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A Hybrid Deep Learning Model for Short-Term Traffic Flow Pre-Diction Considering Spatiotemporal Features
| Content Provider | MDPI |
|---|---|
| Author | Zhou, Shenghan Wei, Chaofan Song, Chaofei Fu, Yu Luo, Rui Chang, Wenbing Yang, Linchao |
| Copyright Year | 2022 |
| Description | Traffic flow prediction is one of the basic, key problems with developing an intelligent transportation system since accurate and timely traffic flow prediction can provide information support and decision support for traffic control and guidance. However, due to the complex characteristics of traffic information, it is still a challenging task. This paper proposes a novel hybrid deep learning model for short-term traffic flow prediction by considering the inherent features of traffic data. The proposed model consists of three components: the recent, daily and weekly components. The recent component is integrated with an improved graph convolutional network (GCN) and bi-directional LSTM (Bi-LSTM). It is designed to capture spatiotemporal features. The remaining two components are built by multi-layer Bi-LSTM. They are developed to extract the periodic features. The proposed model focus on the important information by using an attention mechanism. We tested the performance of our model with a real-world traffic dataset and the experimental results indicate that our model has better prediction performance than those developed previously. |
| Starting Page | 10039 |
| e-ISSN | 20711050 |
| DOI | 10.3390/su141610039 |
| Journal | Sustainability |
| Issue Number | 16 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-13 |
| Access Restriction | Open |
| Subject Keyword | Sustainability Transportation Science and Technology Traffic Flow Prediction Hybrid Deep Learning Bi-lstm Graph Convolution Network |
| Content Type | Text |
| Resource Type | Article |
