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Highway Speed Prediction Using Gated Recurrent Unit Neural Networks
| Content Provider | MDPI |
|---|---|
| Author | Jeong, Myeong-Hun Lee, Tae-Young Jeon, Seung-Bae Youm, Minkyo |
| Copyright Year | 2021 |
| Description | Movement analytics and mobility insights play a crucial role in urban planning and transportation management. The plethora of mobility data sources, such as GPS trajectories, poses new challenges and opportunities for understanding and predicting movement patterns. In this study, we predict highway speed using a gated recurrent unit (GRU) neural network. Based on statistical models, previous approaches suffer from the inherited features of traffic data, such as nonlinear problems. The proposed method predicts highway speed based on the GRU method after training on digital tachograph data (DTG). The DTG data were recorded in one month, giving approximately 300 million records. These data included the velocity and locations of vehicles on the highway. Experimental results demonstrate that the GRU-based deep learning approach outperformed the state-of-the-art alternatives, the autoregressive integrated moving average model, and the long short-term neural network (LSTM) model, in terms of prediction accuracy. Further, the computational cost of the GRU model was lower than that of the LSTM. The proposed method can be applied to traffic prediction and intelligent transportation systems. |
| Starting Page | 3059 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app11073059 |
| Journal | Applied Sciences |
| Issue Number | 7 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-03-29 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Transportation Science and Technology Traffic Speed Prediction Gated Recurrent Unit Long Short-term Memory |
| Content Type | Text |
| Resource Type | Article |
