铁道运输与经济2025，Vol.47Issue(4)：128-140,13.DOI:10.16668/j.cnki.issn.1003-1421.2025.04.14
基于贝叶斯网络的多制式轨道交通列车运行冲突检测研究
Train Operation Conflict Detection in Multi-Standard Rail Transit Based on Bayesian Networks
摘要
Abstract
To improve the decision-making level of railway dispatching,train operation conflict(TOC)detection was studied based on domain knowledge and data-driven technologies through prediction and inference.The conflict evolution process was analyzed from the level of single train,adjacent trains,and multiple trains.Furthermore,by considering the scenario of multi-standard rail transit trains running on the same line,the basic model structure of Bayesian Networks(BNs)for TOC detection was proposed according to the conflict determination rules.Based on the train operation data,the structure and parameters of the model were optimized by structure learning and parameter learning.Evaluation indicators such as accuracy,precision,recall,and F1 score were used to assess the detection model.The results show that the average detection accuracy for TOC on each line is 81%;the average recall is 86%,and the average F1 score is 83%.A comparison with commonly used TOC detection models shows that the proposed BN model demonstrates higher accuracy and lower misclassification rates in TOC detection.The main advantage of the model is that the causal relationship between the variables can be explained through the BN structure.In addition,determining the location of the conflict by the train delays predicted by the model enhances the performance of the model,which lays a decision-making basis for TOC mitigation.关键词
铁路运输/列车运行冲突检测/贝叶斯网络/多制式轨道交通/领域知识/数据驱动Key words
Railway Transportation/Train Operation Conflict Detection/Bayesian Network/Multi-Standard Rail Transit/Domain Knowledge/Data Drive分类
交通工程引用本文复制引用
钟兴莉,黄平,彭其渊,温傈文..基于贝叶斯网络的多制式轨道交通列车运行冲突检测研究[J].铁道运输与经济,2025,47(4):128-140,13.基金项目
国家重点研发计划项目(2022YFB4300502) （2022YFB4300502）
国家自然科学基金项目(72301221) （72301221）
