Name
Title | ||
|---|---|---|
Traffic-signal control reinforcement learning approach for continuous-time Markov games. |
Abstract | ||
|---|---|---|
Traffic-Signal Control (TSC) models have been transformed from simple pre-timed isolated indications to a more complex form of actuated and coordinated TSC models for highways, railroads, etc. However, existing TSC models cannot always manage inconveniences like: over-saturation, delays by incidents, congestion by weather conditions, among others, which is why this is still an open area of research. An important challenge is to propose a TSC solution model for multiple intersections, which adapts traffic signal timing according to real-time traffic. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.engappai.2019.103415 | Engineering Applications of Artificial Intelligence |
Keywords | Field | DocType |
Traffic signal,Nash games,Continuous-time,Markov models | Temporal difference learning,Mathematical optimization,Computer science,Markov model,Ergodic theory,Markov chain,Proximal Gradient Methods,Nash equilibrium,Complete information,Reinforcement learning | Journal |
Volume | ISSN | Citations |
89 | 0952-1976 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Román Aragon-Gómez | 1 | 0 | 0.34 |
| Julio B. Clempner | 2 | 91 | 20.11 |