A flexible and robust train operation model based on expert knowledge and online adjustment

2017 ◽  
Vol 15 (03) ◽  
pp. 1750023 ◽  
Author(s):  
Chun-Yang Zhang ◽  
Dewang Chen ◽  
Jiateng Yin ◽  
Long Chen
Keyword(s):  
Energy Consumption ◽  
Expert Knowledge ◽  
Operation Mode ◽  
Velocity Curve ◽  
Target Velocity ◽  
Optimization Approach ◽  
Operation Model ◽  
Train Control ◽  
Train Operation ◽  
Automatic Train Operation

Most existing automatic train operation (ATO) models are based on different train control algorithms and aim to closely track the target velocity curve optimized offline. This kind of model easily leads to some problems, such as frequent changes of the control outputs, inflexibility of real-time adjustments, reduced riding comfort and increased energy consumption. A new data-driven train operation (DTO) model is proposed in this paper to conduct the train control by employing expert knowledge learned from experienced drivers, online optimization approach based on gradient descent, and a heuristic parking method. Rather than directly to model the target velocity curve, the DTO model alternatively uses the online and offline operation data to infer the basic control output according to the domain expert knowledge. The online adjustment is performed over the basic output to achieve stability. The proposed train operation model is evaluated in a simulation platform using the field data collected in YiZhuang Line of Beijing Subway. Compared with the curve tracking approaches, the proposed DTO model achieves significant improvements in energy consumption and riding comfort. Furthermore, the DTO model has more advantages including the flexibility of the timetable adjustments and the less operation mode conversions, that are beneficial to the service life of train operation systems. The DTO model also shows velocity trajectories and operation mode conversions similar to the one of experienced drivers, while achieving less energy consumption and smaller parking error. The robustness of the proposed algorithm is verified through numerical simulations with different system parameters, complicated velocity restrictions, diverse running times and steep gradients.

scholarly journals An Eco-Driving Algorithm for Interoperable Automatic Train Operation

2020 ◽  
Vol 10 (21) ◽  
pp. 7705
Author(s):  
Adrián Fernández-Rodríguez ◽  
Asunción P. Cucala ◽  
Antonio Fernández-Cardador
Keyword(s):  
Energy Consumption ◽  
Differential Evolution ◽  
Minimum Energy ◽  
Differential Evolution Algorithm ◽  
Multiple Time ◽  
Intermediate Target ◽  
Target Time ◽  
Traffic Regulation ◽  
Train Operation ◽  
Automatic Train Operation

The new Automatic Train Operation (ATO) system over the standard European Rail Traffic Management System (ERTMS) will specify the requirements that an automatic train driving system must fulfil in order to be interoperable. The driving is defined by target times located along the journey that are received from the trackside system. Then, the on-board equipment drives the train with the objective of meeting all of the target times. The use of eco-driving methods to calculate the train driving is necessary, as one of the main goals of modern train driving systems is to increase the energy efficiency. This paper presents a simulation-based optimisation algorithm to solve the eco-driving problem constrained by multiple target times. This problem aims to minimize the energy consumption subject to a commercial running time, as the classical eco-driving problem, and also to meet intermediate target times during the journey between stations to enable automatic traffic regulation, especially at junctions. The algorithm proposed combines a Differential Evolution procedure to generate possible solutions with a detailed train simulation model to evaluate them. The use of this algorithm makes possible to find accurate speed profiles that meet the requirements of multiple time objectives. The proposed Differential Evolution algorithm is capable of finding the feasible speed profile with the minimum energy consumption, obtaining a 7.7% of energy variation in the case of a journey with one intermediate target time and 3.1% in the case of two intermediate targets.

Vehicle Test for Verifying Energy-Saving Train Control Based on Automatic Train Operation System

2017 ◽  
Vol 137 (12) ◽  
pp. 924-933 ◽  
Author(s):  
Shoichiro Watanabe ◽  
Yasuhiro Sato ◽  
Takafumi Koseki ◽  
Takeshi Mizuma ◽  
Ryuji Tanaka ◽  
...  
Keyword(s):  
Energy Saving ◽  
Operation System ◽  
Train Control ◽  
Train Operation ◽  
Vehicle Test ◽  
Automatic Train Operation

scholarly journals RAILTOPOMODEL AND RAILML 3 IN OVERALL CONTEXT

2017 ◽  
Vol 11 ◽  
pp. 16 ◽  
Author(s):  
Adam Hlubuček
Keyword(s):  
Current Form ◽  
Train Control ◽  
Exchange Format ◽  
Train Operation ◽  
Automatic Train Operation ◽  
Insight Into

This paper aims to provide a brief insight into the UIC RailTopomodel and railML initiative. Concerning the railML exchange format, mainly the current form of forthcoming third edition, involving especially the infrastructure schema, based on the RailTopoModel modelling principles, is taken into account. When rewarding, the comparison between railML 3 and the previous railML 2.3 version is given. At the end, the author focuses on selected issues of possible applicability of these tools for the needs of such systems as the automatic train control, automatic train operation, prediction of train etc. If essential, some extensions of the structures are proposed.

Energy-Efficient Train Operation Strategy with Speed Profiles Selection for an Urban Metro Line

2019 ◽  
Vol 2673 (4) ◽  
pp. 348-360 ◽  
Author(s):  
Pengli Mo ◽  
Lixing Yang ◽  
Ziyou Gao
Keyword(s):  
Energy Consumption ◽  
Programming Model ◽  
Computational Time ◽  
Speed Profile ◽  
Operational Environment ◽  
Passenger Demand ◽  
Proposed Model ◽  
Train Operation ◽  
Automatic Train Operation ◽  
Metro System

In the daily operations of a metro system, physical trains track the speed profiles which are embedded in the automatic train operation (ATO) system, and the speed profile plays a key role in determining both energy consumption and travel time. With consideration of the real-world operational environment and uncertain levels of passenger demand, this study specifically proposes an integer programming model with respect to energy consumption to generate a robust operational strategy which determines the speed profile choice on each segment. Because of the computational complexity of the proposed model, a heuristic algorithm is designed, which combines a genetic algorithm (GA) and a nomadic algorithm (NA), to find a good solution in acceptable computational time. Finally, numerical experiments based on the Beijing Yizhuang metro line are implemented to verify the effectiveness and efficiency of this approach.

scholarly journals An Online Energy-Saving Driving Strategy for Metro Train Operation Based on the Model Predictive Control of Switched-Mode Dynamical Systems

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4933 ◽  
Author(s):  
Fei Shang ◽  
Jingyuan Zhan ◽  
Yangzhou Chen
Keyword(s):  
Energy Consumption ◽  
Model Predictive Control ◽  
Energy Saving ◽  
Predictive Control ◽  
Control Algorithm ◽  
Optimization Problem ◽  
Online Optimization ◽  
Mode Switching ◽  
Train Control ◽  
Train Operation

With the rapid development of urban rail transit systems and the consequent sharp increase of energy consumption, the energy-saving train operation problem has been attracting much attention. Extensive studies have been devoted to optimal control of a single metro train in an inter-station run to minimize the energy consumption. However, most of the existing work focuses on offline optimization of the energy-saving driving strategy, which still needs to be tracked in real train operation. In order to attain better performance in the presence of disturbances, this paper studies the online optimization problem of the energy-saving driving strategy for a single metro train, by employing the model predictive control (MPC) approach. Firstly, a switched-mode dynamical system model is introduced to describe the dynamics of a metro train. Based on this model, an MPC-based online optimization problem is formulated for obtaining the optimal mode switching times with minimal energy consumption for a single train in an inter-station run. Then we propose an algorithm to solve the constrained optimization problem at each time step by utilizing the exterior point penalty function method. The proposed online optimal train control algorithm which determines the mode switching times can not only improve the computational efficiency but also enhances the robustness to disturbances in real scenarios. Finally, the effectiveness and advantages of this online optimal train control algorithm are illustrated through case studies of a single train in an inter-station run.

Automatic train operation in the CBTC system

2020 ◽  
Vol 131 ◽  
pp. 99-118
Author(s):  
Aleksandra Modrzejewska
Keyword(s):  
Control Systems ◽  
Traffic Management ◽  
Rail Transport ◽  
Train Control ◽  
Railway Traffic ◽  
Train Operation ◽  
Alternative Means ◽  
Fully Automatic ◽  
Transport Problems ◽  
Automatic Train Operation

Rail transport adapts to the requirements of the modern agglomerations and provides solutions that ensure greater speed and capacity, while being environmentally friendly. Alternative means of rail transport as well as train control systems are proposed. The basis of innovative, effective, attractive and, above all, safe railway is a traffic automation, which can be implemented to a varying range and degree. Automation of systems of the train control and railway traffic management is an area that is constantly being scientifically researched and developed. The most technologically advanced control systems, in which the human factor is eliminated, are CBTC systems. This article presents the characteristics and components of one of the CBTC class family solutions used in the world, i.e. Bombardier’s product - CITYFLO 650. On the example of the CITYFLO 650 solution, the analysis of the fully automatic train operation was performed. Each of the stages of the fully automatic train operation was included in this paper. The conducted analysis confirms the legitimacy of using the CBTC technology on very demanding city lines. Bidirectional train-to-wayside data communications and determination of train location to a high degree of precision make it that CBTC systems fulfill the criteria set by large, fast-growing cities and a growing population. Poland, as a country currently looking for solutions that would reduce the transport problems of large cities, is considering such innovative proposals for rail traffic.

Intelligent train control for cooperative train formation: A deep reinforcement learning approach

2021 ◽  
pp. 095965182110647
Author(s):  
Danyang Zhang ◽  
Junhui Zhao ◽  
Yang Zhang ◽  
Qingmiao Zhang
Keyword(s):  
Formation Control ◽  
Learning Algorithm ◽  
Operation Mode ◽  
Control Process ◽  
Q Learning ◽  
Train Control ◽  
System A ◽  
Train Operation ◽  
Metro System

Considering the intelligent train control problem in long-term evolution for metro system, a new train-to-train communication-based train control system is proposed, where the cooperative train formation technology is introduced for realizing a more flexible train operation mode. To break the limitation of centralized train control, a pre-exploration-based two-stage deep Q-learning algorithm is adopted in the cooperative train formation, which is one of the first intelligent approaches for urban railway formation control. In addition, a comfort-considered algorithm is given, where optimization measures are taken for providing superior passenger experience. The simulation results illustrate that the optimized algorithm has a smoother jerk curve during the train control process, and the passenger comfort can be improved. Furthermore, the proposed algorithm can effectively accomplish the train control task in the multi-train tracking scenarios, and meet the control requirements of the cooperative formation system.

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