Empowering high-speed train positioning: Innovative paradigm for generating universal virtual positioning big data

Xiaoyu Zheng; Dewang Chen; Liping Zhuang; Xiaoyu Zheng; Dewang Chen; Liping Zhuang

doi:10.3934/era.2023314

Electronic Research Archive

2023, Volume 31, Issue 10: 6197-6215. doi: 10.3934/era.2023314

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Empowering high-speed train positioning: Innovative paradigm for generating universal virtual positioning big data

1.
School of Transportation, Fujian University of Technology, Fuzhou 350118, China
2.
Intelligent Transportation System Research Center, Fujian University of Technology, Fuzhou 350118, China
3.
School of Electronics, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China

Received: 27 July 2023 Revised: 04 September 2023 Accepted: 06 September 2023 Published: 18 September 2023

High-speed trains (HSTs) positioning is a critical technology that affects the safety and operational efficiency of trains. The unique operating environment of HSTs, coupled with the limitations of real data collection, poses challenges in obtaining large-scale and diverse positioning data. To tackle this problem, we introduce a comprehensive method for generating virtual position data for HSTs. Utilizing virtual simulation technology and expert expertise, this method constructs a HST operating simulation environment on the Unity 3D platform, effectively simulating a range of operating scenarios and complex scenes. Positioning data is collected using virtual sensors, while error characteristics are incorporated to emulate real data collection behavior. The contribution of this paper lies in providing abundant, reliable, controllable and diverse positioning data for HSTs, thereby offering novel insights and data support for the evaluation and optimization of positioning algorithms. This method is not only applicable to various routes and scenarios, but also delivers fresh perspectives on data generation for research in other domains, boasting a broad scope of application.
- intelligent transportation,
- high-speed railway,
- virtual train positioning data,
- big data,
- dataset generation
Citation: Xiaoyu Zheng, Dewang Chen, Liping Zhuang. Empowering high-speed train positioning: Innovative paradigm for generating universal virtual positioning big data[J]. Electronic Research Archive, 2023, 31(10): 6197-6215. doi: 10.3934/era.2023314

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Abstract

High-speed trains (HSTs) positioning is a critical technology that affects the safety and operational efficiency of trains. The unique operating environment of HSTs, coupled with the limitations of real data collection, poses challenges in obtaining large-scale and diverse positioning data. To tackle this problem, we introduce a comprehensive method for generating virtual position data for HSTs. Utilizing virtual simulation technology and expert expertise, this method constructs a HST operating simulation environment on the Unity 3D platform, effectively simulating a range of operating scenarios and complex scenes. Positioning data is collected using virtual sensors, while error characteristics are incorporated to emulate real data collection behavior. The contribution of this paper lies in providing abundant, reliable, controllable and diverse positioning data for HSTs, thereby offering novel insights and data support for the evaluation and optimization of positioning algorithms. This method is not only applicable to various routes and scenarios, but also delivers fresh perspectives on data generation for research in other domains, boasting a broad scope of application.

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