摘要
为了提升城市轨道交通列车运行过程中的准时性和安全性,实现精准停车,需要对列车运行过程中的速度曲线进行跟踪预测。文章首先基于实测数据对列车瞬时功率进行计算,并运用随机森林模型根据功率曲线进行区间分类;然后基于神经网络建立了一种适用于不同区间的城市轨道交通列车速度曲线实时预测方法;最后对所建立的列车速度预测模型进行了测试。根据仿真数据和实际线路数据进行的模型测试结果表明,所提算法能有效实时预测列车的速度曲线,其速度跟踪误差相比传统神经网络模型的降低了57.7%,相比随机森林回归模型的降低了73.9%。
In order to improve the punctuality and safety of urban rail transit trains during operation and achieve accurate parking,it is necessary to track and predict the speed curve during the train operation.This paper firstly calculates the instantaneous power of the train based on the measured data,and then uses the random forest model to classify the interval according to the power curve,and then establishes a real-time prediction method for the speed curve of urban rail transit trains based on neural network for different intervals.The train speed prediction model is tested.The results of model testing on the simulation data and actual line data show that the proposed algorithm can effectively predict the speed curve of the train in real time,improve the accuracy of speed tracking control.The error is reduced by 57.7%compared with the traditional neural network model,and the error is reduced by 73.9%compared with the random forest regression model.
作者
秦建楠
胡文斌
徐立
QIN Jiannan;HU Wenbin;XU Li(Nanjing University of Science and Technology,Nanjing,Jiangsu 210018,China)
出处
《控制与信息技术》
2022年第6期62-68,共7页
CONTROL AND INFORMATION TECHNOLOGY
