Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network (1705.02755v1)

Abstract: Adaptive traffic signal control, which adjusts traffic signal timing according to real-time traffic, has been shown to be an effective method to reduce traffic congestion. Available works on adaptive traffic signal control make responsive traffic signal control decisions based on human-crafted features (e.g. vehicle queue length). However, human-crafted features are abstractions of raw traffic data (e.g., position and speed of vehicles), which ignore some useful traffic information and lead to suboptimal traffic signal controls. In this paper, we propose a deep reinforcement learning algorithm that automatically extracts all useful features (machine-crafted features) from raw real-time traffic data and learns the optimal policy for adaptive traffic signal control. To improve algorithm stability, we adopt experience replay and target network mechanisms. Simulation results show that our algorithm reduces vehicle delay by up to 47% and 86% when compared to another two popular traffic signal control algorithms, longest queue first algorithm and fixed time control algorithm, respectively.

• The paper introduces a deep reinforcement learning approach for adaptive traffic signal control, utilizing machine-crafted features directly from raw traffic data for optimal policy learning.
• The method incorporates experience replay and a target network, key techniques used to stabilize the deep reinforcement learning algorithm and ensure robust convergence.
• Numerical simulations show the algorithm effectively reduces vehicle delay, demonstrating up to 47% improvement over longest queue first and 86% over fixed-time control.

Adaptive Reinforcement Learning for Traffic Signal Control

The paper "Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network" addresses the issue of traffic congestion, a prominent concern in urban environments due to increased vehicle proliferation, insufficient infrastructure, and inefficient traffic signal control mechanisms. Fixed-time signal controls are inadequate due to their lack of adaptability to dynamic traffic demands, leading to the compelling need for intelligent, responsive traffic systems. This work proposes a solution through a deep reinforcement learning (DRL) approach tailored for adaptive traffic signal control.

Problem Statement and Approach

Fixed-time traffic signal control predetermines intersection signals based on historical data, undermining the flexibility required for handling fluctuating traffic loads. Conversely, adaptive traffic signal control can accommodate real-time traffic dynamics, purportedly reducing congestion effectively.

The paper introduces a DRL model which abandons human-crafted features such as vehicle queue lengths and average delays. These features abstract raw traffic data, leading to suboptimal signal control since they overlook transient traffic information. The novel DRL system extracts machine-crafted features directly from raw data, including vehicle speed, position, and signal states by employing convolutional neural networks (CNNs) for feature extraction and optimal policy learning.

Key Components

1. Intersection Model: The authors conceptualize a typical four-way intersection, with lanes designated for different maneuvers, controlled by timed signals. The DRL agent perceives intersection states as matrix representations of vehicle positions and velocities.
2. State, Action, Reward: The model frames the traffic control problem as a Markov decision process, defining the state as the current traffic configuration and signals, actions as the alternatives for signal switching, and the reward based on changes in vehicle staying time.
3. Experience Replay and Target Network: The paper tackles algorithm instability—an inherent challenge in DRL—by integrating experience replay and employing a separate target network for stable Q-learning updates. These techniques enhance the robustness and convergence of the learning process.

Numerical Results

Simulation outcomes are central to demonstrating this approach's efficacy. The proposed algorithm reportedly reduces vehicle delay by up to 47% compared to the longest queue first algorithm and 86% compared to the fixed-time control algorithm. Evaluation involves thorough traffic simulations reflecting diverse vehicle entry scenarios while emphasizing fairness, where no specific road experiences excessively longer delays.

Implications and Future Research Directions

Practical implications of this research underscore significant improvements in urban traffic management systems through intelligent adaptability and real-time responsiveness. The DRL framework signifies advancements beyond static configurations, offering viable solutions for reducing urban congestion, fuel consumption, and environmental pollution.

Future research could explore scaling the method to complex road networks beyond single intersections and integrating multi-agent DRL models to enhance interoperability and coordination across traffic grids. Additionally, incorporating real-world network constraints and sensor inaccuracies are vital considerations for future enhancements.

Conclusion

The paper on adaptive traffic signal control via DRL presents a methodologically sound and computationally viable approach to mitigating traffic congestion. The research offers promising avenues for deploying AI-driven solutions in transportation, echoing the potential of intelligent traffic systems in urban planning and smart city frameworks.