GSOANR-based multi-objective train trajectory optimization

Railway transport is an important mode of transportation in China, UK, and worldwide. It is expected to play an even more important role in transportation decarbonization in shifting more passenger trips and freight transportations to railway. It is strategically important to reduce the energy consumption and improve the overall energy efficiency of the railway systems. The introduction of automatic train operation (ATO) systems in electrified railway systems makes it possible to achieve energy consumption reduction while satisfying other railway operation criteria such as the need for passenger comfort, train punctuality, and safety. Although energy saving of railway systems has attracted substantial interest in recent years, ATO systems are not environmentally friendly as expected and there is a lack of a set of energy saving performance measures for railway system planning and operation. In this paper, the features of ATO systems are incorporated into the problem formulation, and a modified GSO algorithm with adaptive neighbourhood range (namely the GSOANR algorithm) is proposed as the solver of the problem. The proposed method is applied to the trajectory planning of a 58-km-long route between Heishan North Station and Fuxin Station of the Beijing-Shenzhen Line in China, and the results confirm that the train operation trajectory generated by the GSOANR algorithm-based optimization method can reduce the electrical energy consumption by about 5.6% compared with the results generated by the standard GSO algorithms, confirming the efficacy of the proposed energy-saving measures and the effectiveness of the novel optimization method, and the referential significance for the study of the train trajectory optimization.