Traffic Signal Transfer Learning for Varied Intersection Structures
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Solution Overview
Problem
Existing traffic signal control methods face challenges in dynamically reflecting changing traffic conditions due to low accuracy in simulation models and time-consuming learning, especially when applying deep reinforcement learning, and transfer learning from image processing fields is hindered by the difficulty in determining similar pre-learning models due to varied road structures and specifications.
Innovation Solution
A method and device for converting dynamic traffic information into a common format, extracting static characteristics, and outputting optimized traffic signals using a neural network model that can adapt to different road environments, enabling automatic selection of an optimal pre-learning model for traffic signal optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep reinforcement learning is applied to traffic signal control to dynamically reflect changing traffic conditions, then the adaptability to traffic conditions is improved, but the learning time becomes excessively long
Solution Approach 1:
The patent applies transfer learning by pre-training a neural network model on source traffic data before deploying it to the target environment. This preliminary action on source data allows the model to acquire general traffic control knowledge in advance, significantly reducing the learning time required when deployed to new intersections with different road structures and traffic patterns.
2Productivity
If transfer learning from image processing fields is applied to traffic signal optimization, then the learning efficiency is improved, but it becomes difficult to determine suitable pre-learning models due to varied road structures and specifications
Solution Approach 1:
The patent designs a universal neural network model architecture that can handle multiple types of intersections with different road structures, numbers of lanes, and traffic configurations. The model is trained on diverse source data encompassing various intersection types, enabling it to adapt to target environments with different physical specifications without requiring structure-specific customization, thus achieving both high learning efficiency and broad compatibility.
Solution Approach 2:
The patent employs parameter adaptation techniques where the pre-trained model's parameters are fine-tuned based on the specific characteristics of the target intersection. By adjusting model parameters according to the target environment's road structure, number of lanes, and traffic patterns, the system maintains high learning efficiency from transfer learning while adapting to the specific physical specifications of different intersections.
3Device complexity
If a fixed-time control method based on statistical data is used for traffic signal control, then the system complexity is reduced, but the ability to dynamically reflect changing traffic conditions deteriorates
Solution Approach 1:
The patent replaces the mechanical fixed-time control system with an intelligent neural network-based system. Instead of relying on pre-programmed timing schedules based on historical statistics, the system uses a trained neural network that processes real-time traffic data and dynamically determines optimal signal timings, enabling the system to adapt to changing traffic conditions while maintaining manageable complexity through the use of transfer learning.
Data Source
AI summary
A method and device for a transfer learning for traffic signal optimization are provided. The method may include converting first dynamic traffic information extracted based on a first type of state information corresponding to a first intersection type, and second dynamic traffic information extracted based on a second type of state information corresponding to a second intersection type into a common format; extracting at least one static characteristic information corresponding to at least one of the first intersection type or the second intersection type based on context information; based on the first and second dynamic traffic information and the at least one static characteristic information, outputting a first and second type of action information for an optimized traffic signal for the first and second intersection type, respectively.