Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel traffic control architecture which is based on fog computing paradigm and reinforcement leaning technologies. We firstly provide an overview of this framework and detail the components and workflows designed to relieve traffic congestion. These workflows, which are connecting traffic lights, vehicles, Fog nodes and traffic cloud, aim to generate traffic light control flow and communication flow for each intersection to avoid a traffic jam. In order to make the whole city’s traffic highly efficient, the fog computing paradigm and a distributed reinforcement learning algorithm is designed to overcome communication bandwidth limitation and local optimal traffic control flow, respectively. We also demonstrate that our framework outperforms traditional systems and provides high practicability in future research for building the intelligent transportation system. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.future.2019.02.058 | Future Generation Computer Systems |
Keywords | Field | DocType |
Fog computing,Traffic congestion,Reinforcement learning,WorkFlows | Architecture,Traffic signal,Computer science,Control flow,Real-time computing,Communication bandwidth,Intelligent transportation system,Workflow,Traffic congestion,Cloud computing,Distributed computing | Journal |
Volume | ISSN | Citations |
97 | 0167-739X | 3 |
PageRank | References | Authors |
0.42 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiang Wu | 1 | 304 | 40.42 |
| Jun Shen | 2 | 20 | 8.82 |
| Binbin Yong | 3 | 21 | 5.23 |
| Jun Shen | 4 | 234 | 40.40 |
| Fucun Li | 5 | 6 | 2.52 |
| Jinqiang Wang | 6 | 4 | 1.11 |
| Qingguo Zhou | 7 | 103 | 29.48 |