Spatiotemporal Traffic Flow Prediction with KNN and LSTM
Joint Authors
Luo, Xianglong
Li, Danyang
Yang, Yu
Zhang, Shengrui
Source
Journal of Advanced Transportation
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-27
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems.
Accurate prediction result is the precondition of traffic guidance, management, and control.
To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper.
KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow.
LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations.
The final prediction results are obtained by result-level fusion with rank-exponent weighting method.
The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center.
Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.
American Psychological Association (APA)
Luo, Xianglong& Li, Danyang& Yang, Yu& Zhang, Shengrui. 2019. Spatiotemporal Traffic Flow Prediction with KNN and LSTM. Journal of Advanced Transportation،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1169854
Modern Language Association (MLA)
Luo, Xianglong…[et al.]. Spatiotemporal Traffic Flow Prediction with KNN and LSTM. Journal of Advanced Transportation No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1169854
American Medical Association (AMA)
Luo, Xianglong& Li, Danyang& Yang, Yu& Zhang, Shengrui. Spatiotemporal Traffic Flow Prediction with KNN and LSTM. Journal of Advanced Transportation. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1169854
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1169854