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Research on an Intelligent Dispatching Decision Generation Model for Urban Rail Transit Based on an Event Evolution Graph
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Huixi ZONG1, Li BAI1, Kaiming YAN2, Ming ZHANG1, Jianfeng ZHONG2
Urban Rapid Rail Transit | 2026, 39(3) : 1 - 10
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Urban Rapid Rail Transit | 2026, 39(3): 1-10
• Artifical Intelligence •
Research on an Intelligent Dispatching Decision Generation Model for Urban Rail Transit Based on an Event Evolution Graph
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Huixi ZONG1, Li BAI1, Kaiming YAN2, Ming ZHANG1, Jianfeng ZHONG2
Affiliations
  • 1.Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, Beijing 100081
  • 2.Beijing Jingwei Information Technologies Co., Ltd., Beijing 100081
doi: 10.3969/j.issn.1672-6073.2026.03.001
Outline
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To address the issues of high reliance on manual experience, poor dynamic adaptability of emergency decision-making, and low efficiency of decision generation in urban rail transit emergency response, a decision-making model for emergency response is proposed, which integrates a "predicate-centered event representation framework" and a "causal graph-retrieval augmented generation (RAG) integrated architecture". Firstly, a non-static trigger word is adopted to construct a process-oriented knowledge structure for emergency response that is superior to the static knowledge graph. Then, by combining information grouping, situation assessment, and an "event-situation" two-stage query rewriting strategy, the proposed model enhances traditional RAG with a stronger ability to understand the logic of event chains, thereby generating emergency response decisions that are more actionable and better adapted to specific scenarios. Finally, evaluation indicators such as the decision compliance rate, decision generation time, and decision efficiency improvement are defined and applied to comparative experiments to evaluate the model's performance. The research results show that compared with the traditional RAG method, the proposed model improves key-node accuracy by 29.84% in the decision compliance evaluation and achieves a logical-relation correctness rate of 84.74%. Meanwhile, compared with traditional manual decision-making, the proposed model improves generation efficiency by 57.14% indicating that the model can shorten the response time while meeting compliance requirements, providing a reference for constructing an intelligent dispatch decision generation system for urban rail transit.

urban rail transit  /  intelligent dispatching  /  network-assisted decision-making  /  event evolution graph  /  RAG architecture  /  decision generation
宗慧曦, 白丽, 闫锴明, 张铭, 钟建峰. 基于事理图谱的城轨智能调度决策生成模型研究. 都市快轨交通, 2026 , 39 (3) : 1 -10 . DOI: 10.3969/j.issn.1672-6073.2026.03.001
Huixi ZONG, Li BAI, Kaiming YAN, Ming ZHANG, Jianfeng ZHONG. Research on an Intelligent Dispatching Decision Generation Model for Urban Rail Transit Based on an Event Evolution Graph[J]. Urban Rapid Rail Transit, 2026 , 39 (3) : 1 -10 . DOI: 10.3969/j.issn.1672-6073.2026.03.001
Year 2026 volume 39 Issue 3
188
51
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doi: 10.3969/j.issn.1672-6073.2026.03.001
  • Receive Date:2025-08-31
  • Online Date:2026-09-20
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  • Received:2025-08-31
  • Revised:2025-12-21
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    1.Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, Beijing 100081
    2.Beijing Jingwei Information Technologies Co., Ltd., Beijing 100081
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