Research Article
A Hybrid Deep Learning Approach for Traffic Flow Prediction in Highway Domain
@INPROCEEDINGS{10.1007/978-3-030-67540-0_15, author={Zhe Wang and Weilong Ding and Hui Wang}, title={A Hybrid Deep Learning Approach for Traffic Flow Prediction in Highway Domain}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2021}, month={1}, keywords={Traffic flow prediction Deep learning Spatio-temporal data}, doi={10.1007/978-3-030-67540-0_15} }- Zhe Wang
Weilong Ding
Hui Wang
Year: 2021
A Hybrid Deep Learning Approach for Traffic Flow Prediction in Highway Domain
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-030-67540-0_15
Abstract
With the development of cities, intercity highway plays a vital role in people’s daily travel. The traffic flow on the highway network is also increasingly concerned by road managers and participants. However, due to the influence of highway network topology and extra feature such as weather, accurate traffic flow prediction becomes hard to achieve. It is difficult to construct a multidimensional feature matrix and predict the traffic flow of the network at one time. A novel prediction method based on a hybrid deep learning model is proposed, which can learn multidimensional feature and predict network-wise traffic flow efficiently. The experiment shows that the prediction accuracy of this method is significantly better than existing methods, and it has a good performance during the prediction.
