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During the running of the traffic vehicle, the traffic from other road sections to the target road section is uninterrupted, and the traffic of the adjacent road sections will affect the future traffic flow of the target road section. Therefore, this chapter uses the traffic changes of adjacent road sections and the traffic changes of the target road sections to build multi-dimensional datasets. Based on different model frameworks, four traffic flow prediction models are established, namely the Elman model, LSTM model, WPD-Elman model, and WPD-LSTM model. According to the experimental results, the traffic flow prediction performance of the WPD-Elman model using multi-dimensional data is the best. When building a traffic flow prediction model, the wavelet packet algorithm can effectively improve the accuracy of the model.
During the running of the traffic vehicle, the traffic from other road sections to the target road section is uninterrupted, and the traffic of the adjacent road sections will affect the future traffic flow of the target road section. Therefore, this chapter uses the traffic changes of adjacent road sections and the traffic changes of the target road sections to build multi-dimensional datasets. Based on different model frameworks, four traffic flow prediction models are established, namely the Elman model, LSTM model, WPD-Elman model, and WPD-LSTM model. According to the experimental results, the traffic flow prediction performance of the WPD-Elman model using multi-dimensional data is the best. When building a traffic flow prediction model, the wavelet packet algorithm can effectively improve the accuracy of the model.
Prediction Models of Traffic Flow Driven Based on Multi-Dimensional Data in Smart Traffic Systems
Liu, Hui (author)
Smart Cities: Big Data Prediction Methods and Applications ; Chapter: 7 ; 195-223
2020-03-26
29 pages
Article/Chapter (Book)
Electronic Resource
English
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