Traffic Signal Control Using Hybrid Action Space Deep Reinforcement Learning | JoVE Visualize

Traffic Signal Control Using Hybrid Action Space Deep Reinforcement Learning

Salah Bouktif¹, Abderraouf Cheniki², Ali Ouni³

¹Department of Computer Science and Software Engineering, University of United Arab Emirates, Al Ain 15551, Abu Dhabi, United Arab Emirates.

Sensors (Basel, Switzerland)|April 3, 2021

View abstract on PubMed

Summary

This study introduces a hybrid deep reinforcement learning (DRL) approach for traffic signal control (TSC). The novel framework simultaneously optimizes traffic light phase and duration, outperforming existing DRL methods.

Area of Science:

Artificial Intelligence
Transportation Engineering
Computer Science

Background:

Intelligent traffic signal control (TSC) heavily relies on deep reinforcement learning (DRL).
Existing DRL frameworks for TSC are limited to either discrete (phase selection) or continuous (phase duration) control.
A flexible approach combining both discrete and continuous DRL for TSC is lacking.

Purpose of the Study:

To propose a novel hybrid DRL framework for traffic signal control.
To develop an approach that simultaneously determines the optimal traffic light phase and its duration.
To enhance the efficiency of traffic signal systems through integrated decision-making.

Main Methods:

Adapted a hybrid Deep Reinforcement Learning (DRL) approach.
Customized a Parameterized Deep Q-Networks (P-DQN) architecture for hierarchical decision-making.
Evaluated the framework using Simulation of Urban MObility (SUMO).

Main Results:

The proposed hybrid DRL framework demonstrated superior performance compared to existing benchmarks.
Achieved a 22.20% reduction in average vehicle queue length.
Achieved a 5.78% reduction in average travel time.

Conclusions:

The hybrid DRL approach effectively integrates discrete and continuous decision-making for TSC.
The P-DQN based framework offers a flexible and high-performing solution for intelligent traffic management.
Significant improvements in traffic flow efficiency were observed, highlighting the framework's practical applicability.

Keywords: