With the rapid development of communication, control and computer technologies in the last several decades, automatic train operation (ATO), for which the driver no longer has to cautiously operate the control handle, is emerging in many urban rail systems to replace traditional manual driving in recent years. As technology advances in railway systems, one theoretically challenging and practically significant problem is how to use the ATO system to make the current railway network more efficient with higher carrying capacity, lower cost and improved quality of service by optimized railway traffic management and train operation. In this review, we focus on this emerging technology of automatic train operation (ATO) for its theoretical development and practical implementations. Specifically, this study first presents the background of ATO technology in railways, which involves the detailed description of its development and implementation in urban metro systems, fundamental features and basic structure of a typical ATO system. Then, we present a comprehensive literature review in this area, in which the current studies are generally classified into three main aspects, i.e., train operation modeling techniques, train trajectory optimization and train speed control methods. Finally, the emerging requirements for current ATO systems and the most promising research directions in this area in the future are discussed explicitly, including (i) the practical implementation of ATO in main line and high-speed railways, (ii) the cooperative train operation methods for energy-saving issues and (iii) the integration of railway traffic control with advanced ATO technology.

Research and development of automatic train operation for railway transportation systems: A survey

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS Editor-In-Chief

Various real-world multiobjective optimization problems are dynamic, requiring evolutionary algorithms (EAs) to be able to rapidly track the moving Pareto front of an optimization problem once an environmental change occurs. To this end, several methods have been developed to predict the new location of the moving Pareto set (PS) so that the population can be reinitialized around the predicted location. In this paper, we present a multidirectional prediction strategy to enhance the performance of EAs in solving a dynamic multiobjective optimization problem (DMOP). To more accurately predict the moving location of the PS, the population is clustered into a number of representative groups by a proposed classification strategy, where the number of clusters is adapted according to the intensity of the environmental change. To examine the performance of the developed algorithm, the proposed prediction strategy is compared with four state-of-the-art prediction methods under the framework of particle swarm optimization as well as five popular EAs for dynamic multiobjective optimization. Our experimental results demonstrate that the proposed algorithm can effectively tackle DMOPs.

Multidirectional Prediction Approach for Dynamic Multiobjective Optimization Problems

Railways have already introduced satellite-based localization systems for non-safety related applications. Driven by economic reasons, the use of these systems for new services and, in particular, their introduction in signaling system is seriously investigated today and tested all around the world. Because of the weight of their history, their strong normative context, and the high requested level of safety, the introduction is relatively slow. The aim of this paper is to provide a survey of past and current programs dealing with global navigation satellite systems as a basis to introduce main issues relative to context, standards, performance requirements, and safety proofs. Links with aeronautical concepts are also presented, illustrating the transposable principles and the limits due to the land transport environment.

/pdf/a-survey-of-gnss-based-research-and-developments-for-the-13xo8zy7pj.pdf

A Survey of GNSS-Based Research and Developments for the European Railway Signaling

This paper presents an analytic optimal control method for the virtually coupled train set (VCTS) in high-speed railway, aiming at maintaining consistent speed and safe spacing among trains in the VCTS. The proposed control strategy focuses on both local and string stability under variant maneuvers in the high-speed scenarios. Specifically, a state-space model is firstly formulated to describe the virtually coupled train dynamics, based on which an optimal control formulation is then constructed considering constraints of safe spacing, operation limits and train dynamic performance. To solve the proposed constrained optimal control problem, an analytical algorithm is given based on Pontryagin’s maximum principle. Further, local and string stability are analyzed, and sufficient conditions of stability are mathematically derived to guarantee stable control for both homogeneous and heterogenous VCTS. Numerical simulations were conducted to verify the correctness of derived sufficient stability conditions and the effectiveness of the proposed control strategy under variant maneuvers and disturbances.

An analytical optimal control approach for virtually coupled high-speed trains with local and string stability

A speed trajectory profile indicating the authorized train speed at each position can be used to guide the driver or the automatic train operation (ATO) system to operate the train more efficiently, which is the most important part of the Chinese Train Control System (CTCS) and will decide the safety and efficiency of train operation. The efforts produced by the train to follow the speed trajectory will directly affect the evaluation of train operation. This paper studies the optimization approach for the speed trajectory of high-speed train in a single section. First, we take the energy consumption as the measure of satisfaction of the railway company, and the trip time is being regarded as the passenger satisfaction criterion; then, we present optimal speed trajectory searching strategies under different track characteristics by dividing the section into some subsections according to different speed limitations. After that, we develop a multiobjective optimization model for the speed trajectory, which is subject to the constraints such as safety requirement, track profiles, passenger comfort, and the dynamic performance. For obtaining the Pareto frontier of train speed trajectory, which has equal satisfaction degree on all the objects, a hybrid evolutionary algorithm is designed and applied to solve the model based on the differential evolution and simulating annealing algorithms. By showing some numerical results of simulations, the efficiency of the proposed model and solution methodology is illustrated.

Multiobjective Optimization for Train Speed Trajectory in CTCS High-Speed Railway With Hybrid Evolutionary Algorithm

The cooperative energy-efficient trajectory planning for multiple high-speed train movements is considered in this paper. We model all the high-speed trains as the agents that can communicate with others and propose a local trajectory planning control model using the Model Predictive Control (MPC) theory. After that we design an online distributed cooperative optimization algorithm for multiple train trajectories planning, under which each train agent can regulate the trajectory planning procedure to save energy using redundancy trip time through tuning ACO’s heuristic information parameter. Compared to the existing literature, the vital distinctions of our work lies not only on the online cooperative trajectory planning but also on the distributed mechanism for multiple high-speed trains. Experimental studies are given to illustrate the effectiveness of the proposed methods with the practical operational data of Wuhan-Guangzhou High-speed Railway in China.

Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements

Trajectory planning plays a crucial role in train operation by providing with the authorized speed at each position. The traditional static train trajectory planning methods are always designed offline according to a preplanned timetable, and they ignored the uncertainties of parameters, resulted by line condition, resistance coefficient, and delay. These uncertain disturbances have not been considered adequately in previous studies. This paper deals with the dynamic optimal train trajectory planning problem with uncertainties. First, in order to identify uncertain resistance coefficients and calculate the dynamic limited speed, we present the optimization framework using onboard equipment such as a global navigation satellite system (GNSS) terminal, a power supply system, and a communication device to sample the real-time traffic information. Then, by taking the energy consumption and punctuality as objectives, we propose a moving horizon train trajectory planning optimization model with an adaptive weight allocation mechanism based on trip time error. The innovation of this paper lies not only in the establishment of a novel dynamic optimization model for train trajectory planning but also the strategy that combines real-time traffic information with the trajectory planning procedure. By contrast with most existing solutions, the proposed approach fully takes advantage of the real-time information and thus avoids the difficulties for modeling the uncertain coefficients for train trajectory planning. The efficiency of the proposed approach is illustrated by showing some numerical results of simulations with the infrastructure data from Beijing-Shanghai High-speed Railway of China.

Moving Horizon Optimization of Dynamic Trajectory Planning for High-Speed Train Operation

With the speed-up of trains, the train's positioning environment becomes increasingly complex which requires the train's positioning system have enough anti-interference capability to ensure the precision and reliability of train's positioning. GNSS/INS deeply integrated positioning system could improve the anti-interference ability of the positioning system, achieving real-time positioning with much higher precision and reliability. In this paper, a deeply integrated GNSS/INS strategy for train locating based on vector tracking loop was proposed, and the mathematical model for GNSS/INS integrated system was established.

Research on deeply integrated GPS/INS for autonomous train positioning

The invention discloses a simulation system of vehicle and road collaborative information interaction and a simulation method of the vehicle and road collaborative information interaction. The system comprises a control desk module and a vehicle and road collaborative information interaction network simulation module, wherein the control desk module comprises an external data acceptance sub-module, an operational tracking network (OPNET) data exchange sub-module and a data information display sub-module. The vehicle and road collaborative information interaction network simulation module achieves information transmission and sharing of vehicle network and road side equipment. The vehicle and road collaborative information interaction network simulation module comprises a data conversion module, a network and protocol configuration module and a simulation result analysis module. The invention further discloses the simulation method of the vehicle and road collaborative information interaction. The simulation system of the vehicle and road collaborative information interaction and the simulation method of the vehicle and road collaborative information interaction are used for optimizing road traffic.

Yan Xihui

Papers

Multiobjective Optimization for Train Speed Trajectory in CTCS High-Speed Railway With Hybrid Evolutionary Algorithm

Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements

Moving Horizon Optimization of Dynamic Trajectory Planning for High-Speed Train Operation

Research on deeply integrated GPS/INS for autonomous train positioning

Simulation system of vehicle and road collaborative information interaction and simulation method thereof