Spatial-temporal feature extraction and evaluation network for citywide traffic condition prediction
Traffic condition prediction is crucial for executing traffic control and scheduling tasks within intelligent transportation systems. With diversified data sources, effectively modeling the complex spatial-temporal dependencies in the whole traffic network and predicting nonlinear general traffic condition changes become primary challenges for intelligent transportation systems. In this paper, a double-layer spatial-temporal feature extraction and evaluation (DL-STFEE) model is proposed, aimed at accurately predicting the traffic condition transferring of the whole traffic network. Firstly, a public traffic dataset is processed to extract the spatial and temporal features of vehicles, as well as the clusters of traffic conditions of the entire traffic network. Secondly, a double-layer deep learning traffic condition predictor is proposed. The model incorporates a spatial-temporal feature extraction layer, which leverages the graph convolution network (GCN) and attention mechanism to obtain spatiotemporal features across the whole traffic network. Additionally, a spatial-temporal combination layer employs a high-dimensional self-attention mechanism to integrate features across spatial-temporal combinations, bolstering prediction accuracy. Finally, through rigorous experiments, the contributions of neural network structures on the spatial-temporal feature extraction are comprehensively analyzed and the experiments also validate the effectiveness of traffic condition prediction by the proposed DL-STFEE model.
State Scholarship Funding of CSC under Grant 202206170067
Changsha Automotive Innovation Research Institute Innovation Project-Research on Intelligent Trip Planning System of Pure Electric Vehicles Based on Big Data under Grant CAIRIZT20220105
Science and technology planning project in Yibin city under Grant 2020GY001
Science and technology planning project in Tianjin city under Grant 20YFZCGX00770
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering
Published inIEEE Transactions on Intelligent Vehicles
Pages1 - 16
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
- AM (Accepted Manuscript)
Rights holder© IEEE
Publisher statement© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.