Traffic Light Control via Predicted Vehicle Flow Patterns
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Solution Overview
Problem
Traffic congestion in major cities leads to significant time loss, increased greenhouse gas emissions, and negative health and mental implications due to prolonged commuting times, as existing traffic control methods are inefficient in predicting and managing traffic flow.
Innovation Solution
A computer-implemented method and system using machine learning models to predict traffic patterns based on image sequences from imaging sensors, allowing for optimized control of traffic lights to improve traffic flow and reduce congestion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional traffic control methods are used, then traffic flow can be managed with simple control systems, but traffic congestion increases and commuting time is extended
Solution Approach 1:
The system performs preliminary action by predicting future traffic patterns before they fully develop. The machine learning models analyze current and historical traffic data to forecast upcoming congestion and adjust traffic light control proactively, preventing traffic jams before they occur rather than reacting after congestion has formed.
Solution Approach 2:
The system implements continuous feedback loops where traffic sensor data is constantly collected, analyzed by machine learning models, and used to dynamically adjust traffic light timing. This closed-loop control system monitors traffic flow outcomes and uses that information to optimize future control decisions, progressively improving traffic flow efficiency.
2Productivity
If machine learning models are applied to predict traffic patterns, then traffic flow can be optimized and congestion reduced, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components between traffic sensors and traffic light controllers. These models process raw sensor data and translate it into predictive traffic patterns that inform control decisions, acting as a intelligent mediator that bridges data collection and actuation while managing the complexity of pattern recognition and prediction.
Solution Approach 2:
The system replaces traditional mechanical or rule-based traffic control mechanisms with data-driven machine learning models. Instead of using fixed timing schedules or simple sensor-triggered responses, the system employs trained neural networks and predictive algorithms that automatically learn optimal control strategies from historical data, substituting complex computational processing for simpler physical control logic.
Data Source
AI summary
Disclosed herein are systems and methods for controlling traffic lights according to predicted traffic patterns, comprising receiving one or more image sequence comprising a plurality of images captured by one or more imaging sensor deployed to monitor vehicle traffic in one or more intersection in which traffic light(s) is deployed to control traffic flow, generating a traffic dataset descriptive of time series movement of all vehicles tracked in the image sequence(s), applying a first trained machine learning model to map, based on the traffic dataset, a traffic pattern of the tracked vehicles to one or more of a plurality of learned traffic patterns, applying a second trained machine learning model to predict one or more subsequent traffic patterns based on the mapped traffic pattern, and generating instructions for controlling the traffic light(s) according to the predicted subsequent traffic pattern(s).