With the advantages of high speed and punctuality, urban rail transit has become the preferred choice for many commuters. However, overcrowding of urban rail transit lines during peak hours is a common problem in megacities owing to the excessive passenger demand. To address this problem, this study proposes an integrated optimization method that combines passenger flow control and bus-bridging services. An integer nonlinear optimization model is developed to minimize the weighted passenger waiting time and operating cost of bus-bridging services by considering the coupling relationships between passenger movements on both the metro and buses. Then, the proposed model is equivalently transformed into a linear form with better mathematical properties, and variants of this model are further formulated to extend the usability. Finally, real-world case studies based on the operational data of the Beijing Metro Batong line are conducted to verify the effectiveness of the proposed methods. The computational results indicate that the proposed approach can effectively alleviate overcrowding and reduce passengers' waiting time, thereby improving the operational efficiency and safety of the urban rail transit system.@en

Due to the advantages of high punctuality and speed, urban rail transit systems are attracting more and more commuters. In consequence, overcrowding during peak periods has become the norm in megacities, which poses a great challenge to operations. To address this challenge, this paper investigates the integrated optimization of passenger flow control and bus-bridging on oversaturated urban rail transit lines. Based on the time-dependent passenger demand, we propose an integer nonlinear programming model that aims to minimize the overall system cost. Then, to cope with the solving difficulty due to nonlinearity, the proposed formulation is further transformed into an equivalent mixed-integer linear programming model. Finally, real-world case studies based on the operating data of Beijing metro Batong line are applied to verify the effectiveness of our proposed approach. The computational results illustrate that our proposed method can significantly reduce the average passenger waiting time and improve operational safety.@en