CASIA OpenIR
Acting As A Decision Maker: Traffic-Condition-Aware Ensemble Learning for Traffic Flow Prediction
Yuanyuan Chen1; Hongyu Chen1,2; Peijun Ye1; Yisheng Lv1; Fei-Yue Wang1,3
Source PublicationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
2020
IssueAcceptedPages:Accepted
Abstract

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.

KeywordTraffic Flow Prediction Ensemble Learning Deep Learning
Indexed BySCI
Language英语
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40603
Collection中国科学院自动化研究所
Corresponding AuthorYisheng Lv
Affiliation1.State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences
2.Harbin University of the Science and Technology
3.Institute of Engineering, Macau University of Science and Technology
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yuanyuan Chen,Hongyu Chen,Peijun Ye,et al. Acting As A Decision Maker: Traffic-Condition-Aware Ensemble Learning for Traffic Flow Prediction[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2020(Accepted):Accepted.
APA Yuanyuan Chen,Hongyu Chen,Peijun Ye,Yisheng Lv,&Fei-Yue Wang.(2020).Acting As A Decision Maker: Traffic-Condition-Aware Ensemble Learning for Traffic Flow Prediction.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS(Accepted),Accepted.
MLA Yuanyuan Chen,et al."Acting As A Decision Maker: Traffic-Condition-Aware Ensemble Learning for Traffic Flow Prediction".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS .Accepted(2020):Accepted.
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