Automatic Driving Operation Strategy of Urban Rail Train Based on Improved DQN Algorithm

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.

Energy Saving for Automatic Train Control in Moving Block Signaling System

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.

Braking distance prediction for vehicle consist in low-speed on-sight operation:a Monte Carlo approach

The first and last mile of a railway journey, in both freight and transit applications, constitutes a high effort and is either non-productive(e.g. in the case of depot operations) or highly inefficient(e.g. in industrial railways). These parts are typically managed on-sight, i.e. with no signalling and train protection systems ensuring the freedom of movement. This is possible due to the rather short braking distances of individual vehicles and shunting consists. The present article analyses the braking behaviour of such shunting units. For this purpose, a dedicated model is developed. It is calibrated on published results of brake tests and validated against a high-definition model for lowspeed applications. Based on this model, multiple simulations are executed to obtain a Monte Carlo simulation of the resulting braking distances. Based on the distribution properties and established safety levels, the risk of exceeding certain braking distances is evaluated and maximum braking distances are derived. Together with certain parameters of the system, these can serve in the design and safety assessment of driver assistance systems and automation of these processes.

A fully automatic and high-accuracy surface water mapping framework on Google Earth Engine using Landsat time-series

Efficient and continuous monitoring of surface water is essential for water resource management.Much effort has been devoted to the task of water mapping based on remote sensing images.However,few studies have fully considered the diverse spectral properties of water for the collection of reference samples in an automatic manner.Moreover,water area statistics are sensitive to the satellite image observation quality.This study aims to develop a fully automatic surface water mapping framework based on Google Earth Engine(GEE)with a supervised random forest classifier.A robust scheme was built to automatically construct training samples by merging the information from multi-source water occurrence products.The samples for permanent and seasonal water were mapped and collected separately,so that the supplement of seasonal samples can increase the spectral diversity of the sample space.To reduce the uncertainty of the derived water occurrences,temporal correction was applied to repair the classification maps with invalid observations.Extensive experiments showed that the proposed method can generate reliable samples and produce good-quality water mapping results.Comparative tests indicated that the proposed method produced water maps with a higher quality than the index-based detection methods,as well as the GSWD and GLAD datasets.

Automated Burned Scar Mapping Using Sentinel-2 Imagery

The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper proposes a fully automated methodology for mapping burn scars using Sentinel-2 data. Information extracted from a pair of Sentinel-2 images, one pre-fire and one post-fire, is jointly used to automatically label a set of training patterns via two empirical rules. An initial pixel-based classification is derived using this training set by means of a Support Vector Machine (SVM) classifier. The latter is subsequently smoothed following a multiple spectral-spatial classification (MSSC) approach, which increases the mapping accuracy and thematic consistency of the final burned area delineation. The proposed methodology was tested on six recent wildfire events in Greece, selected to cover representative cases of the Greek ecosystems and to present challenges in burned area mapping. The lowest classification accuracy achieved was 92%, whereas Matthews correlation coefficient (MCC) was greater or equal to 0.85.

Research on Optimization of Freight Train ATO Based on Elite Competition Multi-Objective Particle Swarm Optimization

In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics model of the freight train operation process is established based on the safety and the freight train dynamics model in the process of its operation. The algorithm of combining elite competition strategy with multi-objective particle swarm optimization technology is introduced, and the winning particles are obtained through the competition between two elite particles to guide the update of other particles, so as to balance the convergence and distribution of multi-objective particle swarm optimization. The performance comparison experimental results verify the superiority of the proposed algorithm. The simulation experiments of the actual line verify the feasibility of the model and the effectiveness of the proposed algorithm.

How to Deal with Revolutions in Train Control Systems

Train control systems ensure the safety of railways. This paper begins with a summary of the typical train control systems in Japan and Europe. Based on this summary, the author then raises the following question regarding current train control systems： What approach should be adopted in order to enhance the functionality, safety, and reliability of train control systems and assist in commercial operations on railways？ Next, the author provides a desirable architecture that is likely to assist with the development of new train control systems based on current information and communication technologies. A new unified train control system （UTCS） is proposed that is effective in enhancing the robustness and com- petitiveness of a train control system. The ultimate architecture of the UTCS will be only composed of essential elements such as point machines and level crossing control devices in the field. Finally, a pro- cessing method of the UTCS is discussed.

Model-free adaptive robust control method for high-speed trains

Aiming at the robustness issue in high-speed trains(HSTs)operation control,this article proposes a model-free adaptive control(MFAC)scheme to suppress disturbance.Firstly,the dynamic linearization data model of train system under the action of measurement disturbance is given,and the Kalman filter(KF)based on this model is derived under the minimum variance estimation criterion.Then,according to the KF,an anti-interference MFAC scheme is designed.This scheme only needs the input and output data of the controlled system to realize the MFAC of the train under strong disturbance.Finally,the simulation experiment of CRH380A HSTs is carried out and compared with the traditional MFAC and the MFAC with attenuation factor.The proposed control algorithm can effectively suppress the measurement disturbance,and obtain smaller tracking error and larger signal to noise ratio with better applicability.

Automatic Train Operation Based on Adaptive Terminal Sliding Mode Control

This paper presents an adaptive terminal sliding mode control(ATSMC) method for automatic train operation. The criterion for the design is keeping high-precision tracking with relatively less adjustment of the control input. The ATSMC structure is designed by considering the nonlinear characteristics of the dynamic model and the parametric uncertainties of the train operation in real time. A nonsingular terminal sliding mode control is employed to make the system quickly reach a stable state within a finite time, which makes the control input less adjust to guarantee the riding comfort. An adaptive mechanism is used to estimate controller parameters to get rid of the need of the prior knowledge about the bounds of system uncertainty. Simulations are presented to demonstrate the effectiveness of the proposed controller, which has robust performance to deal with the external disturbance and system parametric uncertainties. Thereby, the system guarantees the train operation to be accurate and comfortable.

Research on Multi-Objective Real-Time Optimization of Automatic Train Operation(ATO) in Urban Rail Transit

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.

公共轨道交通PIS系统改造的设计

随着社会经济发展和城市建设的不断加快,地铁作为快捷、便利、高效的城市交通工具得到了飞快的发展,乘客信息系统(PIS)作为地铁核心的列车运营信息发布系统,可以有效地对乘客进行疏导、引流。地铁PIS系统的建设,不但可以有效地提高地铁服务质量,而且可以确保整个系统安全、高效运营。同时,随着地铁建设时间的不断延长,既有地铁中也存在大量对原有系统的升级改造。在地铁PIS系统整体改造中,原系统不能做任何改动,该系统只对外提供一个数据接口给原PIS(LCD)系统,新增加LED显示屏系统没有数据接口。针对这个问题,改造中采用数据转发服务的方式解决了新PIS系统(LED)没有数据源的问题,使新的PIS系统(LED)得以顺利应用,为今后地铁既有PIS系统的升级和改造提供了全新的思路。