Traffic Flow Prediction Model Based on Multivariate Time Series and Pattern Mining in Terminal Area

被引：0
|
作者：
Zhu W. [1 ]
Chen H. [1 ,3 ]
Liu L. [1 ]
Yuan L. [2 ]
Tian W. [2 ]
机构：
[1] College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing
[3] Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing
来源：
Transactions of Nanjing University of Aeronautics and Astronautics | 2023年 / 40卷 / 05期
基金：
中国国家自然科学基金;
关键词：
deep learning; multivariate time series; pattern mining; time series representation; traffic flow prediction;
D O I：
10.16356/j.1005-1120.2023.05.008
中图分类号：
学科分类号：
摘要：
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. © 2023 Nanjing University of Aeronautics an Astronautics. All rights reserved.
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页码：595 / 606
页数：11
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