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Research and Practice on the Evaluation of Artificial Intelligence Large Models in Urban Rail Transit
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Fucai HUA1, Xin SHAO1, Xiujiang XIA1, Xiaoyi HAN1, Wenjun WANG1, Songwei YU1, Yue JIANG2
Urban Rapid Rail Transit | 2026, 39(3) : 11 - 18
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Urban Rapid Rail Transit | 2026, 39(3): 11-18
• Artifical Intelligence •
Research and Practice on the Evaluation of Artificial Intelligence Large Models in Urban Rail Transit
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Fucai HUA1, Xin SHAO1, Xiujiang XIA1, Xiaoyi HAN1, Wenjun WANG1, Songwei YU1, Yue JIANG2
Affiliations
  • 1.Beijing Urban Construction Design & Development Group Co., Ltd., Beijing 100037
  • 2.Ningbo Rail Transit Group Co., Ltd., Ningbo 315101
doi: 10.3969/j.issn.1672-6073.2026.03.002
Outline
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With the rapid development of large AI models, the demand for their application in urban rail transit is increasing rapidly. However, there is still a lack of evaluation standards and methods for large models specific to the urban rail transit industry. To address this, this paper establishes an evaluation system for large models tailored to the urban rail transit industry. This evaluation system covers the construction of evaluation datasets, the design of evaluation dimensions, the selection of evaluation methods, and the formulation of evaluation metrics. Through this system, the performance of urban rail transit large models for urban rail transit in tasks such as language understanding, knowledge reasoning, image-text alignment, and spatiotemporal consistency can be evaluated comprehensively and objectively. Meanwhile, a dedicated evaluation system for large models for urban rail transit is designed and developed, and the effectiveness of the evaluation system is verified through practical application. The verification results show that the established evaluation system can provide an evaluation basis for the optimization and application of large models in urban rail transit, and promote their implementation and application in the urban rail transit industry.

urban rail transit  /  artificial intelligence  /  large models for urban rail transit  /  model evaluation  /  evaluation system
华福才, 邵昕, 夏秀江, 韩晓艺, 王文俊, 于松伟, 蒋玥. 城轨领域人工智能大模型评测研究与实践. 都市快轨交通, 2026 , 39 (3) : 11 -18 . DOI: 10.3969/j.issn.1672-6073.2026.03.002
Fucai HUA, Xin SHAO, Xiujiang XIA, Xiaoyi HAN, Wenjun WANG, Songwei YU, Yue JIANG. Research and Practice on the Evaluation of Artificial Intelligence Large Models in Urban Rail Transit[J]. Urban Rapid Rail Transit, 2026 , 39 (3) : 11 -18 . DOI: 10.3969/j.issn.1672-6073.2026.03.002
Year 2026 volume 39 Issue 3
133
30
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doi: 10.3969/j.issn.1672-6073.2026.03.002
  • Receive Date:2025-08-15
  • Online Date:2026-09-20
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  • Received:2025-08-15
  • Revised:2025-12-09
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    1.Beijing Urban Construction Design & Development Group Co., Ltd., Beijing 100037
    2.Ningbo Rail Transit Group Co., Ltd., Ningbo 315101
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表12种不同金属材料的力学参数
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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