A capacity adaption optimization model for supernormal operation of urban rail transit networks

Abstract

In response to urban rail transit capacity shortage, a capacity adaption optimization model for the new mixed train operation mode is proposed in the supernormal operation of urban rail transit networks. First, the characteristics of express-local train and long-short route slow train are analyzed to obtain the main influencing factors of capacity adaption. Moreover, various train types are simultaneously classified and recombined in the adaption train operation mode to maximize transportation capacity for passengers. A nonlinear mixed-integer programming model is established to maximize the total number of trains, improving train load rate and reducing the total passenger travel time. In addition, the actual case of Beijing Metro Line 1 is solved to demonstrate the feasibility and effectiveness of the proposed model. The optimal passenger travel time-saving rates of the morning and evening peak hours are 12.1% and 11.5%, respectively. The optimal utilization rates of capacity in the morning and evening peak hours are 79.5% and 84.5%, respectively. Meanwhile, the optimal load factor within the range of 0.7–0.9 would benefit the utilization of train resources and the passengers’ service level. The optimized results show that the proposed capacity adaption optimization model benefits for high service level and practical significance and rationality for operators.