## 5 Conclusions  
The rapid development of urban rail transit has brought about huge energy consumption problems due to its large capacity and long transportation hours. With the development concept of \"low carbon\" and \"sustainable development\", how to reduce train traction energy consumption has become an important element in the sustainable development of rail transit. The focus of this paper is to reduce the train traction energy consumption while reducing the waiting time of passengers on the platform to satisfy their travel demands. Thus, a DRLDA algorithm was proposed for multi-train energy-saving collaborative optimized control to deal with complicated multidimensional constraints. Firstly, the change-point algorithm was used to find the changing points in the time series of passenger flow curve, which constitute the variation point sets. The wavelet variation method was used for denoising to ensure the authenticity of the data. Then, the train operation between stations was studied to determine the combination of train operating conditions, and a single train's energy consumption was calculated. Considering the multidimensional conditions, the single-train energy-saving control model was established, the goal of which was to minimize the multi-train traction energy consumption and the average passenger waiting time. An ARMA-RBFNN algorithm was proposed and applied to implement the single-train energy-saving control strategy. Next, the queuing theory was introduced to study the relationship between passenger waiting time and train arrival intervals and stopping times. The multi-train cooperative energy-saving control model was established based on many factors and conditions. The global optimal value of the multi-train cooperative energy-saving control model was solved by the Dragonfly Algorithm. The DRLDA algorithm was designed to handle multi-train cooperative energy-saving control, and a proactive constraint handling system was added to DRL to adjust learning directions and avoid learning oscillations. The simulation results demonstrated that the MAPE of ARMA-RBFNN was 2.23%; this value was the smallest, which is superior to other algorithms. Moreover, for a single train, the overall energy-saving efficiency was 4.51% and the average passenger waiting time was extended by 0.8 s. For multiple trains, the DRLDA showed advantages over DDPG-RS and DQN-RS in accelerating the learning convergence process and obtaining smooth results, satisfying multidimensional constraints. At the same time, the total traction energy consumption and passenger waiting time were reduced by 3.1% and 5 s, respectively, achieving the energy-saving effect. In the future, our research will mainly focus on the following major aspects: (1) When calculating the passenger waiting time according to the actual situation, this time changes, and can be set to an interval range to restore the authenticity of the data in the future. (2) The multi-train collaborative control model, in the case of interchange, should be studied in depth. (3) We hope to obtain more concrete operational data to study transfer behavior in a larger-scale rail transit network. Conceptualization, L.Q. and L.D. ; methodology, L.Q. and L.D. ; formal analysis, L.Q. and L.D. ; investigation, X.X. and L.Z. ; validation, L.Q. and L.D. ; visualization, L.Q., X.Q. and L.D. ; writing--original draft preparation, L.D. ; writing--review and editing, L.D. supervision, L.Z. ; project administration, X.X. and L.Z. ; funding acquisition, X.X. and L.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant No. 61741303), the National Natural Science Foundation of China (Grant No. 62262011) and the Natural Science Foundation of Guangxi (Grant No. 2021 JJA170130). **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data used to support the findings of this study are available from the first author upon request.