基于集成学习模型的城市轨道交通车载数据分析与列车停车误差预测

提出了一种基于Stacking策略的集成学习模型算法。通过基础模型算法评估阶段和基础模型算法集成阶段,成功选出K个基础模型,并基于模型集成策略完成了模型的集成工作,最终得到了基于Stacking策略的集成预测模型。基于实际案例,使用该集成模型对列车停车误差进行预测,并对预测结果进行验证。验证结果显示,基于Stacking策略的集成学习算法模型的训练效率高、预测精度高,与其他传统模型相比具有较强优势。

Urban Rail Transit On-board Data Analysis and Train Stopping Error Prediction Based on Ensemble Learning Model

An ensemble learning model algorithm based on Stacking strategy is proposed.Through the evaluation stage and the integration stage of the basic model algorithms,K basic models are successfully selected,and the integration work of models based on model ensemble strategy is completed to achieve the integrated prediction model based on Stacking strategy.The integrated model is applied to predict the train stopping error of practical cases,and the prediction results are verified.The verification results show that the ensemble learning model algorithm based on Stacking strategy has high training efficiency and excellent prediction accuracy,which is a strong advantage compared to other conventional models.