Automatic Train Operation System Research Articles

Driverless metros are coming, but what about the drivers? A study on AI-related anxiety and safety performance

The application of automatic train operation (ATO) systems in rail transit gradually replaces manual labor, greatly impacting metro drivers’ daily work. Building on the cognitive appraisal theory of stress and the socially embedded model of thriving at work, this study explored how drivers’ perception of ATO systems would affect their safety behavior within the workplace, in other words, the psychological and behavioral costs of drivers’ AI-related anxiety. Specifically, we analyzed whether job insecurity and thriving at work would mediate the association between AI-related anxiety and safety performance. We also examined the potential attenuating effects of Human Resource (HR) system strength on AI-related anxiety’s impact on job insecurity, thriving at work, and safety performance. We gathered 364 sets of paired data from Chinese metro drivers and their direct supervisors based on a time-lagged design. Moderated serial mediation analyses revealed that AI-related anxiety exerted a significant serial indirect impact on safety performance via job insecurity and thriving at work. Additionally, we found conditional serial indirect effects of AI-related anxiety on safety performance as a function of drivers’ perceptions of HR system strength. Theoretical and practical implications are discussed for a scholarly and practical understanding of dealing with AI-related anxiety. Metro organizations were recommended to place more emphasis on the HR process to mitigate the costs of drivers’ AI-related anxiety.

European Common Data Management Platform Definition for Railway AI Function Development

Digitalisation and automation of operations in the railway industry include the use of Automatic Train Operation systems that provide automated functions to reach different levels of automation, known as the Grade of Automation (GoA) levels. These levels go up to GoA4 in which the train is automatically controlled without any staff on board. Artificial intelligence has emerged as technology that can substitute humans in certain driving tasks, in GoA3 (driverless) and GoA4 (unattended) modes. AI capabilities include perception, decision-making, precise positioning, or optimization of communications. The success of AI models depends on the quality and diversity of the data used for training, along with the set-up of a data life-cycle framework that covers creation, training, testing, deployment and monitorisation. The management of training datasets implies both expensive and time-consuming data gathering, labelling, curation and formatting efforts, potentially hindering the development of reliable AI systems. This paper presents a Common Data Management Platform developed by a consortium of European railway stakeholders, devised to efficiently manage data for AI training, and which is demonstrated in two different Proofs of Concept.

A data-driven approach for railway in-train forces monitoring

Railway in-train forces are an essential element in assessing multiple aspects of rolling stocks. Conventional methods for obtaining the forces can be time-consuming and require significant investment in manpower and domain expertise, while only gathering the force data for specific service conditions one at a time. However, automatic train operation (ATO) systems can measure real-time information for trains and tracks by on-board and trackside devices, which could provide an opportunity for in-train forces monitoring. This paper presents a data-driven approach that uses ATO-measured data and a neural network model to monitor in-train forces under service conditions. To develop this approach, longitudinal train dynamics simulations (LTSs) for a freight train were conducted to establish the relationship between ATO measurements and in-train forces on specific couplers, which was embedded in a large amount of training data. After that, a specially developed self-attention-based causal convolutional neural network (SA-CNN) was employed to learn the underlying relationship and estimate the in-train force histories considering temporal dependencies. The comparative evaluation between the SA-CNN against four alternative neural network models revealed that the SA-CNN exhibits a slightly higher level of accuracy. Furthermore, the generalisation capability of the well-trained SA-CNN model was confirmed by numerical LTSs under four different service conditions. The results indicated that the data-driven approach has superior compatibility for arbitrarily combined inputs with significantly reduced computational time compared to LTSs. This approach holds the potential for achieving reliable in-situ monitoring of railway in-train forces, which is beneficial to both in-train force-related research and industrial applications.

An Automatic Train Operation Based Real-Time Rescheduling Model for High-Speed Railway

With the continuous development of the Automatic Train Operation (ATO) system in high-speed railways, automatic driving is progressively supplanting manual operations, ushering in a new era of predictability and reliability for high-speed railway transport. Concurrently, the advent of the ATO system provides a notable impact on real-time rescheduling during disruptions, as it equips dispatchers with precise insights into train operation statuses. This paper is dedicated to a thorough analysis of how the transition to automatic driving in train operations influences the real-time rescheduling model. Based on the distinctive impact of the ATO system on real-time rescheduling, we have proposed a mixed-integer linear programming model that combines train re-timing, reordering, and the minimization of passenger delays. To validate the effectiveness of our model, we present several experiments conducted using data from the Beijing–Shanghai high-speed railway line. The results unequivocally demonstrate that our ATO-based model significantly mitigates train delay time, demonstrating its practical value in optimizing high-speed railway operations.

GSOANR-based multi-objective train trajectory optimization

ABSTRACT Railway transport is an important mode of transportation in China, UK, and worldwide. It is expected to play an even more important role in transportation decarbonization in shifting more passenger trips and freight transportations to railway. It is strategically important to reduce the energy consumption and improve the overall energy efficiency of the railway systems. The introduction of automatic train operation (ATO) systems in electrified railway systems makes it possible to achieve energy consumption reduction while satisfying other railway operation criteria such as the need for passenger comfort, train punctuality, and safety. Although energy saving of railway systems has attracted substantial interest in recent years, ATO systems are not environmentally friendly as expected and there is a lack of a set of energy saving performance measures for railway system planning and operation. In this paper, the features of ATO systems are incorporated into the problem formulation, and a modified GSO algorithm with adaptive neighbourhood range (namely the GSOANR algorithm) is proposed as the solver of the problem. The proposed method is applied to the trajectory planning of a 58-km-long route between Heishan North Station and Fuxin Station of the Beijing-Shenzhen Line in China, and the results confirm that the train operation trajectory generated by the GSOANR algorithm-based optimization method can reduce the electrical energy consumption by about 5.6% compared with the results generated by the standard GSO algorithms, confirming the efficacy of the proposed energy-saving measures and the effectiveness of the novel optimization method, and the referential significance for the study of the train trajectory optimization.

Data-driven integral sliding mode control based on disturbance decoupling technology for electric multiple unit

The controller with high reliability and excellent tracking effect is essential for electric multiple unit (EMU) operation system. Nevertheless, similar to many complex systems, the EMU operation process is multivariable, strong coupled, nonlinear and time-varying. In particular, the coupling relationship between the variables in the system seriously affects the performance of the control system. Disturbance decoupling technology is an effective method to address the above problem, so this paper proposes a multi-input-multi-output (MIMO) data-driven integral sliding mode decoupling control (DISMDC) algorithm for the automatic train operation (ATO) system. Based on dynamic linearization (DL) technology, DISMDC equates the EMU operation process with a full format dynamic linearization (FFDL) data model, and uses FFDL data model to design a discrete integral sliding mode control (SMC) law. Meanwhile, the discrete extended state observer (DESO) is introduced to estimate the unmodeled dynamics, unknown disturbances and coupling between variables of the FFDL data model in real time, which further improves the equivalent description of the system and enhances the decoupling effect and control performance. The DISMDC method is tested on a semiphysical simulation platform of CRH380A EMU equipped in the laboratory, and compared with some traditional model-based and data-driven methods. The simulation results show that the control performance of DISMDC algorithm is better than some traditional methods. The velocity tracking error of each power unit of the EMU is within ±0.15 km/h, the control force and acceleration are within [−51 kN, 42 kN] and [−0.8532 m/s2, 0.7810 m/s2], respectively, and the variations are stable. The decoupling control of EMU under disturbance is realized.

A real-time vital control module to increase capabilities of railway control systems in highly automated train operations

Recent advances in technology and railway have led to the introduction of systems and infrastructures capable of driving trains automatically. The Automatic Train Operation (ATO) system has been optimized for active human supervision. The next challenge is to realize ATO systems capable of achieving unsupervised operations on the mainlines. However, at this aim, additional safety functionalities should be provided. In this paper, we propose a pioneer hardware/software Vital Control Module (VCM) architecture capable of expanding the control capabilities of the existing train control system. The VCM includes a Printed Circuit Board (PCB), to be integrated into the cabin equipment, managed by a reliable and safe hard Real-Time Operating System (RTOS). Both hardware and software are developed to be compliant with related safety standards. The VCM integrates an application logic that acts as an on-board equipment control core, assessing the overall operativity in real-time, and promptly issuing emergency brakes if hazardous situations occur. The application logic has been developed with a model-based approach via Simulink/Stateflow tool and implemented as a C-script on the Xilinx Ultrascale + core housed on the PCB. We have used two testbeds to evaluate the VCM performance. Experimental results showed that the Worst-Case Response Time (WCRT) by the application logic is 13.6 times smaller than the most limiting specification-related deadline. The achieved earliness (− 1.8 ms out of 2 ms of deadline) allows for the easy expansion of VCM’s train protection capabilities in the future. Results from the second testbed showed that the VCM can intervene to mitigate hazardous situations ~ 2 times faster than the current automatic train protection systems according to the related standard.

A Two-Level Fuzzy Multi-Objective Design of ATO Driving Commands for Energy-Efficient Operation of Metropolitan Railway Lines

Policies for reducing CO2 and other GHG emissions have motivated an increase in electrification in metropolitan areas, mandating reductions in energy consumption. Metro systems are keystone contributors to the sustainability of cities; they can reduce the energy consumption of cities through the use of the economic driving parameters in their onboard automatic train operation systems (ATO) and through the strategic design of efficient timetables. This paper proposes a two-level optimization method to design efficient, comfortable, and robust driving commands to be programmed in all the interstations of a metro line. This method aims to increase the sustainability of metro operations by producing efficient timetables with economic driving for each interstation while considering comfort restrictions and train mass uncertainty. First, in the eco-driving level, an optimal Pareto front between every pair of successive stations is obtained using a multi-objective particle swarm optimization algorithm with fuzzy parameters (F-MOPSO). This front contains optimized speed profiles for different running times considering train mass variations. The global problem is stated as a multi-objective combinatorial problem, and a fuzzy greedy randomized adaptive search procedure (F-GRASP) is used to perform an intelligent search for the optimal timetables. Thus, a global front of interstation driving commands is computed for the whole line, showing the minimum energy consumption for different travel times. This method is analyzed in a case study with real data from a Spanish metro line. The results are compared with the minimum running time timetable and a typical timetable design procedure. The proposed algorithms achieve a 24% reduction in energy consumption in comparison to the fastest driving commands timetable, representing a 4% increase in energy savings over the uniform timetable design.

Simulation of Comfort Algorithm for Automatic Driving of Urban Rail Train

Abstract The automation and intelligence of transportation vehicles is the focus and hotspot of the future research and development of the entire transportation industry, which can effectively improve a series of social problems such as traffic accidents, exhaust pollution, and traffic jams caused by the current increase in the number of transportation vehicles. With the rapid development of rail transit in our country, more and more people choose urban rail transit for travel, which is greatly convenient for travel. The comfort of Automatic Train Operation (ATO) system is an inevitable consideration when people choose it. Aiming at the comfort of ATO system, this paper designs a train operation target curve that can meet the comfort index of train. At the same time, two simulation models are established by using the SIMULINK module of MATLAB software to compare the experiments. One model is the train simulation model based on PID control, and the other model is the train simulation model based on fuzzy PID. The final simulation results show that fuzzy PID control has stronger superiority in train comfort in the process of train motion simulation, and the traditional PID control is not as good as fuzzy PID control in train comfort.

Estimating the safety and reliability of the control system of a locomotive with machine vision

Abstract. Aim. The paper aims to evaluate the indicators of safety and reliability of the MALS suite of technology that ensures the control of locomotives. Increased indicators are achieved through the use of additional controls. As such, a second virtual channel is proposed. The latter allows detecting MALS failures without affecting the shunting engine control algorithms.Methods. The paper uses the graph method. Using a modified topological semi-Markov method, formulas were deduced for calculating the mean time to failure and the safety factor.Results. The paper individually examines the mean time of the automatic train operation system of a shunting engine remaining in at least SIL3 and mean time to hazardous failure. The authors research the dependence of the above indicators on the failure rate of the MALS equipment and the machine vision. Using the graph method, the level of the system’s functional safety was evaluated by calculating the safety factor and the danger factor. The dependence of the above factors on the system recovery time and probability of detection of component failures was examined.

A Two-Stage Optimization Algorithm of the Train Traction Energy Consumption in Urban Rail Transit

The optimization of the train traction energy consumption in urban rail transit is a nonlinear optimization problem, and its solution is a very difficult task. This article focuses on the optimization of train traction energy consumption on the line with multiple stations. First, a single-objective nonlinear energy-saving optimization model is established for a given line and total travel time. Second, a two-stage optimization algorithm is proposed to solve this model. In the first stage, for a given interval and travel time, an optimization model of the traction energy consumption is established by using the distance discretization method. A dichotomous iterative algorithm based on energy consumption is proposed to search the optimal traction energy consumption. By calling this algorithm repeatedly, the energy–time curve of each interval can be obtained. In the second stage, according to these energy–time curves, the total travel time of a train on the line is allocated to each interval one by one in the form of time slices. And finally the optimal travel time and optimal traction energy consumption of the train on each interval are obtained. This method does not need to set the train operation mode sequence in advance but adaptively selects the energy-saving operation mode according to the line constraints and the train parameters. The up-direction from Yizhuang to Tongjinan of the Beijing Metro Yizhuang line in China is selected as the test section. The experimental results demonstrate the effectiveness and computational efficiency of our proposed methods and algorithms. Moreover, the dichotomous iterative algorithm runs so fast that it can be used in real-time driver advisory systems or automatic train operation systems.

Common data management platform for artificial sense training and testing for railway applications

The railway sector is evolving towards digitalisation and automation. Automatic Train Operation (ATO) systems provide different degrees of operation autonomy according to the different Grade of Automation (GoA) levels. GoA3 (driverless) and GoA4 (unattended) levels remove drivers from railway operations, what makes necessary the introduction of Artificial Intelligence (AI) for perception tasks. The performance of AI models directly depends on the quality and diversity of the data used for training. Gathering, preparing and labelling these data is extremely costly and time-consuming, what can jeopardise the development of reliable AI-based systems. This paper describes a common data management platform devised by a consortium of stakeholders in the European railway sector to efficiently ingest and share the required data and presents a Proof of Concept (PoC) of it.

Train-Centric Communication Based Autonomous Train Control System

Driverless train control systems are becoming popular in the world. Notably, this technology has already been applied for several years. However, these kinds of Automatic Train Operation systems are still under the framework that network elements and movement authority are generated from the ground control center. Hence, the aforementioned rail systems are “automated” but not “autonomous,” and the architecture utilization and train tracking interval are limited. With the development of advanced sensors and control algorithms, the train intends to be equipped with a decision-making capability. In order to further improve train movement coordination and efficiency, an Autonomous Train Control System (ATCS) is proposed. This paper provides three main contributions: Firstly, the system structure is described, and the information following in ATCS is discussed with general operation scenarios. Secondly, data isolation is broken under ATCS, and data prediction and edge-based information fusion are applied to process the real dynamic data and estimate the train's speed and position. Thirdly, with the assistant of data fusion, model predictive and data-based control methodologies are discussed.

Energy-efficient automatic train operation for high-speed railways: Considering discrete notches and neutral sections

The energy-efficient driving of automatic train operation (ATO) system in high-speed railway systems has attracted more and more attention. Different from the energy-efficient automatic train control problem for metro trains, the traction/braking forces given by high-speed ATO systems are currently not continuous but discrete values corresponding to traction/braking notches due to the limited communication interface between ATO systems and rolling stocks. In addition, since high-speed trains are powered by single-phase alternating current, there is no power supply for train traction in neutral sections, which is introduced to separate two catenaries powered by currents with different phases. In this paper, we propose a discrete-space based mathematical formulation for the energy-efficient automatic train control problem for high-speed trains, where binary variables are introduced to indicate the traction/braking notch selections and to indicate whether a discrete space interval is a neutral section or not. The mathematical formulation is then transformed into a mixed integer linear programming (MILP) model, which can be solved by existing solvers. However, due to the discretization of the space the discrete traction/braking notches, the resulting MILP problem could be infeasible and the optimal driving strategy cannot be obtained. Hence, we present a novel relaxation method, i.e., the final braking notch relaxation method, to relax a few constraints in the final braking phase and to generate a feasible speed profile. Comprehensive experiments are investigated based on Beijing–Harbin high-speed railway line, where the energy consumption of the speed profiles with discrete (i.e., notch-based) traction/braking forces is slightly higher than that with continuous traction/braking forces. Moreover, the impacts of the neutral section locations and the effects of different numbers of traction/braking notches are deeply analyzed.

Toward the Trajectory Predictor for Automatic Train Operation System Using CNN–LSTM Network

The accurate trajectory of the train ahead with more dynamic behaviour, such as train position, speed, acceleration, etc., is the critical issue of virtual coupling for future railways, which can drastically reduce their headways and increase line capacity. This paper presents an integrated convolutional neural network (CNN) and long short-term memory (LSTM) hybrid model for the task of trajectory prediction. A CNN–LSTM hybrid algorithm has been proposed. The model employs CNN and LSTM to extract the spatial dimension feature of the trajectory and the long-term dependencies of train trajectory data, respectively. The proposed CNN–LSTM model has superiority in achieving collaborative data mining on spatiotemporal measurement data to simultaneously learn spatial and temporal features from phasor measurement unit data. Therefore, the high-precision prediction of the train trajectory prediction is achieved based on the sufficient fusion of the above features. We use real automatic train operation (ATO) collected data for experiments and compare the proposed method with recurrent neural networks (RNN), recurrent neural networks (GRU), LSTM, and stateful-LSTM models on the same data sets. Experimental results show that the prediction performance of long-term trajectories is satisfyingly accurate. The root mean square error (RMSE) error can be reduced to less than 0.21 m, and the hit rate achieves 93% when the time horizon increases to 4S, respectively.

<b>Development of Automatic Train Operation System Based on Intermittent Type ATP with Continuous Speed Checks</b>

<b>Development of Automatic Train Operation System Based on Intermittent Type ATP with Continuous Speed Checks</b>

An Experimental Analysis of Hierarchical Rail Traffic and Train Control in a Stochastic Environment

The hierarchical connection of Rail Traffic Management System (TMS) and Automatic Train Operation (ATO) for mainline railways has been proposed for a while; however, few have investigated this hierarchical connection with the real field. This paper studies in detail the benefits and limitations of an integrated framework of TMS and ATO in stochastic and dynamic conditions in terms of punctuality, energy efficiency, and conflict-resolving. A simulation is built by interfacing a rescheduling tool and a stand-alone ATO tool with the realistic traffic simulation environment OpenTrack. The investigation refers to different disturbed traffic scenarios obtained by sampling train entrance delays and dwell times within a typical Monte Carlo scheme. Results obtained for the Dutch railway corridor Utrecht–Den Bosch prove the value of the approach. In case of no disruptions, the implementation of ATO systems is beneficial for maintaining timetables and saving energy costs. In case of delay disruptions, the TMS rescheduling has its full effect only if trains are able to follow TMS rescheduled timetables, while the energy-saving by using ATO can only be achieved with conflict-free schedules. A bidirectional communication between ATO and TMS is therefore beneficial for conflict-resolving and energy-saving.

Intelligent-PID controller design for speed track in automatic train operation system with heuristic algorithms

In this paper, the problem of speed tracing for automatic train operation is studied. A new Intelligent-PID controller is proposed in which four optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Imperium Colony Algorithm (ICA) for the best parameter tuning with the integration of a novel switching function are used. The algorithms are analyzed and specialized for different driving modes including: acceleration, cruising, braking and speed profile shift. By the use of a switch, the PID controller is tuned according to the best algorithm. The switching action is done through a slight change from the current position to the best values by transient values determined by the other algorithm outputs. The simulation results indicate the excellence of the proposed method. The performance of the suggested structure is compared with a single-mode optimization algorithm without use of the switch. The results of the comparison show that the proposed method can track the trajectory on all driving modes with very high accuracy.

Multi objective ant Colony Optimisation to obtain efficient metro speed profiles

Obtaining efficient speed profiles for metro trains is a multi-objective optimisation problem where energy consumption and travel time must be balanced. Automatic Train Operation (ATO) systems may handle a great number of possible speed profiles; hence optimisation algorithms are required to find efficient ones in a timely manner. This paper aims to assess the performance of a particular meta-heuristic optimisation algorithm, a variation of the traditional Ant Colony (ACO) modified to deal with multi-objective problems with continuous variables: MOACOr. This algorithm is used to obtain efficient speed profiles in up to 32 interstation sections in the metro network of Valencia (Spain), and the convergence and diversity of these solution sets is evaluated through metrics such as Inverse Generational Distance (GD) and Normalised Hypervolume (NH). The results are then compared to those obtained with a conventional genetic algorithm (NSGA-II), including a statistical analysis to identify significant differences. It has been found that MOACOr shows a better performance than NSGA-II in terms of convergence, regularity and diversity of the solution. These results indicate that MOACOr is a good alternative to the widely used genetic algorithm and could be a better tool for rail operation managers trying to improve energy efficiency.

Measures and Methods for the Evaluation of ATO Algorithms

There is increasing interest in automating train operations of mainline services, e.g., to increase network capacity. Automatic train operation (ATO) is already achieved by several pilot projects, but is still not implemented on a large scale. Functional, interoperability and performance tests are necessary before ATO can be introduced generally. Virtual preliminary analysis will contribute to the validation process to ensure a safe and successful implementation. This paper aims to present an approach that applies to the performance testing of ATO systems. Therefore, methods and test standards for technologies enabling automatic operation in other transport sectors are reviewed. The main findings have been adapted, transformed and combined to be used as a general strategy for virtual performance testing in the railway sector. Specifically, universal performance indicators commonly used in the railway sector, namely punctuality, accuracy, energy consumption, safety and comfort, are presented. They are refined by adding sub-indicators specific to the performance evaluation of ATO algorithms. A layer model for scenario description is adapted from the automotive sector, as well as the definition of different scenario types. Lastly, factors that can influence the performance of an ATO algorithm are identified. For demonstration purposes, a simple case study is conducted. Thereby we exemplarily show-cased the approach for ATO performance testing using a microscopic train simulator in combination with an ATO algorithm.