# A Multi-Level Speed Guidance Cooperative Approach Based on Bidirectional Periodic Green Wave Coordination Under Intelligent and Connected Environment

## TL;DR

This paper introduces a new traffic control method that uses speed guidance and green wave coordination to reduce delays and improve intersection efficiency in smart cities.

## Contribution

A novel bi-level combinatorial optimization method combining deep Q learning and genetic algorithms for green wave coordination.

## Key findings

- The proposed method reduces average delay by 20.76% compared to conventional strategies.
- It also decreases the number of stops by 44.49% in coordinated intersections.
- The method improves green light time utilization in intelligent and connected environments.

## Abstract

To maximize arterial green wave bandwidth utilization, this study aims to minimize average travel delays at coordinated intersections and maximize vehicle throughput. In view of the aforementioned points, the present paper sets out a collaborative optimization method for the control of related intersection groups. The method combines multi-level speed guidance with green wave coordinated control. In an intelligent and connected environment (ICE), the driving trajectory of the initial vehicle is determined in each optimization cycle following the receipt of active speed guidance. Subsequently, the driving trajectories of subsequent vehicles are calculated, with an assessment made as to whether they can leave the intersection before the end of the green light. The subsequent step involves the calculation of a characteristic index, comprising the average speed of the arterial coordination section and its corresponding phase offset. The phase offset is then optimized with the objective of maximizing the comprehensive bandwidth of green wave coordination within the control range. The maximum average speed and the bidirectional cycle comprehensive green wave bandwidth are employed as the control objectives. Finally, a model is constructed through the combination of multi-level vehicle speed guidance with bidirectional cycle green wave coordinated control. A bi-level combinatorial optimization method is constructed through a combinatorial deep Q learning method, named Deep Q Network-Genetic Algorithm (DQNGA), with the objective of obtaining the global optimal solution. Finally, the reliability of the method is validated using traffic flow data and map sensor data on several associated road sections in a city. The results demonstrate that the proposed method reduces the average delay and number of stops by 20.76% and 44.49%, respectively, outperforming conventional traffic control strategies. This suggests that the issue of inefficient utilization of green light time in arterial coordinated signal control has been effectively addressed. Consequently, the efficiency of intersections in the intelligent and connected environment has been enhanced.