Back to Search
Start Over
A Dual-Stream Cross AGFormer-GPT Network for Traffic Flow Prediction Based on Large-Scale Road Sensor Data.
- Source :
- Sensors (14248220); Jun2024, Vol. 24 Issue 12, p3905, 15p
- Publication Year :
- 2024
-
Abstract
- Traffic flow prediction can provide important reference data for managers to maintain traffic order, and can also be based on personal travel plans for optimal route selection. On account of the development of sensors and data collection technology, large-scale road network historical data can be effectively used, but their high non-linearity makes it meaningful to establish effective prediction models. In this regard, this paper proposes a dual-stream cross AGFormer-GPT network with prompt engineering for traffic flow prediction, which integrates traffic occupancy and speed as two prompts into traffic flow in the form of cross-attention, and uniquely mines spatial correlation and temporal correlation information through the dual-stream cross structure, effectively combining the advantages of the adaptive graph neural network and large language model to improve prediction accuracy. The experimental results on two PeMS road network data sets have verified that the model has improved by about 1.2% in traffic prediction accuracy under different road networks. [ABSTRACT FROM AUTHOR]
- Subjects :
- TRAFFIC flow
GRAPH neural networks
LANGUAGE models
TRAFFIC engineering
TRAFFIC speed
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 24
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 178190581
- Full Text :
- https://doi.org/10.3390/s24123905