Learning Traffic Signal Control via Genetic Programming: A New Approach for Efficient Traffic Signal Control

Concepts de base

Proposing a new approach using genetic programming for efficient and explainable traffic signal control.

Résumé

This article introduces a new method, GPLight, that uses genetic programming to optimize traffic signal control. It addresses the limitations of existing methods, such as DRL, by evolving an urgency function to determine the most appropriate signal phase. The proposed approach outperforms traditional methods and a well-known DRL method in various scenarios. The explainability and effectiveness of the evolved urgency function are highlighted, providing insights for future research in traffic signal control.

Abstract:

• Learning-based methods, like Deep Reinforcement Learning (DRL), have shown success in traffic signal control.
• Proposed method, GPLight, uses genetic programming to optimize urgency function for signal control.
• Experimental results show GPLight outperforms traditional methods and DRL in traffic signal control.

Introduction:

• Traffic signal control is crucial for transportation efficiency and safety.
• Existing DRL methods face challenges in reward design and explainability.
• GPLight proposes a new approach using genetic programming for traffic signal control optimization.

Background:

• Traditional traffic signal control methods include fixed-time, actuated, and selection-based adaptive control.
• Optimization-based methods rely on human expertise and optimization processes.
• DRL methods, like MPLight, use reinforcement learning for traffic signal control.

GPLight:

• Urgency function calculates phase urgency based on lane features.
• Signal control is determined by urgency function using genetic programming.
• Evolutionary process involves selection, crossover, and mutation to optimize urgency function.

Experiments:

• Tested on real-world traffic datasets in CityFlow simulator.
• Compared with traditional methods and DRL methods like MPLight.
• GPLight consistently outperforms other methods in terms of average travel time.

Performance Comparison:

• GPLight shows significant improvement over traditional methods and DRL methods.
• Feature importance analysis reveals the significance of incoming lane features.
• Evolved urgency function demonstrates explainability and effectiveness in traffic signal control.

Stats

Genetic programming is adopted to perform gradient-free optimization of the urgency function.
The proposed GPLight outperforms traditional methods and a well-known DRL-based method in traffic signal control.

Citations

"The proposed GPLight treats the traffic signal control as a black-box model and conducts a global search for the most accurate urgency function."
"The urgency function can calculate the phase urgency for a specific phase based on the current road conditions."

Idées clés tirées de

Learning Traffic Signal Control via Genetic Programming

by Xiao-Cheng L... à arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17328.pdf

Questions plus approfondies

How can the explainability of traffic signal control strategies be further improved

To further improve the explainability of traffic signal control strategies, several approaches can be considered. One way is to incorporate visualization techniques to represent the decision-making process of the urgency function. This could involve creating graphical representations of the evolved urgency functions, highlighting the key features and rules that contribute to the decision-making process. Additionally, providing detailed explanations or annotations for each part of the urgency function can enhance understanding. Another approach is to develop interactive tools or interfaces that allow users to explore and interact with the urgency function, enabling them to see how different inputs affect the output and how the urgency values are calculated. Furthermore, incorporating natural language explanations or summaries of the urgency function's behavior can make it more accessible to a wider audience, including stakeholders who may not have a technical background in genetic programming or traffic signal control.

What are the potential challenges of scaling up the proposed GPLight method to larger traffic networks

Scaling up the proposed GPLight method to larger traffic networks may present several challenges. One challenge is the increased complexity and computational resources required to handle a larger number of intersections and roads. As the network size grows, the search space for evolving the urgency function also expands, leading to longer optimization times and potentially higher computational costs. Another challenge is the potential for increased traffic congestion and variability in larger networks, which may require more sophisticated urgency functions to effectively control traffic signals. Additionally, ensuring the scalability and efficiency of the genetic programming approach in optimizing urgency functions for large-scale networks is crucial, as the method needs to maintain performance and effectiveness across a wide range of traffic scenarios. Addressing these challenges will be essential for successfully applying GPLight to larger traffic networks.

How can the concept of urgency function evolved by genetic programming be applied to other optimization problems beyond traffic signal control

The concept of urgency function evolved by genetic programming in traffic signal control can be applied to other optimization problems beyond traffic signal control by adapting the methodology to suit the specific problem domain. For example, in supply chain management, the urgency function could be used to optimize inventory levels or production schedules based on real-time demand and resource availability. In healthcare, the urgency function could be applied to optimize patient scheduling in hospitals or healthcare facilities to minimize wait times and improve efficiency. By defining the problem-specific features and objectives, the urgency function can be tailored to address the unique requirements of different optimization problems. The flexibility and adaptability of genetic programming make it a versatile tool for evolving solutions to a wide range of optimization challenges.