Traffic signal timing via deep reinforcement learning

By Li Li, Yisheng Lv, Feiyue Wang. IEEE/CAA Journal of Automatica Sinica, 2016

540 citations recorded by the source corpus.

Subfields: Building and Construction, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/d09223da6cf1ae3b7692095a05fe45f5e6a95a13

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

In Control and Systems Engineering

Citations in the corpus

A Deep Reinforcement Learning Network for Traffic Light Cycle Control (2018, methodology): “third one is a state-of-the-art method, Deep Q Network (DQN) [13].”
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey (2020, background): “the noise on input data [40].”
IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control (2018, methodology): “To solve the in-managablely large state space of previous methods, recent studies [15, 22] propose to apply Deep Q-learning methods using continuous state representations.”
Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services (2018, methodology): “[21] also developed a DRL approach for traffic signal timing aiming to have a better signal timing plan.”
Transfer Learning in Deep Reinforcement Learning: A Survey (2020, methodology): “For example, Transportation Systems domain has adopted RL for addressing traffic congestion issues with better traffic signal scheduling and transportation resource allocation [8], [9], [169], [170].”
Big Data Analytics in Intelligent Transportation Systems: A Survey (2019, background): “Reinforcement learning is highly relevant to control and optimization theory, and it has been proved to be quite feasible in traffic signal control in ITS [75]–[78].”
Flow: A Modular Learning Framework for Mixed Autonomy Traffic (2017, methodology): “Deep RL has been used for traffic prediction [85], [86] and control [87], [88], [89].”
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario (2019, background): “Traffic signal control problem, one of the biggest urban problems, is drawing increasing attention in recent years [5, 9, 10].”
Integrating Independent and Centralized Multi-agent Reinforcement Learning for Traffic Signal Network Optimization (2019, background): “While early works proposed to optimize traffic light controllers based on expert knowledge and traditional model-based planning [4, 9, 18], there are promising recent results on applying flexible model-free methods in reinforcement learning (RL) [21] and deep RL, such as DQN in particular [16], to f”
An Introduction to Deep Reinforcement Learning (2018, unknown): “They also have the possibility to help managing ecological resources (Dietterich, 2009) or reducing greenhouse gas emissions by, e.g., optimizing traffic (Li et al., 2016).”
Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis (2018, methodology): “and Casas use DRL for controlling the timing of traffic lights [36, 37].”