计算机应用与软件2018，Vol.35Issue(4)：91-96,6.DOI:10.3969/j.issn.1000-386x.2018.04.017
基于LSTM和Kalman滤波的公交车到站时间预测
BUS ARRIVAL TIME PREDICTION BASED ON LSTM AND KALMAN FILTERING
摘要
Abstract
The construction of intelligent transportation system has become the main problem facing the development of urban transportation.The prediction of bus arrival time is an important part of intelligent transportation system.Bus arrival time data is time-series data with long-term and short-term characteristics,and the bus is susceptible to external factors,so the bus arrival time is also dynamic.Based on the above problems,a bus arrival time prediction model based on LSTM and Kalman filtering was proposed,in which LSTM model was used to predict the basic time series of bus arrival and arrival,and Kalman filtering model was used to dynamically adjust the basic time series.Finally,the adjusted prediction accuracy,mean square deviation and mean absolute deviation were respectively compared with those predicted by LSTM,SVM and SVM + Kalman models.It was proved that the accuracy,mean square deviation and average absolute deviation of LSTM + Kalman model prediction were better than the comparative model.关键词
智能交通/公交车到站时间/LSTM模型/Kalman滤波/时间序列Key words
Intelligent transportation/Bus arrival time/LSTM model/Kalman filtering/Time series分类
信息技术与安全科学引用本文复制引用
范光鹏,孙仁诚,邵峰晶..基于LSTM和Kalman滤波的公交车到站时间预测[J].计算机应用与软件,2018,35(4):91-96,6.基金项目
国家自然科学基金面上项目(41476101). （41476101）
