Onboard Metro Train Localization Based on the Train Motion and Track Geometry Feature Fusion
The metro train localization in an underground environment is a challenging task to provide the reliable and accurate train location for railway operation and maintenance. Nowadays, a user-friendly, high-precision, cost-effective, and convenient train localization method is needed for onboard railway inspection in the underground environment. In this research, we propose a train localization method by train motion and track geometry features fusion. A single microelectromechanical system (MEMS) inertial measurement unit (IMU) mounted onboard an in-service train, is used to sense the train motion, and the measurements are mapped into absolute location along the track by map-matching. Based on that, the train speed estimation is calibrated through train motion and track geometry features fusion to update the train location on the track. Then, the average localization error is estimated to evaluate the train localization performance in the absence of the real train trajectory data. The field experiment is carried out to verify the applicability and assess the train localization accuracy of the proposed method, the results of which show less than 1% average localization errors in an interval. Data from MEMS IMUs mounted on different cars are also tested, showing satisfactory repeatability and reliability performance.
Authors
- Zhilu Lai (ORCID: https://orcid.org/0000-0001-6227-6123)
- Chenglin Chen (ORCID: https://orcid.org/0009-0008-6281-409X)
- Ruohui Zhang (ORCID: https://orcid.org/0000-0002-9429-5481)
- Huiyue Tang (ORCID: https://orcid.org/0000-0002-5587-8676)
- Yong Qin (ORCID: https://orcid.org/0000-0002-6519-8316)
- Yun Bai (ORCID: https://orcid.org/0000-0001-5304-7185)
Institutions
- New Jersey Institute of Technology (US)
- Hong Kong University of Science and Technology (HK)
- Beijing Jiaotong University (CN)
- National Supercomputing Center in Shenzhen (CN)
- Cloud Computing Center (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2025-05-23
- DOI
- https://doi.org/10.1109/tits.2025.3571433
- Citations
- 1
- Primary Topic
- Railway Engineering and Dynamics
- Type
- article
- Field-Weighted Citation Impact
- 0.71
Funders
- National Natural Science Foundation of China
- Guangzhou Municipal Science and Technology Project