Name
Title | ||
|---|---|---|
Reinforcement Learning-Based Variable Speed Limit Control Strategy to Reduce Traffic Congestion at Freeway Recurrent Bottlenecks. |
Abstract | ||
|---|---|---|
The primary objective of this paper was to incorporate the reinforcement learning technique in variable speed limit (VSL) control strategies to reduce system travel time at freeway bottlenecks. A Q-learning (QL)-based VSL control strategy was proposed. The controller included two components: a QL-based offline agent and an online VSL controller. The VSL controller was trained to learn the optimal ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TITS.2017.2687620 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
Traffic control,Optimization,Optimal control,Heuristic algorithms,Learning (artificial intelligence),Computational modeling,Adaptive control | Bottleneck,Control theory,Traffic flow,Optimal control,Cell Transmission Model,Simulation,Traffic simulation,Engineering,Adaptive control,Traffic congestion | Journal |
Volume | Issue | ISSN |
18 | 11 | 1524-9050 |
Citations | PageRank | References |
7 | 0.59 | 16 |
Authors | ||
5 | ||