Reinforcement learning for true adaptive traffic signal control

By Baher Abdulhai, R. Pringle, G. J. Karakoulas. 2003

543 citations recorded by the source corpus.

Subfields: Automotive Engineering, Building and Construction, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/6c1309a5cb147856f785253d99424685cf5c12fc

How researchers see this paper

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

In Automotive Engineering

In Building and Construction

In Control and Systems Engineering

Citations in the corpus

PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network (2019, methodology): “Earlier work using tabular Q-learning [3, 9] can only deal with discrete state representations.”
Statistical methods versus neural networks in transportation research: Differences, similarities and some insights (2011, methodology): “1 Some studies in transportation have also used NN for traffic control; such research includes the work of Bingham (2001), Abdulhai et al. (2003), Srinivasan et al.”
Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning (2017, background): “In particular, signiﬁcant research has been conducted employing reinforcement learning methods in the area of traﬃc light signal control [39, 2, 7].”
A Survey on Traffic Signal Control Methods (2019, methodology): “In the problem scenario of signal control, earlier methods use simple state features like levels of congestion (which can be converted to discrete state values) and employ tabular Q-learning [Abdulhai et al. 2003; El-Tantawy and Abdulhai 2010] to learn the value function.”
Using a Deep Reinforcement Learning Agent for Traffic Signal Control (2016, background): “Previous research efforts have defined the state space as some attribute of traffic, the number of queued vehicles [10][12][13][14] and traffic flow [15][6] the”
IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control (2018, background): “Methods in [1, 2, 4, 8, 24] designed the state as discrete values like the location of vehicles or number of waited cars.”
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”
A Deep Reinforcement Learning Network for Traffic Light Cycle Control (2018, background): “Due to the technique limitation at the time in reinforcement learning, they usually make a small-size state space, such as the number of waiting vehicles [8], [19], [20] and the statistics of traffic flow [21], [22].”
Data-Driven Intelligent Transportation Systems: A Survey (2011, methodology): “[90] proposed a Q-learning algorithm, which is a type of”
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].”
Reinforcement Learning With Function Approximation for Traffic Signal Control (2011, methodology): “In [1], Q-learning with lookup table representation has been”
Multiagent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLIN-ATSC): Methodology and Large-Scale Application on Downtown Toronto (2013, background): “However, the applicability of such approaches is limited to optimize a few traffic signal agents due to the obvious exponentially increasing joint space of states with the increase in the number of agents [8].”