Optimization of urban rail train operation plans with supply-demand coordination under green orientation

To address the issues of underutilized transport capacity, low passenger demand satisfaction, and high enterprise operating costs caused by unreasonable train operation plans, this study proposes a supply-demand coordinated train operation plan for urban rail transit based on the principle of traffic resource optimization allocation. Incorporating passenger travel behaviors and travel preferences, a multi-objective optimization model is established. The objectives are to maximize train supply-demand coordination, minimize the total passenger travel costs, and minimize the total enterprise operating costs. The model is subject to constraints such as carbon emissions, train departure frequency and line throughput capacity. An adaptive annealing-whale optimization algorithm is designed to solve the model, and its effectiveness is verified through a case study. The results show that, compared to the full-length route with multi-type formation, short-turn route with short formation, and short-turn route with long formation models, the short-turn route with multi-type formation model not only optimizes carbon emissions but also improves train supply-demand coordination by 185.92, 53.15 and 36.07, respectively. It also reduces the total passenger travel costs by 17.8%, 11.45% and 8.34%, and decreases the total enterprise operating costs by 29.09%, 5.67% and 4.97%, respectively. Furthermore, compared with the whale optimization algorithm and simulated annealing algorithm, the proposed adaptive annealing-whale optimization algorithm improves the fitness value by 7.55% and 5.65%, and convergence speed by 4.46% and 7.55%, respectively, while optimizing all objectives. Therefore, the proposed model effectively enhances passengers, enterprise costs as well as carbon emissions, enhances supply-demand coordination. The designed algorithm demonstrates high solution performance in satisfying the model's requirements.