Zero on-site testing on GNSS for train control towards digital railways

Abstract

The integration of Global Navigation Satellite System (GNSS) technology into railway train control systems is a crucial step toward achieving the vision of a digital railway. Traditional train control systems undergo extensive in-house tests and prolonged field tests for certification and approval before operational deployment, leading to high costs, delays, and operational disruptions. This paper introduces a GNSS-based train control localization framework which eliminates the need for on-site testing by leveraging train movement dynamics and 3D environment modeling to create a zero on-site testing platform. The proposed framework simulates train movement and the surrounding 3D environment using collected railway line location data and environmental attributes to generate realistic multipath signals and obscuration effects. This approach enables comprehensive laboratory-based case studies for train localization, reducing the huge amount test of needed for physical field trials. The framework is established in house, using the data collected at the Test Base of China Academy of Railway Sciences (Circular Railway). Results from the open area and cutting environment tests demonstrate high localization accuracy repeatability within the simulated environment, validating the feasibility and effectiveness of zero on-site testing for GNSS-based train control systems. This research highlights the potential of GNSS simulation platforms in enhancing cost efficiency, operational safety, and accuracy for future digital railways.

Keywords

GNSS testing / Railway train localization / 3D environment modeling / Digital track map / Zero on-site testing

CRediT authorship contribution statement

Debiao Lu: Writing - review & editing, Writing - original draft, Data curation, Conceptualization. Wenzheng Qiao: Writing - original draft, Formal analysis, Data curation. Baigen Cai: Resources, Project administration, Methodology, Funding acquisition. Jian Wang: Validation, Software, Resources. Jiang Liu: Resources, Investigation, Formal analysis. Yuchen Zhao: Data curation. Chunjie Qiao: Methodology, Investigation. Dirk Spiegel: Methodology.

Data availability

Data will be made available on request.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research is supported by the National Natural Science Foundation of China (62027809, U2268206, T2222015, U2468202).

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