南京航空航天大学学报（英文版）2023，Vol.40Issue(5)：595-606,12.DOI:10.16356/j.1005-1120.2023.05.008
基于多元时序和模式挖掘的终端区交通流预测
Traffic Flow Prediction Model Based on Multivariate Time Series and Pattern Mining in Terminal Area
摘要
Abstract
To improve the accuracy of traffic flow prediction under different weather scenarios in the terminal area,a terminal area traffic flow prediction model fusing multivariate time series and pattern mining(MTSPM)is proposed.Firstly,a multivariate time series-based traffic flow prediction model for terminal areas is presented where the traffic demand,weather,and strategy of terminal areas are fused to optimize the traffic flow prediction by a deep learning model CNN-GRUA,here CNN is the convolutional neural network and GRUA denotes the gated recurrent unit(GRU)model with attention mechanism.Secondly,a time series bag-of-pattern(BOP)representation based on trend segmentation symbolization,TSSBOP,is designed for univariate time series prediction model to mine the intrinsic patterns in the traffic flow series through trend-based segmentation,symbolization,and pattern representation.Finally,the final traffic flow prediction values are obtained by weighted fusion based on the prediction accuracy on the validation set of the two models.The comparison experiments on the historical data set of the Guangzhou terminal area show that the proposed time series representation TSSBOP can effectively mine the patterns in the original time series,and the proposed traffic flow prediction model MTSPM can significantly enhance the performance of traffic flow prediction under different weather scenarios in the terminal area.关键词
交通流预测/多元时间序列/时间序列表示/模式挖掘/深度学习Key words
traffic flow prediction/multivariate time series/time series representation/pattern mining/deep learning分类
信息技术与安全科学引用本文复制引用
祝玮琦,陈海燕,刘莉,袁立罡,田文..基于多元时序和模式挖掘的终端区交通流预测[J].南京航空航天大学学报（英文版）,2023,40(5):595-606,12.基金项目
This work was supported by the Na-tional Key R&D Program of China(Nos.2022YFB2602403,2022YFB2602401)and the National Natural Science Foun-dation of China(No.71971112). （Nos.2022YFB2602403,2022YFB2602401）
