Hybridized-GNSS Approaches to Train Positioning: Challenges and Open Issues on Uncertainty

RegioRail—GNSS Train-Positioning System for Automatic Indications of Crisis Traffic Situations on Regional Rail Lines

The identification of the position of rail vehicles plays a crucial role in the control of rail traffic. Available, up-to-date information on the position of vehicles allows us to efficiently deal with selected traffic situations where the position of vehicles is very important. The main objective of this article is to introduce (i) a concept of a solution for identification of the current position of rail vehicles based on the worldwide-recognized system of the GNSS with the use of an original railway network data model, and (ii) the use of this concept as supplementary support for the dispatcher control of rail traffic on regional lines. The solution was based on an original, multilayer rail network data model supporting (i) the identification of rail vehicle position and (ii) novel algorithms evaluating the mutual positions of several trains while detecting the selected crisis situation. In addition, original algorithms that enable automatic network model-building (on the database server level) directly from the official railway infrastructure database were developed. The verification of the proposed solutions (using rail traffic simulations) was focused on the evaluation of (i) the changing mutual positions (distances) of trains on the railway network, (ii) the detection of nonstandard or crisis traffic situations, and (iii) the results of the calculations of necessary braking distances of trains for stopping and collision avoidance. The above verification demonstrated the good applicability of the proposed solutions for the potential deployment within supplementary software support for real traffic control. The described concept of the supplementary support determined for railway traffic control (using the localization of trains by means of the GNSS) is intended mainly for regional, single-rail lines. This type of line is very often not sufficiently equipped with standard signaling and interlocking equipment to ensure the necessary traffic safety. Therefore, when deploying this support, the new algorithms for the automatic detection of critical traffic situations represent a significant potential contribution to increasing operational safety.

Development of a Novel Railway Positioning System Using RFID Technology

Currently, a number of positioning systems are in use to locate trains on the railway network; but these generally have limited precision. Thus, this paper focuses on testing and validating the suitability of radio frequency identification (RFID) technology, for aligning vehicles to switch and crossing (S&C) positions on the railway network. This offers the possibility of accurately knowing the position of vehicles equipped with monitoring equipment, such as the network rail track recording vehicle (TRV), and aligning the data with reference to the locations of the S&C (and ideally to key elements within a particular S&C). The concept is to install two tags, one on the switch-toe sleeper and the second on the crossing-nose sleeper, with an RFID reader that will be installed underneath the vehicle. Thus, the key features of the S&C, the switch toe and crossing nose, will be considered as a definitive reference point for the inspection vehicle’s position. As a monitoring vehicle passes over a piece of S&C, the proposed positioning system will provide information about this S&C’s ID, which is stored inside the RFID tags and will indicate the S&C’s GPS coordinates. As part of the research in this paper, more than 400 tests have been performed to investigate two different RFID technologies, passive and semi-passive, tested in a variety of conditions: including different passage speeds, different distances between the RFID reader and the tags, and varied strength signal transmitted between the reader and the tags. Based on lab testing and analysis of the recorded data, it is concluded that passive RFID technology is the most suitable of the two technologies. The conclusions find that the proposed RFID-based solution can offer a more precise positioning solution to be a reference point for the train location within the network.

Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion

This article presents the research and results of field tests and simulations regarding an autonomous/robotic railway vehicle, designed to collect multiple information on safety and functional parameters of a surface railway and/or subway section, based on data fusion and machine learning. The maintenance of complex railways, or subway networks with long operating times is a difficult process and intensive resources consuming. The proposed solution delivers human operators in the fault management service and operations from the time-consuming task of railway inspection and measurements, by integrating several sensors and collecting most relevant information on railway, associated automation equipment and infrastructure on a single intelligent platform. The robotic cart integrates autonomy, remote sensing, artificial intelligence, and ability to detect even infrastructural anomalies. Moreover, via a future process of complex statistical filtering of data, it is foreseen that the solution might be configured to offer second-order information about infrastructure changes, such as land sliding, water flooding, or similar modifications. Results of simulations and field tests show the ability of the platform to integrate several fault management operations in a single process, useful in increasing railway capacity and resilience.

Verification of GNSS Measurements of the Railway Track Using Standard Techniques for Determining Coordinates

The problem of the reproduction of the railway geometric layout in the global spatial system is currently solved in the form of measurements that use geodetic railway networks and also, in recent years, efficient methods of mobile positioning (mainly satellite and inert). The team of authors from the Gdańsk University of Technology and the Maritime University in Gdynia as part of the research project InnoSatTrack is looking for effective and efficient methods for the inventory of railway lines. The research is part of a wider investigation BRIK (Research and Development in Railway Infrastructure, in polish: Badania i Rozwój w Infrastrukturze Kolejowej). This paper presents a comparative analysis of the problem of the reproduction of the trajectory of the measuring system using tacheometry, satellite measurements made using a measurement trolley, and mobile satellite measurements. Algorithms enabling the assessment of the compliance of satellite measurements with classic tacheometric measurements were presented. To this end, the authors held measurement sessions using modern geodetic instruments and satellite navigation on a section of the railway line. The results of the measurements indicate the convergence of the level of accuracy achieved by different measuring techniques.

Digital Filtering of Railway Track Coordinates in Mobile Multi-Receiver GNSS Measurements

The article discusses an important issue in connection with the technique of mobile Global Navigation Satellite System (GNSS) measurements of railway track coordinates, which is digital filtering performed to precisely determine railway track axes. For this purpose, a measuring technique is proposed which bases on the use of a measuring platform with a number of appropriately distributed GNSS receivers, where two of them determine the directional base vector of the platform. The receivers used in the research had high measuring frequency in the Real Time Kinematic (RTK) operating mode and enabled correction of the obtained results in post–processing. A key problem discussed in the article is the method for assessing the quality of the measurement results obtained from GNSS receivers, and their preparation for further processing making use of geometrically constrained parameters of the base vector and specialized digital filtering, among other elements, to precisely determining the track axis. The obtained results confirm the applicability of the used method of GNSS signal processing.