Traffic Light Speed Guidance Using GPS and Adaptive Signal Timing
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Solution Overview
Problem
Existing technologies for controlling vehicle speed at traffic lights are limited by high installation and maintenance costs of smart traffic lights, reliance on accurate and up-to-date vehicle-to-vehicle communication, and inability to adapt to changing traffic conditions in real-time.
Innovation Solution
A method and system that uses GPS location, real-time traffic light status, and machine learning techniques to recommend a cruising speed to vehicles, allowing them to traverse routes without stopping at traffic lights, while being cost-effective and adaptable to changing traffic patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If smart traffic lights with sensors are installed to detect vehicles and adjust timing, then traffic flow is optimized and wait time is reduced, but installation and maintenance costs increase significantly
Solution Approach 1:
The patent uses GPS location data and machine learning models to create a virtual representation of traffic light status and vehicle positions, eliminating the need for physical sensors at each intersection. The system copies relevant traffic information through wireless communication between vehicles and the central server, providing smart traffic light functionality without expensive infrastructure installation.
Solution Approach 2:
The patent replaces the mechanical sensor-based detection system with an electronic information processing system. Instead of using physical sensors to detect vehicles and trigger light changes, the system uses GPS tracking, wireless communication, and machine learning algorithms to predict optimal vehicle speeds and coordinate traffic light timing, substituting electronic computation for mechanical detection.
2Productivity
If vehicle-to-vehicle communication is used to provide real-time traffic information, then vehicles can adjust speed to minimize waiting time, but the system becomes unreliable when communication is inaccurate or outdated
Solution Approach 1:
The patent implements a feedback mechanism where vehicles continuously transmit their position and speed data to the central server, which processes this information and sends back optimized speed recommendations. The system constantly updates traffic light status predictions based on real-time vehicle positions and adjusts speed recommendations accordingly, creating a closed-loop control system that maintains reliability through continuous information exchange and validation.
3Ease of operation
If pre-programmed timers are used to control traffic lights, then the system is simple to operate, but significant wait times occur especially during heavy traffic periods
Solution Approach 1:
The patent transforms the static, fixed timing system into a dynamic, adaptive system. Instead of predetermined timer schedules, the system continuously calculates optimal traffic light timing based on real-time vehicle positions, speeds, and predicted arrival times. The machine learning model dynamically adjusts speed recommendations and traffic light coordination to match actual traffic conditions, making the system as simple to operate as timers but as adaptive as complex sensor systems.
Data Source
AI summary
The present invention relates to method and system for controlling speed of vehicle by using GPS location, real-time traffic light status and machine learning techniques to recommend cruising speed to vehicle without stopping at the traffic lights. The method includes determining status of traffic lights located on route of traffic junctions and associated time period for status of traffic lights based on data received from sensors. The method includes broadcasting status of traffic lights and associated time period. The method includes calculating in real-time a recommended cruising speed of the vehicle based on GPS location associated with vehicle, status of traffic lights and associated time period, and a time period required by vehicle to arrive at the traffic lights using machine learning techniques. The method includes providing in real-time the recommended cruising speed to vehicle. The method includes adaptively controlling in real-time speed of vehicle to recommended cruising speed.