Acting as a Decision Maker: Traffic-Condition-Aware Ensemble Learning for Traffic Flow Prediction

被引:18
作者
Chen, Yuanyuan [1 ]
Chen, Hongyu [1 ,2 ]
Ye, Peijun [1 ,3 ]
Lv, Yisheng [1 ]
Wang, Fei-Yue [1 ,4 ]
机构
[1] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
[2] Harbin Univ Sci & Technol, Sch Automat, Harbin 150080, Peoples R China
[3] Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China
[4] Macau Univ Sci & Technol, Inst Engn, Taipa, Macao, Peoples R China
基金
中国国家自然科学基金;
关键词
Traffic flow prediction; ensemble learning; deep learning; VOLUME;
D O I
10.1109/TITS.2020.3032758
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Accurate traffic prediction under various conditions is an important but challenging task. Due to the complicated non-stationary temporal dynamics in traffic flow time series and spatial dependencies on roadway networks, there is no particular method that is clearly superior to all others. Here, we focus on investigating ensemble learning that benefits from multiple base models, and propose a traffic-condition-aware ensemble approach that acts as a decision maker by stacking multiple predictions based on dynamic traffic conditions. To sense traffic conditions, we apply the Convolutional Neural Network (CNN) model to capture the spatiotemporal patterns embedded in traffic flow. Then, the high-level features extracted by CNN are used to generate weights to ensemble multiple predictions of different models. Extensive experiments are performed with a real traffic dataset from the Caltrans Performance Measurement System. We compare the proposed approach with competitive models, including Gradient Boosting Regression Tree (GBRT) model, Weight Regression model, Support Vector Regression (SVR) model, Long Short-term Memory (LSTM) model, Historical Average (HA) model and CNN model. Experimental results demonstrate that our method can effectively improve the performances of traffic flow prediction.
引用
收藏
页码:3190 / 3200
页数:11
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