To realize a better automatic train driving operation control strategy for urban rail trains,an automatic train driving method with improved DQN algorithm(classical deep reinforcement learning algorithm)is proposed as...To realize a better automatic train driving operation control strategy for urban rail trains,an automatic train driving method with improved DQN algorithm(classical deep reinforcement learning algorithm)is proposed as a research object.Firstly,the train control model is established by considering the train operation requirements.Secondly,the dueling network and DDQN ideas are introduced to prevent the value function overestimation problem.Finally,the priority experience playback and“restricted speed arrival time”are used to reduce the useless experience utilization.The experiments are carried out to verify the train operation strategy method by simulating the actual line conditions.From the experimental results,the train operation meets the ATO requirements,the energy consumption is 15.75%more energy-efficient than the actual operation,and the algorithm convergence speed is improved by about 37%.The improved DQN method not only enhances the efficiency of the algorithm but also forms a more effective operation strategy than the actual operation,thereby contributing meaningfully to the advancement of automatic train operation intelligence.展开更多
East Japan Railway Company(JR East)is aiming to“realize driverless train operation”as one of the key measures to respond to rapid changes in the business environment.Currently,Automatic Train Operation(ATO)equipment...East Japan Railway Company(JR East)is aiming to“realize driverless train operation”as one of the key measures to respond to rapid changes in the business environment.Currently,Automatic Train Operation(ATO)equipment is not installed on the Shinkansen,but there are plans to introduce ATO or driverless operation in the near future.From 2018-2021,the Ministry of Land,Infrastructure,Transport and Tourism(MLIT)held the“ATO Technology Study Group for Railways”in which the concept of technical requirements necessary for driverless operation was discussed.In 2021,JR East conducted the GOA4 demonstration test on the Joetsu Shinkansen.In this test,we were able to confirm the basic functions of Shinkansen vehicles such as automatic departure control,speed control,fixed position stop control,and remote stop control using ATO.We aim to realize unattended operation(GOA4)for deadhead trains between Niigata Station and the Niigata Shinkansen Rolling Stock Center by the end of the 2020 s,and driverless operation(GOA3)for passenger trains of the Joetsu Shinkansen by the mid-2030s and continue to develop the necessary technologies and build systems.展开更多
With rapid development of the railway traffic, the moving block signaling system （MBS） method has become more and more important for increasing the track capacity by allowing trains to run in a shorter time-headway ...With rapid development of the railway traffic, the moving block signaling system （MBS） method has become more and more important for increasing the track capacity by allowing trains to run in a shorter time-headway while maintaining the required safety margins. In this framework, the tracking target point of the following train is moving forward with its leading train. This paper focuses on the energy saving tracking control of two successive trains in MBS. Nonlinear programming method is used to optimize the energy-saving speed trajectory of the following train. The real-time location of the leading train could be integrated into the optimization process. Due to simplicity, it can be used for online implementation. The feasibility and effectiveness are verified through simulation. The results show that the new method is efficient on energy saving even when disturbances present.展开更多
The determination and optimization of Automatic Train Operation(ATO) control strategy is one of the most critical technologies for urban rail train operation. The practical ATO optimal control strategy must consider m...The determination and optimization of Automatic Train Operation(ATO) control strategy is one of the most critical technologies for urban rail train operation. The practical ATO optimal control strategy must consider many goals of the train operation, such as safety, accuracy, comfort, energy saving and so on. This paper designs a set of efficient and universal multi-objective control strategy. Firstly, based on the analysis of urban rail transit and its operating environment, the multi-objective optimization model considering all the indexes of train operation is established by using multi-objective optimization theory. Secondly, Non-dominated Sorting Genetic Algorithm II(NSGA-II) is used to solve the model, and the optimal speed curve of train running is generated.Finally, the intelligent controller is designed by the combination of fuzzy controller algorithm and the predictive control algorithm, which can control and optimize the train operation in real time. Then the robustness of the control system can ensure and the requirements for multi-objective in train operation can be satisfied.展开更多
为提高干线铁路的运输效率和能力,欧洲铁路部门开始研究欧洲列控系统(ETCS)应用自动驾驶(ATO)的可行性。通过分析ETCS系统应用自动驾驶(ATO over ETCS)的研究背景和过程,阐述系统架构、工作原理、自动驾驶的功能需求以及系统规范的架构...为提高干线铁路的运输效率和能力,欧洲铁路部门开始研究欧洲列控系统(ETCS)应用自动驾驶(ATO)的可行性。通过分析ETCS系统应用自动驾驶(ATO over ETCS)的研究背景和过程,阐述系统架构、工作原理、自动驾驶的功能需求以及系统规范的架构体系。同时介绍即将投入运营的ETCS系统应用自动驾驶实例,并分析它们的结构和基本原理。展开更多
文摘To realize a better automatic train driving operation control strategy for urban rail trains,an automatic train driving method with improved DQN algorithm(classical deep reinforcement learning algorithm)is proposed as a research object.Firstly,the train control model is established by considering the train operation requirements.Secondly,the dueling network and DDQN ideas are introduced to prevent the value function overestimation problem.Finally,the priority experience playback and“restricted speed arrival time”are used to reduce the useless experience utilization.The experiments are carried out to verify the train operation strategy method by simulating the actual line conditions.From the experimental results,the train operation meets the ATO requirements,the energy consumption is 15.75%more energy-efficient than the actual operation,and the algorithm convergence speed is improved by about 37%.The improved DQN method not only enhances the efficiency of the algorithm but also forms a more effective operation strategy than the actual operation,thereby contributing meaningfully to the advancement of automatic train operation intelligence.
文摘East Japan Railway Company(JR East)is aiming to“realize driverless train operation”as one of the key measures to respond to rapid changes in the business environment.Currently,Automatic Train Operation(ATO)equipment is not installed on the Shinkansen,but there are plans to introduce ATO or driverless operation in the near future.From 2018-2021,the Ministry of Land,Infrastructure,Transport and Tourism(MLIT)held the“ATO Technology Study Group for Railways”in which the concept of technical requirements necessary for driverless operation was discussed.In 2021,JR East conducted the GOA4 demonstration test on the Joetsu Shinkansen.In this test,we were able to confirm the basic functions of Shinkansen vehicles such as automatic departure control,speed control,fixed position stop control,and remote stop control using ATO.We aim to realize unattended operation(GOA4)for deadhead trains between Niigata Station and the Niigata Shinkansen Rolling Stock Center by the end of the 2020 s,and driverless operation(GOA3)for passenger trains of the Joetsu Shinkansen by the mid-2030s and continue to develop the necessary technologies and build systems.
文摘With rapid development of the railway traffic, the moving block signaling system （MBS） method has become more and more important for increasing the track capacity by allowing trains to run in a shorter time-headway while maintaining the required safety margins. In this framework, the tracking target point of the following train is moving forward with its leading train. This paper focuses on the energy saving tracking control of two successive trains in MBS. Nonlinear programming method is used to optimize the energy-saving speed trajectory of the following train. The real-time location of the leading train could be integrated into the optimization process. Due to simplicity, it can be used for online implementation. The feasibility and effectiveness are verified through simulation. The results show that the new method is efficient on energy saving even when disturbances present.
文摘The determination and optimization of Automatic Train Operation(ATO) control strategy is one of the most critical technologies for urban rail train operation. The practical ATO optimal control strategy must consider many goals of the train operation, such as safety, accuracy, comfort, energy saving and so on. This paper designs a set of efficient and universal multi-objective control strategy. Firstly, based on the analysis of urban rail transit and its operating environment, the multi-objective optimization model considering all the indexes of train operation is established by using multi-objective optimization theory. Secondly, Non-dominated Sorting Genetic Algorithm II(NSGA-II) is used to solve the model, and the optimal speed curve of train running is generated.Finally, the intelligent controller is designed by the combination of fuzzy controller algorithm and the predictive control algorithm, which can control and optimize the train operation in real time. Then the robustness of the control system can ensure and the requirements for multi-objective in train operation can be satisfied.
文摘为提高干线铁路的运输效率和能力,欧洲铁路部门开始研究欧洲列控系统(ETCS)应用自动驾驶(ATO)的可行性。通过分析ETCS系统应用自动驾驶(ATO over ETCS)的研究背景和过程,阐述系统架构、工作原理、自动驾驶的功能需求以及系统规范的架构体系。同时介绍即将投入运营的ETCS系统应用自动驾驶实例,并分析它们的结构和基本原理。
