แนวคิดหลัก
Proposing a new approach using genetic programming for efficient and explainable traffic signal control.
บทคัดย่อ
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.
สถิติ
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.
คำพูด
"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."