Multi-Objective Optimization of Train Scheduling Using Genetic Algorithm With Short-Turning Strategy and Non-Uniform Headway

With the rapid development of urban rail transit, how to reasonably arrange train scheduling during peak hours has become a key issue that needs to be urgently solved. Traditional dispatching methods have difficulty coping with the dynamic changes in passenger demand, resulting in unreasonable capacity allocation, increased operating costs, and increased environmental burden. To deal with this problem, this paper proposes a multi-objective train operation optimization scheme based on headway and turnaround stations of short routes. By taking the turnaround stations of the short route as the decision variables and adjusting them with the non-uniform headway, an optimization model is constructed with passenger delay cost, enterprise operating cost, and carbon emission cost as the objective functions. Using the actual operating data of Wuhan Metro Line 5 as an example, the genetic algorithm was used in MATLAB to solve the model. The results of the numerical experiments show that the total cost after optimization by this scheme is reduced by 37.1 % and 17.5 % compared to the other two schemes, respectively. This study provides new ideas for achieving sustainable urban rail transit operations.