Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems

no code implementations • 21 Apr 2021 • Alvaro Cabrejas-Egea, Raymond Zhang, Neil Walton

Recently, Intelligent Transportation Systems are leveraging the power of increased sensory coverage and computing power to deliver data-intensive solutions achieving higher levels of performance than traditional systems.

Q-Learning reinforcement-learning +1

Assessment of Reward Functions in Reinforcement Learning for Multi-Modal Urban Traffic Control under Real-World limitations

no code implementations • 17 Oct 2020 • Alvaro Cabrejas-Egea, Colm Connaughton

We use a calibrated model in terms of demand, sensors, green times and other operational constraints of a real intersection in Greater Manchester, UK.

reinforcement-learning Reinforcement Learning (RL)

Assessment of Reward Functions for Reinforcement Learning Traffic Signal Control under Real-World Limitations

no code implementations • 26 Aug 2020 • Alvaro Cabrejas-Egea, Shaun Howell, Maksis Knutins, Colm Connaughton

Adaptive traffic signal control is one key avenue for mitigating the growing consequences of traffic congestion.