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. (C) 2016 Elsevier Ltd. All rights reserved.

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.

With the aim to improve the fault‐tolerant performance of GNSS based deeply integrated train positio‐ning system ,a FRBFNN based fault detection algorithm was proposed referring to the basic principles of fuzzy system and neural network .The FRBFNN based fault detection model for the integrated train positioning sys‐tem was established ,while the corresponding training algorithm was designed based on the thought of immuni‐ty differential evolution .With respect to the issues of fault isolation of integrated train positioning and system reconfiguration ,the rules for state transition of integrated system structure were defined ,and the fault‐tolerant control process was also designed aiming at increasing the fault tolerant control performance of the integrated train positioning system .The effectiveness and practicality of the proposed model and solution were verified by computer simulation ,using the real operation data of a train in a section of the Qinghai‐Tibet railway .

To solve the performance deterioration problem of traditional loosely integrated GPS/INS under the complex positioning environment,a deeply integrated GPS/INS solution for automatic train positioning based on vector tracking was designed and proposed,and the realization flow of the automatic train positioning algo-rithm was given,while the mathematic model of the signal tracking filter was also established.In view of the non-linear problem of the train positioning system,the thought of strong tracking filtering was introduced to improve the conventional cubature Kalman filtering algorithm and up-grade its stability and robustness.The ef-fectiveness and practicality of the proposed solution were verified by computer simulation,using the train’s op-eration data in a section of the Qinghai-Tibet Railway.

数据融合技术是对多个传感器提供的信息按某种最优融合准则进行融合,能提高数据的精确度.总结数据融合在RUNE、ITCS及GLLS等国内外典型列控系统研究中的应用情况,根据上述几个典型系统研究的测试结果,分析数据融合在列车定位中应用的有效性和优越性;展望数据融合在列车定位中的应用.

数据融合技术是对多个传感器提供的信息按某种最优融合准则进行融合,能够提高数据的精确度.在列车定位系统中应用数据融合技术,可以有效提高列车定位精度,保障列车安全.阐述数据融合的功能原理、关键技术,对数据融合关键技术广泛应用的几种非线性滤波算法特性和测试精度进行比较,分析保证列车组合定位系统安全完整性的方法.

The multi-objective operation optimization model for high-speed trains was established subject to the constraints of traffic safety,velocity limitation,running time and dynamic vehicle performance and with the energy consumption,travel time,accurate stopping and comfort level as the optimization indexes.In order to solve the local optimum problem of the traditional differential evolution algorithm,the improved crossover operation method was designed and the idea of simulated annealing was introduced into enhance the ability to search for the optimal solution.The optimal algorithm for multi-object high-speed train operation optimization was proposed on the basis of the modified differential evolution algorithm.Taking data measured in a section of the Beijing-Shanghai High-speed Railway Line as basic data,the effectiveness and practicality of the proposed algorithm were verified by computer simulation.

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.

Train locating information is very important for train control system, how to achieve automatic identification of train track occupancy using GNSS simply in some railway sections without track circuits has been a crucial problem. Hausdorff distance can be used to measure the mismatch between two sets, which is widely used in object matching. This paper presents a novel algorithm for automatic identification of train track occupancy based on LTS-Hausdorff distance and D-S evidence theory. The LTS-Hausdorff distance reference template of railway track was established, the calculation process of LTS-Hausdorff distance and the identify strategy of train track occupancy based on D-S evidence theory were studied. Test results show that the algorithm is efficient and can achieve automatic track identification in low cost.

In order to resolve the automatic identification problems of train track occupancy at turnouts and on parallel sections,a new automatic identification algorithm was proposed based on LTS-Hausdorff distance and D-S evidence theory.The reference template of track LTS-Hausdorff distance was established,the calculation process of LTS-Hausdorff distance and the decision method of automatic identification were analyzed,and the effects of train speed and search threshold on the algorithm were studied.Test result shows that when there are 10 track points,the results of the new algorithm and the maximum likelihood track identification decision are same.The higher train speed is,the less track points are,and the algorithm is still effective.The smaller search threshold is,the shorter the algorithm realizing time is.So the algorithm is valid.4 tabs,5 figs.14 refs.

Based on multi-resolution modeling and High Level Architecture (HLA),CTCS-3 train control system simulation was studied. According to the structure of CTCS-3,four key modules including Radio Block Center (RBC),train control center (TCC),the balise information transmission module and the on-board vital computer were chosen to constitute the train control model. Based on the difference of the information transmission level,using multi-resolution modeling method,the different modules of train control model were divided into high,middle and low resolution module. The HLA simulation technology was used to construct the property publication and the ordering relationship of train control model federation object module and federation member,and the train control simulation process was realized with RTI software. The simulation realized the following simulation scenes,which included the connection and disconnection of the train control model and RTI software,the movement authorization generation realized by RBC and TCC in different resolution,the on-board vital computer computed object distance curve and train running view display,etc. The simulation results have validated the feasibility of the multi-resolution modeling method in CTCS-3 simulation.