorporating passenger travel time, potential power interruption, and energy consumption. Particularly, for calculating regenerative energy utilization, we suggest a many-to-many energy allocation mechanism between decelerating and accelerating trains based on the real-time energy demands and supplies. A heuristic algorithm is developed to obtain a regular and cyclic timetable for minimizing passenger travel time, the potential power interruptions, and energy consumption. The suggested model and algorithm are tested based on the smart-card data collected from a bidirectional metro line in Beijing (China). The results show that the suggested approach significantly improves energy efficiency, reduces passenger waiting time, and decreases power interruption risks, compared with the currently used scheduling method.

An advanced metro system becomes imperative towards efficient and sustainable operations with the rapid growth of urban construction. As the essential factors to implement transport sustainability, a referred energy-efficient trajectory and an energy-saving infrastructure for train operations could guide trains running through with conquering complicated environmental impacts and help to control the energy flows in traction processes. Thus, we attempt to discover the optimal way of adapting to the track changes and resistances in train running and evaluate the energy exchanges with Energy Storage System (ESS) in an extended power supply network. In this study, we firstly optimize a train trajectory with consideration of air and track resistances by using a modified Q-Learning (QL) method. Secondly, for coordinated application between ESS and trains, an integration module is designed by implanting train trajectories. Specifically, based on the working properties of ESS, a deceleration-acceleration time separation is matched for supplementing the charging/discharging times of the devices. The space-time presentation is introduced to portray train running phases for connections with control processes of ESS. Then, a Mixed Integer Linear Programming (MILP) model is proposed for minimizing deceleration-acceleration overlaps. A Tabu Search (TS) algorithm is introduced to solve such complex problems. Finally, a numerical test is conducted to demonstrate the feasibility of the model in Beijing, China.

The crew re-scheduling problem (CRSP) is a critical challenge in the operation and management of urban rail transit (URT) systems, especially when restoring service after disruptions. When a crew member unexpectedly leaves duty due to emergency events like illness, the train assigned to that crew may get stranded in one operating direction, which will block the following trains operating in the same direction. To address this issue, this study first introduces a closed-loop driving strategy. This strategy reallocates limited crew resources across both operating directions to maintain the basic operations of the URT system during emergency periods. On this basis, an integer linear programming (ILP) model is developed to describe the dynamic adjustments of train departure times. Valid inequalities are incorporated to generate feasible crew task sets rapidly, and the proposed model is solved by using Gurobi. To meet the stringent time requirements for rescheduling during disruptions, an improved greedy algorithm is further designed to manage crew assignment under emergency conditions efficiently. Finally, the effectiveness of the proposed approach is evaluated through a real-world case study based on the Beijing urban rail transit network. The results demonstrate that the proposed model can respond rapidly within 30 min after an incident occurs. It not only limits the generation time of each crew task to within 1 min but also achieves a relative working balance between crews by combining short-duration tasks.