An AutoEncoder and LSTM-Based Traffic Flow Prediction Method
Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow pre...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/13/2946 |
| _version_ | 1811187156720287744 |
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| author | Wangyang Wei Honghai Wu Huadong Ma |
| author_facet | Wangyang Wei Honghai Wu Huadong Ma |
| author_sort | Wangyang Wei |
| collection | DOAJ |
| description | Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days. |
| first_indexed | 2024-04-11T13:57:27Z |
| format | Article |
| id | doaj.art-d4d7630a3c5e4f6e8af92feb400c5f64 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:57:27Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d4d7630a3c5e4f6e8af92feb400c5f642022-12-22T04:20:13ZengMDPI AGSensors1424-82202019-07-011913294610.3390/s19132946s19132946An AutoEncoder and LSTM-Based Traffic Flow Prediction MethodWangyang Wei0Honghai Wu1Huadong Ma2Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaInformation Engineering College, Henan University of Science and Technology, Luoyang 471023, ChinaBeijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSmart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.https://www.mdpi.com/1424-8220/19/13/2946AutoEncoderlong short-term memorytraffic flow prediction |
| spellingShingle | Wangyang Wei Honghai Wu Huadong Ma An AutoEncoder and LSTM-Based Traffic Flow Prediction Method Sensors AutoEncoder long short-term memory traffic flow prediction |
| title | An AutoEncoder and LSTM-Based Traffic Flow Prediction Method |
| title_full | An AutoEncoder and LSTM-Based Traffic Flow Prediction Method |
| title_fullStr | An AutoEncoder and LSTM-Based Traffic Flow Prediction Method |
| title_full_unstemmed | An AutoEncoder and LSTM-Based Traffic Flow Prediction Method |
| title_short | An AutoEncoder and LSTM-Based Traffic Flow Prediction Method |
| title_sort | autoencoder and lstm based traffic flow prediction method |
| topic | AutoEncoder long short-term memory traffic flow prediction |
| url | https://www.mdpi.com/1424-8220/19/13/2946 |
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