Universal Attention-Based Reinforcement Learning for Traffic Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing traffic congestion control methods require training of computing systems for each new intersection with unique structures or traffic flow distributions, making them inefficient and costly, especially with varying numbers of roads, lanes, and phases.
Innovation Solution
A deep reinforcement learning model with two attention models is developed to handle different numbers of roads and phases, enabling decision-making across various intersection configurations by training a single universal model that can be applied to multiple environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a reinforcement learning model is trained for each new intersection with unique structures or traffic flow distributions, then the control accuracy for that specific intersection is improved, but the time and computational resources required for training increase significantly
Solution Approach 1:
The patent applies universality by designing a single reinforcement learning model architecture that can handle multiple intersection configurations with varying numbers of roads, lanes, and phases. The model uses attention mechanisms to dynamically adapt to different intersection structures without requiring retraining, allowing one model to serve multiple functions across diverse intersection types.
Solution Approach 2:
The patent utilizes parameter changes by modifying the attention mechanism parameters and model inputs to accommodate different intersection configurations. The model adjusts its internal parameters and attention weights based on the specific characteristics of each intersection, enabling accurate control without retraining while adapting to varying numbers of roads, lanes, and phases.
2Ease of manufacture
If traditional traffic signal control methods are used with fixed cycles and adaptive measures, then the system is simple to implement, but it cannot effectively handle varying numbers of roads, lanes, and phases across different intersections
Solution Approach 1:
The patent applies dynamics by implementing a reinforcement learning model with attention mechanisms that can dynamically adapt to different intersection configurations. The model processes variable inputs representing different numbers of roads, lanes, and phases, and dynamically adjusts its control decisions based on the specific characteristics of each intersection, making the system both simple to deploy and highly adaptable.
Solution Approach 2:
The patent uses segmentation by dividing the intersection control problem into distinct components that can be independently processed. The attention mechanism segments the input data into relevant features corresponding to different roads, lanes, and phases, allowing the model to handle varying configurations by selectively attending to appropriate segments of the input.
Data Source
AI summary
A computing system trains a reinforcement learning model comprising multiple different attention model components. The reinforcement learning model trains on training data of a first environment (e.g., a first traffic intersection). The reinforcement learning model trains by training a state attention computer model on the training data that weighs each of respective inputs of a respective state. The reinforcement learning model trains by training an action attention computer model that determines a probability of switching from a first action to a second action of the first set of the multiple candidate actions (e.g., changing traffic colors of traffic lights).Alternatively, or additionally, a computing system generates an indication of a selected outcome according to the reinforcement learning model and sends a selection output to the second environment (e.g., a second traffic intersection with more lanes than the first traffic intersection) to implement the selected action in the second environment.