SPaT Prediction Using Machine Learning for Dynamic Traffic Signals
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Solution Overview
Problem
Conventional systems for predicting traffic signal phase and timing (SPaT) are limited in their ability to accurately predict SPaT for dynamically actuated traffic lights, which are influenced by real-time traffic conditions.
Innovation Solution
A SPaT prediction system that utilizes a trained machine module, trained using historical traffic signal information and a supervised machine learning algorithm, to predict SPaT for future traffic signal cycles based on both historical and real-time traffic signal data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional SPaT prediction systems are used, then the system complexity is low, but the prediction accuracy for dynamically actuated traffic lights deteriorates
Solution Approach 1:
The patent replaces conventional mechanical/scheduled traffic light control systems with an intelligent system that uses machine learning algorithms (specifically Long Short-Term Memory networks) to predict SPaT. This substitution enables the system to handle dynamic traffic conditions adaptively, achieving higher prediction accuracy for dynamically actuated traffic lights while managing system complexity through software-based intelligence rather than hardware complexity
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring actual traffic conditions and comparing them with predicted SPaT. The machine learning model uses historical traffic data and real-time observations to refine its predictions, creating a closed-loop system that improves accuracy over time. This feedback approach allows the system to adapt to changing traffic patterns and maintain high prediction accuracy despite increasing system complexity
2Loss of energy
If vehicles wait at traffic lights during red or yellow phases, then traffic safety is maintained, but fuel consumption and energy waste increase
Solution Approach 1:
The system enables preliminary action by providing advance notification of upcoming green phases through accurate SPaT prediction. Vehicles can begin preparing to move (reducing engine idling, positioning drivers) before the light actually turns green, thereby reducing the energy wasted during the red/yellow wait period while maintaining safety through predictable, planned maneuvers rather than abrupt stops and starts
Solution Approach 2:
The system promotes self-service by enabling vehicles to autonomously optimize their own operation based on predicted SPaT information. Drivers or autonomous systems can independently decide the optimal timing for approaching and passing traffic lights, managing their own energy consumption without requiring external intervention or sacrificing safety, as the prediction system provides the necessary information for informed decision-making
3Adaptability or versatility
If traffic lights operate on fixed schedules, then the control system is simple, but the adaptability to real-time traffic conditions deteriorates
Solution Approach 1:
The patent implements dynamics by transitioning from static, fixed-schedule traffic light control to a dynamic system that continuously adapts to real-time traffic conditions. The machine learning model processes incoming traffic data and adjusts SPaT predictions accordingly, allowing the system to respond flexibly to changing patterns. This dynamic approach enhances adaptability while managing complexity through algorithmic intelligence that learns from historical data
Solution Approach 2:
The system utilizes parameter changes by continuously updating its prediction models with new traffic data, allowing the underlying parameters of the control system to evolve and adapt to changing traffic patterns. The machine learning algorithm adjusts its internal parameters based on historical and real-time data, enabling the system to adapt to new traffic conditions without requiring complete system redesign, thus balancing adaptability with manageable complexity
Data Source
AI summary
A traffic signal phase and timing (SPaT) prediction system is disclosed. The system may include a transceiver configured to receive historical traffic signal information associated with a traffic light and real-time traffic signal information. The system may further include a memory configured to store a training data and a trained machine model. The trained machine model may be trained using the training data that includes the historical traffic signal information. The system may further include a processor configured to execute instructions stored in the trained machine model to predict traffic signal information (e.g., SPaT) associated with a future traffic signal cycle based on the real-time traffic signal information. The processor may further output the traffic signal information associated with the future traffic signal cycle to a vehicle.