Traffic Light Tracking With GPS Speed Guidance
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Solution Overview
Problem
Existing traffic light control systems are expensive to install and maintain, and they may not provide real-time, accurate information about traffic light status, leading to increased wait times and reduced traffic flow efficiency.
Innovation Solution
A system using GPS location, real-time traffic light status, and machine learning techniques to recommend cruising speed to vehicles, enabling them to traverse routes without stopping at traffic lights, by integrating sensors, a central server, and communication networks for data exchange and adaptive speed control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If smart traffic lights with sensors are installed to detect vehicles and adjust timing, then traffic flow efficiency is improved, but installation and maintenance costs increase
Solution Approach 1:
The patent introduces a central server as an intermediary that coordinates between vehicles and traffic lights. The server receives vehicle location data, determines optimal speeds to pass through intersections during green phases, and communicates this information back to vehicles. This mediator approach improves traffic flow efficiency without requiring expensive sensor installations at every traffic light, as the server processes data centrally and generates speed recommendations for vehicles.
2Loss of time
If vehicle-to-vehicle communication is used to provide real-time traffic information, then waiting time at traffic lights is reduced, but system complexity increases
Solution Approach 1:
The patent makes the central server multi-functional by having it perform multiple tasks: collecting vehicle location data, determining optimal speeds for passing intersections, managing traffic light coordination, and providing recommendations to vehicles. This universal approach reduces waiting time at traffic lights through centralized intelligence rather than requiring complex peer-to-peer vehicle communication systems, thereby reducing overall system complexity while achieving the time savings benefit.
3Device complexity
If pre-programmed timers are used to control traffic lights, then system simplicity is maintained, but wait times increase significantly
Solution Approach 1:
The patent implements a feedback mechanism where the central server continuously receives real-time vehicle location data from GPS systems, processes this information to determine current traffic conditions, calculates optimal speeds for vehicles to pass through intersections during green phases, and communicates these recommendations back to vehicles. This feedback loop enables dynamic traffic light coordination that significantly reduces wait times while maintaining relative system simplicity through centralized processing rather than complex distributed control.
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.