Name
Title | ||
|---|---|---|
Meta-learning based spatial-temporal graph attention network for traffic signal control |
Abstract | ||
|---|---|---|
Traffic signal control is of great importance to the urban transportation systems and public travel, yet it becomes challenging because of two essential factors. First, spatial–temporal correlations are crucial to an intersection scenario. However, existing works have either considered only one of these features or a simple fusion of spatial and temporal information without adequately exploiting the potential correlations. Second, some works using graph neural network treats static graph nodes among adjacent intersections, ignoring the fact that intersection traffic is changing dynamically. These dynamically changing characteristics of an intersection are likewise significant for traffic signal prediction. If these problems are not resolved, the traffic pressure will increase and people’s time will be wasted. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.knosys.2022.109166 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Traffic signal control,Reinforcement learning,Spatial–temporal modeling,Graph attention network,Deep meta-learning,Traffic congestion | Journal | 250 |
ISSN | Citations | PageRank |
0950-7051 | 0 | 0.34 |
References | Authors | |
0 | 6 | |