Multi-objective optimization for through train service integrating train operation plan and type selection

Providing effective Through Train Services (TTSs) faces challenges due to complex infrastructure conditions, train performances and passenger demands. To enhance TTSs between two different classes of urban rail transit lines with variations in train speed and capacity, we propose a multi-objective Integer Non-Linear Programming (INLP) model. This model maximizes passenger travel time savings and average train load utilization, and develops an integrated approach to simultaneously optimize the frequencies of through express trains and local trains, as well as the operation zones, stopping patterns and type selection of through trains. Additionally, a Non-Dominated Sorting Genetic Algorithm II is designed to solve the INLP model based on a simple test network and a real-world case from the Nanjing Subway. The unique benefits of our proposed method are demonstrated by a comprehensive compared with the Single Line Operation Mode and the all-stop plans under Through Operation Mode.