Traffic Light Cruising Speed Control Using GPS and Signal Status
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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 and reliance on vehicle-to-vehicle communication for real-time traffic information, which may not always be accurate or up-to-date.
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 conditions.
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 and processes digital data from existing GPS sources rather than installing new hardware infrastructure.
Solution Approach 2:
The patent replaces the mechanical sensor-based detection system with a software-based machine learning model that processes GPS data. Instead of using physical sensors to detect vehicle presence, the system uses computational algorithms to predict traffic light status and optimize vehicle speed based on digital data inputs.
2Loss of time
If vehicle-to-vehicle communication is used to share real-time traffic information, then waiting time at traffic lights is reduced, but information accuracy and timeliness deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model continuously receives GPS location data from vehicles, processes this information, and generates speed recommendations that are fed back to vehicles. This closed-loop system ensures information remains current and accurate by constantly updating based on real-time vehicle positions and predicted traffic light status.
Solution Approach 2:
The patent introduces a central server with machine learning algorithms as an intermediary that processes and validates traffic information before distributing it to vehicles. This intermediary layer filters and verifies data from multiple GPS sources, ensuring the accuracy and reliability of the traffic light status information shared among vehicles.
3Device complexity
If vehicles stop and wait at traffic lights, then traffic flow control is simplified, but fuel consumption increases and environmental pollution worsens
Solution Approach 1:
The patent uses machine learning models to predict future traffic light status based on current GPS data and historical patterns. This preliminary action allows the system to recommend optimal speeds that will position vehicles to pass through intersections during green phases, preventing the need for stops before the lights even change.
Solution Approach 2:
The patent implements dynamic speed recommendations that continuously adjust based on real-time GPS location data and predicted traffic light status. Rather than using fixed speed limits or static timing patterns, the system dynamically optimizes vehicle speed profiles to match changing traffic conditions, allowing vehicles to maintain motion and avoid stops.
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.