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Nowadays the railway industry is beginning to give serious consideration to using intelligent traffic management systems (TMSs) in order to improve railway performance regarding train and passenger delays and robust use of capacity. The TMS is responsible for handling railway traffic once a disturbance happens. A fundamental input parameter of a TM...
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Citations
... Currently, train positioning primarily depends on trackside equipment, which no longer meets the demands of the new generation of train control systems [1]. Advancements in high-speed rail signaling technology have driven the development of multi-sensor combination positioning methods, addressing the limitations of single-sensor systems and opening new possibilities for achieving high-precision train positioning [2,3]. ...
To address the issue of reduced positioning accuracy caused by satellite signal interruptions when trains pass through long tunnels, a novel train positioning method based on an innovative adaptive unscented Kalman filter (UKF) under kinematic constraints is proposed. This method aims to improve the accuracy of the location of trains during operation. By considering the dynamic characteristics of the train, a dynamic kinematic-constrained inertial navigation system (INS)/odometer (ODO) combination positioning system is established. This system utilizes kinematic constraints to correct the accumulated errors of the INS. Additionally, the algorithm incorporates real-time estimation of the measurement noise covariance using innovation sequences. The updated adaptive estimation algorithm is applied within the UKF framework for nonlinear filtering, forming the innovative adaptive UKF algorithm. At each time step, the difference between the ODO sensor data and the INS output is used as the measurement input for the innovative adaptive UKF algorithm, enabling global estimation. This process ultimately yields the actual positioning result for the train. Simulation results demonstrate that the innovative adaptive UKF train positioning method, incorporating kinematic constraints, effectively mitigates the impact of satellite signal interruptions. Compared with the traditional INS/ODO positioning method, the innovative adaptive UKF method reduces position errors by 34.35% and speed errors by 36.33%. Overall, this method enhances navigation accuracy, minimizes train positioning errors, and meets the requirements of modern train positioning systems.
... Determination of train positions within the railway network must be fail-safe and of high accuracy. Inaccurate train position data can lead to delays in operation [1]. In systems operating today, train position is determined using trackside equipment such as axle counters and balises. ...
Train localization during Global Navigation Satellite Systems (GNSS) outages presents challenges for ensuring failsafe and accurate positioning in railway networks. This paper proposes a minimalist approach exploiting track geometry and Inertial Measurement Unit (IMU) sensor data. By integrating a discrete track map as a Look-Up Table (LUT) into a Particle Filter (PF) based solution, accurate train positioning is achieved with only an IMU sensor and track map data. The approach is tested on an open railway positioning data set, showing that accurate positioning (absolute errors below 10 m) can be maintained during GNSS outages up to 30 s in the given data. We simulate outages on different track segments and show that accurate positioning is reached during track curves and curvy railway lines. The approach can be used as a redundant complement to established positioning solutions to increase the position estimate's reliability and robustness.
... Within a pre-established timetable where capacity utilisation approaches the maximum, stochastic phenomena related to delays or emergencies may cause the delays to propagate to an increasing number of trains. Therefore, the train traffic control process is based on the dynamic resolution of traffic conflicts and train departure handling to minimise the total delay time across the network [21][22][23]. ...
Railway line capacity is growing in importance as a criterion for the assessment of railroad infrastructure performance. This problem is becoming more and more relevant as the demand for rail transport increases, especially considering the transport policy related to the promotion of climate-neutral means of transport. Insufficient capacity affects the stability and reliability of railway traffic operations. The analytical methods used for capacity estimation are typically insufficient to solve problems of a multicriteria nature (i.e. problems which take traffic heterogeneity and human factors into account). Optimisation methods, on the other hand, usually yield the best results if the current timetable for a given line is known. Additionally, they do not strongly consider the impact of the running characteristics of a specific train type and of the system or several systems in operation on the line (e.g. national Class B system and ERTMS/ETCS system). Therefore, this paper proposes a model and a simulation program developed in the Matlab and Simulink environment to be used to simulate on-route train movement, to study railroad capacity with different control systems, as well as for predictive train control to minimise energy losses. The authors described the assumptions adopted for the simulation program and the input parameters configurable against a specific line segment. They also discussed selected results derived from simulations of controlling the departure of trains to a railway line with the purpose of energy loss reduction.
... Positioning systems are generally divided into trackside and on-board equipment, aiming to obtain different results [1,2]. Trackside equipment is usually aimed at train integrity, wheelset monitoring and evaluating vehicles as they pass by the inspection points [3][4][5]. ...
In railways, using a track- and ride-quality monitoring system on in-service train has become desirable for coordination and security. Identification of the track- or train-related rough rides via train crew can be estimated to the nearest kilometre. However, if the train is equipped with a monitoring system a better location and track quality evaluation can be provided. These systems commonly use information such as GNSS and/or an odometer to provide location information. This work proposes a practical method for track alignment estimation using real data from an in-cab inertial measurement system and using also a novel method based on crosslevel variations. The speed estimation is done through speed-related harmonics detected on inertial sensors, which depend on speed and track characteristics; and distance correction is provided by comparing crosslevel derived from inertial sensors and a reference track geometry. The effectiveness and accuracy of the method is demonstrated with data collected between London and Ashford.
... As the high-speed railway network becomes denser and the number of trains running on the network increases, the safety of high-speed trains has become an issue of great concern [1,2]. There is a requirement for real-time, continuous, accurate, and reliable navigation information for the operation control system to control, schedule, and monitor train movements [1,3,4]. ...
... The curved line of a high-speed railway track is generally composed of a straight line, a gentle curve, and a circular curve [22], as shown in Figure 1. There is an installation lever arm between the rotational center of the train body and the MIMU, and the lever arm speed exists at the position of the MIMU during turning [2,23]. ...
... The curved line of a high-speed railway track is generally composed of a straight line, a gentle curve, and a circular curve [22], as shown in Figure 1. There is an installation lever arm between the rotational center of the train body and the MIMU, and the lever arm speed exists at the position of the MIMU during turning [2,23]. The train turns under the cooperation of the forward traction, track, bogie, and wheels [24]. ...
When a high-speed train is running in a tunnel, the global navigation satellite system (GNSS) signal is completely lost. Relying only on the inertial navigation system (INS) composed of Micro-electromechanical Systems (MEMS) devices leads to large navigation errors. To solve this problem, an integrated micro inertial measurement unit (MIMU), odometer (ODO), and motion constraint (MC) tunnel navigation method is proposed. This method first establishes a motion constraint model based on the installation angles of MIMU; secondly, the effect of turning on the motion constraint model and the odometer is analyzed and the use condition of the motion constraints is obtained; the installation angles of MIMU are then estimated when GNSS signal is good and the use condition of the motion constraints is met; finally, the forward speed measured by the odometer and the motion constraints are applied to suppress the error of the INS and improve the navigation accuracy in the tunnel. Based on this method, high-speed train navigation tests were carried out both in areal tunnel environment and in a case study with an artificially disconnected GNSS signal. The experimental results showed that the navigation accuracy of the train in the tunnel was significantly improved. Seamless navigation was achieved inside and outside the tunnel, which verified the effectiveness of the method.
