Name
Abstract | ||
|---|---|---|
Traffic flow prediction has received extensive attention recently, since it is a key step to prevent and mitigate traffic congestion in urban areas. However, most previous studies on traffic flow prediction fail to capture fine-grained traffic information (like link-level traffic) and ignore the impacts from other factors, such as route structure and weather conditions. In this paper, we propose a deep and embedding learning approach (DELA) that can help to explicitly learn from fine-grained traffic information, route structure, and weather conditions. In particular, our DELA consists of an embedding component, a convolutional neural network (CNN) component and a long short-term memory (LSTM) component. The embedding component can capture the categorical feature information and identify correlated features. Meanwhile, the CNN component can learn the 2-D traffic flow data while the LSTM component has the benefits of maintaining a long-term memory of historical data. The integration of the three models together can improve the prediction accuracy of traffic flow. We conduct extensive experiments on realistic traffic flow dataset to evaluate the performance of our DELA and make comparison with other existing models. The experimental results show that the proposed DELA outperforms the existing methods in terms of prediction accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tits.2019.2909904 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
Meteorology,Predictive models,Urban areas,Deep learning,Sensors,Roads,Informatics | Computer vision,Informatics,Urban informatics,Embedding,Traffic flow,Convolutional neural network,Categorical variable,Artificial intelligence,Deep learning,Engineering,Traffic congestion,Machine learning | Journal |
Volume | Issue | ISSN |
20 | 10 | 1524-9050 |
Citations | PageRank | References |
3 | 0.38 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zibin Zheng | 1 | 3731 | 199.37 |
| Ya-Tao Yang | 2 | 61 | 7.14 |
| jiahao liu | 3 | 23 | 11.31 |
| Hongning Dai | 4 | 629 | 62.25 |
| Yan Zhang | 5 | 5818 | 354.13 |