Federated Traffic Signal Control for Diverse Intersection Conditions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional traffic signal optimization methods are inadequate in addressing diverse intersection characteristics and evolving traffic dynamics, leading to inefficiencies and increased congestion in urban areas.
Innovation Solution
A system and method utilizing federated and reinforcement learning to adapt traffic signal control to unique intersection demands, aggregating model parameters across local, intermediate, and central servers to optimize traffic management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional traffic signal optimization methods are used, then implementation is simple, but they are inadequate in addressing diverse intersection characteristics and evolving traffic dynamics
Solution Approach 1:
The system segments the traffic control problem into multiple model types (first model type and second model type) that can be independently trained and applied to different intersection characteristics. Local models are trained at individual intersections, intermediate servers aggregate parameters for specific model types, and a central server coordinates across all intersections. This segmentation allows the system to adapt to diverse intersection characteristics while managing complexity through modular architecture.
Solution Approach 2:
The system introduces a hierarchical dimension to the traffic control architecture, adding intermediate servers between local intersections and the central server. This creates multiple levels of aggregation (local → intermediate → central) that enable the system to handle diverse intersection characteristics by processing and aggregating data at appropriate hierarchical levels, thereby improving adaptability without overwhelming central control with all raw data.
2Productivity
If federated and reinforcement learning are used to adapt to local conditions, then traffic signal efficiency is enhanced, but computational complexity increases across multiple servers
Solution Approach 1:
The system extracts the computationally intensive model training process from centralized control and distributes it to local models at each intersection through federated learning. Each local model trains independently using local traffic data, and only model parameters (not raw data) are transmitted to intermediate and central servers for aggregation. This extraction enables traffic signal efficiency enhancement through adaptive local learning while reducing the computational burden on central infrastructure.
Solution Approach 2:
Local models perform self-service by autonomously training and adapting to local intersection conditions using reinforcement learning. Each local model independently processes local traffic patterns and generates optimized signal control parameters without requiring constant central intervention. This self-service capability enhances traffic signal efficiency through localized adaptation while minimizing the computational complexity required at remote servers, which only need to aggregate parameters rather than perform full model training.
3Adaptability or versatility
If multiple model types are aggregated across intermediate servers, then routing optimization improves, but data communication overhead increases
Solution Approach 1:
The system implements partial aggregation by having intermediate servers aggregate parameters for specific model types (first model type and second model type) separately rather than aggregating all parameters at once. This partial action approach allows routing optimization to improve progressively as different model types are aggregated and applied, while reducing data communication overhead by transmitting only the necessary parameter subsets between intermediate and central servers rather than complete model datasets.
Data Source
AI summary
A system and method training local models associated with a roadside device to form sets of model parameters for controlling a portion of a roadway. Each local model is associated with different model type. Parameters are communicated to intermediate serves, are aggregated by model type and communicated to a global server where common models have parameters aggregated together. At a global server global parameters are generated by aggregation. The first global parameters for a first model type and the second global parameters for a second model type are communicated to update the local models by communicating the global parameters through the intermediate servers. The roadside devices are operated with the first global parameters or the second global parameters.