This paper addresses the challenge of achieving high-precision headway estimation in virtual coupling trains by proposing a method utilising a high-order extended Kalman filter-based smoother. In this approach, the leading train uses a high-order extended Kalman filter to obtain its current operational state and then transmits historical state data to the following train. The following train then employs a high-order extended Kalman smoother to refine the state estimation and determine dynamic headway estimation. The high-order extended Kalman filter, based on Taylor series expansion, enhances state estimation accuracy by minimising truncation errors. It constructs a pseudo-linear representation of the full-space hidden variables and establishes high-order states, facilitating the modelling of measurements to align with the filtering derivation process. The high-order extended Kalman smoother continuously optimises current-state estimation using future measurement sequences, with the derivation process realised through the orthogonal theorem and innovation analysis. Ultimately, the headway estimation is updated based on the smoothed state provided by the smoother, and the effectiveness of the proposed method is validated through a multi-mode operating process of virtual coupling trains.

Fault diagnosis of railway assets has drawn the interest of both the scholarly and engineering communities. Federated learning (FL) enables training models across distributed assets to preserve data privacy and reduce high data transfer costs, which has been applied in fault diagnosis. However, the imperfect data transmission problem due to communication errors easily results in low accuracy of FL-based fault diagnosis in the railway system. To solve the problem, a sequential and asynchronous federated learning framework is proposed for fault diagnosis of railway point machines (RPMs) in this work. First, a dual-branch network is proposed as the global model in asynchronous FL for reducing parameters, while maintaining high accuracy. Second, a time cycle mechanism based on sequential Kalman filtering is proposed for reducing the negative impact of data communication errors. Finally, experimental results demonstrates that the proposed method enhances the applicability of online RPM fault diagnosis training in real deployment scenarios.

With the integration of 5G NR (New Radio) into railway systems, the demand for enhanced positioning and trajectory tracking performance in High-Speed Train (HST) networks has grown. However, many existing train positioning schemes rely on traditional algorithms like the Extended Kalman Filter (EKF), which may fall short of meeting the precision requirements, particularly in 5G HST scenarios. Addressing this limitation, this paper draws on the concepts presented by Ko et al. (2022) and introduces an Improved Extended Kalman Filter (IEKF) using the Least Squares of Undermeasurement (LSU) technique, specifically tailored for nonlinear systems. The IEKF, expanding step by step, theoretically captures statistical properties of the Knorr set for any order prediction error, providing richer information on higher-order terms compared to the traditional EKF. Additionally, for a more intuitive comparison of the IEKF unfolded to different orders, a novel performance indicator is introduced. In conclusion, to validate the effectiveness of our proposed algorithm in real-world scenarios, we demonstrate its superior localization performance by comparing Mean Squared Error (MSE) and Mean Absolute Error (MAE) with traditional nonlinear localization algorithms. The comparisons are based on simulation examples involving train localization tracking and an industrial device ablation system.

Recently, Ko et al. (2022) proposed a high-speed railway positioning scheme based on an improved Kalman filter using 5G NR signals. Although the proposal was promising, our research and analysis have revealed that the method has serious design flaws in the proposed filtering principles, rendering the algorithm infeasible. Specifically, the flaws are related to the computation and usability of high-order terms in the prediction error after Taylor expansion and prediction error derivation.

Automatic inspections of overhead contact lines (OCLs) are developed to implement anomaly detection during normal operation. It is an essential prerequisite for efficient maintenance of railway electrification system. This paper presents a comprehensive survey on the inspections of OCLs with an emphasis on computer vision technology, which has developed rapidly due to its ability to understand images. Our survey begins with a brief introduction on anomalies in OCL inspections and generic procedures of computer vision inspection for anomaly detection. Subsequently, for detecting deviations of parameters and defective components during OCL inspections, the existing techniques involving stereo vision and object vision, especially convolution neural networks are described in detail from two aspects: measurement of OCL parameters and identification of OCL conditions. Some interference factors in OCL inspection are analyzed. Actual cases of the inspections are also briefly shown. Challenges and suggestions for further research on OCL inspection are drawn toward the end of the paper.

Now more than ever, monitoring railway track geometry from in-service vehicles is an attractive proposition that ensures improved infrastructure performance without interrupting railway operations. Communicating the collected sensors-data to a central server has always been an issue due to the current GSM-R and LTE data-rate and spectrum limitations. The prospect of opportunistic access to an inefficiently utilised frequency spectrum, known as TV White Spaces (TVWS), is proposed to solve the spectrum scarcity problem that exploits desirable railway propagation characteristics. In order to provide full protection for the spectrum primary users, IEEE 802.22 standard sets strict policies on the mobile platforms. This research proposes a novel handover scheme that utilises a greedy algorithm to select the operational frequency channels. The scheme takes into account; the train's trajectory, including the possibility of train delays, and coexistence issues between the spectrum's secondary users. A case study of two trains reporting their collected maintenance data to 3 Access Points (APs) while travelling between Selly Oak and New Street Station, Birmingham, UK is presented. For high channels availability (≥40%), an average of 30 megabytes of extra track data can be transmitted using the new approach for an 8 min journey. In addition, a single channel can be used in the new approach for an average consecutive distance of 1.05 km compared with an average of 0.58 km for IEEE 802.22 standard. Both systems provide identical interference performance with more transmission power that can reach up to 42.2 dBm allowed under the new scheme.

Inspection of physical surface defects is a significant concern in many industrial areas. In railway systems, this process mainly includes the detection and classification of defects in rails and wheels, for which laser‐based optical inspection technologies have gradually been applied in the form of 2D profile measurement, benefiting from its high precision and robustness to surface conditions. However, defect classification and evaluation after the initial detection works still rely heavily on human inspectors to make maintenance suggestions. The linear nature of rails makes it possible to increase the dimension of rail measurement data from 2D to 3D by aligning 2D profiles along the rail, from which more comprehensive diagnosis information becomes available. In combination with appropriate artificial intelligence algorithms, this approach can potentially replace human‐dominated defect classification and evaluation work. This study presents a 3D model‐based railway track surface defect classification and evaluation method. A set of geometrical features are extracted from the 3D model of track surface defects to describe a distinguishable pattern for each category of defect. Multi‐class classifiers are then tested and have shown promising results on a group of artificial track surface defects, giving a systemic solution for 3D model‐based automatic track surface defect inspection.

With the increasing concerns on energy consumption and operating cost in metro systems, energy saving on train operation attracts significant attentions. Previous studies have mainly focused on optimal control of a single train and energy-efficient train timetabling. The former does not consider the synchronization of motoring and braking trains, which cannot ensure the proper utilization of regenerative energy on the metro lines without energy storage systems. The latter includes scheduling train operations to synchronize motoring and braking trains for the better utilization of regenerative energy. However, the overlapping time of motoring and braking trains is usually as short as a few seconds and the energy reduction might be made impossible by train delays, which are common in practice. This paper presents a model framework, on the extents of motoring/braking of train acceleration and station stopping, as well as the locations of switching train operation modes, for real-time cooperative control of multiple metro trains. The objective is to minimize the net energy consumption with the consideration of utilizing regenerative energy. A cooperative co-evolutionary algorithm is developed to attain the solution of the proposed model. Case studies on a real-life metro line demonstrate the energy saving performance of the proposed approach compared with separate train control and timetable optimization, from no disturbance to a good range of delays. The results also indicate that the partial motoring in train acceleration and partial braking in station stopping achieve better net energy reduction, in comparison with full motoring/braking preferred in previous studies.

As demand for rail transportation continues to increase rapidly, significant challenges have emerged in many railway systems in terms of Capacity, Safety and Customer Satisfaction. To deal with these challenges, intelligent technologies, such as artificial intelligence, big data, and machine learning, have been gradually introduced to rail transportation. It is therefore timely and appropriate to have a focused investigation and discussion about Intelligent Rail Transportation. This special issue provides a forum for scientists and engineers working in academia, industry, and government to present their latest research findings and engineering experiences in developing and applying intelligent technologies to improve railway’s autonomy, cooperation, and integration.

For metro systems in over-crowded conditions, when an unexpected disturbance occurs, the operation of trains might be disturbed due to the high frequency and density of the metro traffic. A large number of passengers might be stranded on platforms due to service gaps and the limited free capacity of trains. In this paper, by introducing binary variables as selection indicators for ATO profiles which were preset in on-board ATO systems by metro signal suppliers, we develop a mixed integer programming (MIP) model for a metro train timetable rescheduling problem in order to jointly optimize the total train delay, the number of stranded passengers, and the energy consumption of trains. We formulate the total energy consumption as the difference between the tractive energy consumption and the regenerated energy by considering the mass of in-vehicle passengers. Then, we adopt commercial optimization software CPLEX to solve the proposed model, which can obtain tradeoff solutions in a short time. Finally, three numerical experiments based on real-world operational data are carried out to verify the effectiveness of the proposed method.

This paper presents the development of SmartDrive package to achieve the application of energy-efficient driving strategy. The results are from collaboration between Ricardo Rail and the Birmingham Centre for Railway Research and Education (BCRRE). Advanced tram and train trajectory optimization techniques developed by BCRRE as part of the UKTRAM More Energy Efficiency Tram project have been now incorporated in Ricardo's SmartDrive product offering. The train trajectory optimization method, associated driver training and awareness package (SmartDrive) has been developed for use on tram, metro, and some heavy rail systems. A simulator was designed that can simulate the movement of railway vehicles and calculate the detailed power system energy consumption with different train trajectories when implemented on a typical AC or DC powered route. The energy evaluation results from the simulator will provide several potential energy-saving solutions for the existing route. An enhanced Brute Force algorithm was developed to achieve the optimization quickly and efficiently. Analysis of the results showed that by implementing an optimal speed trajectory, the energy usage in the network can be significantly reduced. A driver practical training system and the optimized lineside driving control signage, based on the optimized trajectory were developed for testing. This system instructed drivers to maximize coasting in segregated sections of the network and to match optimal speed limits in busier street sections. The field trials and real daily operations in the Edinburgh Tram Line, U.K., have shown that energy savings of 10%-20% are achievable.

Communication-based train control (CBTC) systems have been playing a progressively significant role in metro signaling in recent years. As safety-critical systems, CBTC systems have very strict requirements on the wireless communication performance between train and wayside access points (AP), which is highly dependent on the deployment of the APs. In this paper, by customizing and adopting a decomposition-based multiobjective evolutionary algorithm, the proposed AP deployment optimization method has been implemented, verified, and its implementation accuracy has been assessed. A real-world case study is carried out in an integrated simulation platform, in which the optimized AP deployments are verified and show better performance than the original planning.

In railway condition monitoring, feature classification is a very critical step, and the extracted features are used to classify the types and levels of the faults. To achieve better accuracy and efficiency in the classification, the extracted features must be properly selected. In this paper, maximal information coefficient is employed in two different stages to establish a new feature selection method. By using this proposed two-stage feature selection method, strong features with low redundancy are reserved as the optimal feature subset, which results in the classification process having a more moderate computational cost and good overall performance. To evaluate this proposed two-stage selection method and prove its advantages over others, a case study focusing on the rolling bearing is carried out. The result shows that the proposed selection method can achieve a satisfactory overall classification performance with low-computational cost.

Train positioning is of critical importance for communication-based train control (CBTC) systems. The objective of this paper is to provide an algorithm to generate the precise estimates of the train position and velocity for CBTC systems with carrier-sense multiple access (CSMA) protocol scheduling, thereby improving the accuracy of train positioning as well as the availability of CBTC systems. First, the dynamics of a train with N cars linked by couplers is described based on Newton's motion equations. Then, the transmission model reflecting the behaviors of p-persistent CSMA protocol is presented by using a Bernoulli distributed sequence whose probability distribution is dependent on the number of trains sharing with one communication channel [i.e., N(k)]. Furthermore, the value of N(k) is assumed to be unknown but bounded by two known positive integers. The purpose of the problem addressed is to design an estimator, such that the estimation error is exponentially ultimately bounded (with a certain asymptotic upper bound) in mean square subject to the external resistive force. By utilizing the stochastic analysis approach, sufficient conditions are established to guarantee the ultimate boundedness of the estimation error in mean square. For the purpose of designing the desired estimator gains under different requirements (e.g., smallest ultimate bound and fastest decay rate), two optimization problems are solved in terms of linear matrix inequalities. Finally, a simulation example is given to illustrate the effectiveness of the estimator design scheme.

Communication-based train control (CBTC) systems are a new generation of metro signaling system dependent on wireless technology with appropriate access point (AP) deployment. Improved AP deployment can improve the reliability of wireless CBTC systems. This paper proposes a method for optimizing the AP deployment in the data communication system of CBTC. To validate the optimal AP deployment, an integrated simulation environment is used to test the performance of the optimized AP deployments.

This paper describes on-going research developing a system to harvest and utilise open and crowdsourced data related to the UK railway systems. This system will allow the controllers and decision makers to listen to the messages posted on social networks by passengers or other members of the public and relate these messages to specific (physical) trains that are referred to in those messages, by fusing information from other open sources. This will enable the railway controllers to take prompt actions in case of any emergency or simply to improve the quality of customer service.

This paper presents the railway core ontologies, a group of related ontologies designed to model the rail domain in detail. The purpose of these ontologies is to enable improved data integration in the rail domain, which will deliver business benefits in the form of improved customer perceptions and more efficient use of the rail network. The modularity of the ontologies allows for both detailed modelling of the domain at a high level and the storing of instance data at lower levels. It concludes that the benefits of improved rail data integration are best realised through the use of the railway core ontologies.

There usually exists a high density of traffic through bottleneck sections of mainline railways, where a perturbation of one single train could result in long consequential delays across a number of trains. In the event of disturbances, rescheduling trains approaching the bottleneck will be necessary to increase the throughput of the section. To model the real-time train rescheduling problems around bottleneck sections, a mixed-integer programming model is presented in this paper. An innovative improved algorithm (DE_JRM) is developed to solve the problem. The model and the algorithms are validated with a case study using Monte Carlo methodology, which demonstrates that the proposed algorithm can reduce the weighted average delay and satisfy the requirements of real-time traffic control applications.

Increasing attention is being paid to energy efficiency in subway systems to reduce operational cost and carbon emissions. Optimization of the driving strategy and efficient utilization of regenerative energy are two effective methods to reduce the energy consumption for electric subway systems. Based on a common scenario that an accelerating train can reuse the regenerative energy from a braking train on the opposite track, this paper proposes a cooperative train control model to minimize the practical energy consumption, i.e., the difference between traction energy and the reused regenerative energy. First, we design a numerical algorithm to calculate the optimal driving strategy with the given trip time, in which the variable traction force, braking force, speed limits, and gradients are considered. Then, a cooperative train control model is formulated to adjust the departure time of the accelerating train for reducing the practical energy consumption during the trip by efficiently using the regenerative energy of the braking train. Furthermore, a bisection method is presented to solve the optimal departure time for an accelerating train. Finally, the optimal driving strategy is obtained for the accelerating train with the optimal departure time. Case studies based on the Yizhuang Line, Beijing Subway, China, are presented to illustrate the effectiveness of the proposed approach on energy saving.

Railway accidents, such as collisions, conflicts, and derailments, are still happening, despite the implementation of advanced railway control systems. One of the reasons for this is the architecture employed in current systems and existing operating processes, which allow human errors to occur. This paper processes a design concept and architecture for the next generation of train control systems (NGTCS). Some key technologies, such as parallel monitoring, system-level “fail-safe”, data sharing and fusion, common-mode cause error avoidance, and the illegal or incorrect operation of alarms by railway workers, are considered. This paper also details the principle and method of parallel monitoring for some key operations such as train tracking interval, interlocking, and train speed limit protection. The NGTCS is a highly intelligent monitoring system that represents system theory, system safety, deeper integration, and data fusion between subsystems and parallel monitoring on critical subjects.

In railway operations, if the journey of a preceding train is disturbed, the service interval between it and the following trains may fall below the minimum line headway distance. If this occurs, train interactions will happen, which will result in extra energy usage, knock-on delays, and penalties for the operators. This paper describes a train trajectory (driving speed curve) optimization study to consider the tradeoff between reductions in train energy usage against increases in delay penalty in a delay situation with a fixed block signaling system. The interactions between trains are considered by recalculating the behavior of the second and subsequent trains based on the performance of all trains in the network, apart from the leading train. A multitrain simulator was developed specifically for the study. Three searching methods, namely, enhanced brute force, ant colony optimization, and genetic algorithm, are implemented in order to find the optimal results quickly and efficiently. The result shows that, by using optimal train trajectories and driving styles, interactions between trains can be reduced, thereby improving performance and reducing the energy required. This also has the effect of improving safety and passenger comfort.

This paper presents early findings from a larger study, into the use of linked data in the rail domain. The study and other literature has shown there to be benefits from improved integration of data in this domain and proposes that linked data in general and ontology in particular will address this. The paper will set out the current state of data integration in the British rail domain, highlighting issues found there. The manner in which linked data is employed in the broader transport domain will then be examined along with previous work pertaining to the rail domain.

Regenerative braking improves the energy efficiency of railway transportation by converting kinetic energy into electric energy. This paper proposes a method to apply the Bellman-Ford (BF) algorithm to search for the train braking speed trajectory to increase the total regenerative braking energy (RBE) in a blended braking mode with both electric and mechanical braking forces available. The BF algorithm is applied in a discretized train-state model. A typical suburban train has been modeled and studied under real engineering scenarios involving changing gradients, journey time, and speed limits. It is found that the searched braking speed trajectory is able to achieve a significant increase in the RBE, in comparison with the constant-braking-rate (CBR) method with only a minor difference in the total braking time. An RBE increment rate of 17.23% has been achieved. Verification of the proposed method using BF has been performed in a simplified scenario with zero gradient and without considering the constraints of braking time and speed limits. Linear programming (LP) is applied to search for a train trajectory with the maximum RBE and achieves solutions that can be used to verify the proposed method using BF. It is found that it is possible to achieve a near-optimal solution using BF and the solution can be further improved with a more complex search space. The proposed method takes advantage of robustness and simplicity of modeling in a complex engineering scenario, in which a number of nonlinear constraints are involved.

An energy-efficient train trajectory describing the motion of a single train can be used as an input to a driver guidance system or to an automatic train control system. The solution for the best trajectory is subject to certain operational, geographic, and physical constraints. There are two types of strategies commonly applied to obtain the energy-efficient trajectory. One is to allow the train to coast, thus using its available time margin to save energy. The other one is to control the speed dynamically while maintaining the required journey time. This paper proposes a distance-based train trajectory searching model, upon which three optimization algorithms are applied to search for the optimum train speed trajectory. Instead of searching for a detailed complicated control input for the train traction system, this model tries to obtain the speed level at each preset position along the journey. Three commonly adopted algorithms are extensively studied in a comparative style. It is found that the ant colony optimization (ACO) algorithm obtains better balance between stability and the quality of the results, in comparison with the genetic algorithm (GA). For offline applications, the additional computational effort required by dynamic programming (DP) is outweighed by the quality of the solution. It is recommended that multiple algorithms should be used to identify the optimum single-train trajectory and to improve the robustness of searched results.

An innovative topology-based method for modeling railway train control systems is proposed in this paper. The method addresses the problems of having to rely too much on designers' experience and of incurring excessive cost of validation and verification in the development of railway train control systems. Four topics are discussed in the paper: 1) the definition of basic topological units for modeling railway networks, based on the essential characteristics of these units; 2) the concept of a train movement authority topological space; 3) the interpretation of the train control logic as a topological space construct; and 4) topological space theorems for train control system verification. A case study is also presented, where the approach was applied in the simulation model of a typical railway network, and the results show good performance, which meets the system requirements.