In big cities, the metro lines usually face great pressure caused by huge passengers demand, especially during peak hours. When disruptions occur, passengers accumulate quickly at stations. It is of great importance for dispatchers to take passenger flow control into consideration for the traffic management to ensure passengers' safety and to maintain their satisfaction. This paper proposes an integrated disruption management model, which incorporates train rescheduling and passenger flow control. In this model, the train services can be short-turned, cancelled and rerouted, while the number of passengers entering a station is managed by controlling the station gates with consideration of the capacities of platforms and trains. Moreover, the number of passengers arriving at a station is calculated according to the origin-destination matrices. The objectives are to recover the train operation to the original timetable as soon as possible and to minimize the waiting time of passengers outside the stations. With the interaction between train services, passengers and station gates, an iterative metaheuristic approach is proposed to solve the integrated disruption management problem. Based on the data of a Beijing metro line, numerical experiments are conducted to test the proposed algorithm. The results demonstrate the importance of integrated disruption management and the effectiveness of our solution method.

Urban railway transit systems in big cities operate at high capacity and represent the main arteries of city transport networks. In current operations, infrastructure failures occur occasionally causing severe disruptions. In this research, we propose a novel integrated disruption management methodology for automatically rescheduling trains and controlling passenger flows for a given disruption. Our framework incorporates a train traffic management model together with a model for adjusting flows of passengers and aims to minimize the total delay of passengers, the number of denied passengers, adjustments to train services, and recover time. On the train side, we short-turn, cancel and reroute train services. On the passenger side, we reflow passengers according to a disrupted timetable and control station gates. We test our integrated disruption management approach on real-life cases and discover dependencies between delayed/denied passengers and traffic management. Our goal is developing practical solutions to this critical transportation problem that will lead to establishing advanced decision support systems to assist metro dispatchers.