Objective: The main objective of this project is to develop a system that can predict traffic patterns and dynamically adjust traffic signals to improve the flow of vehicles on the road. This will not only reduce the travel time for commuters but also reduce the carbon footprint by reducing the idling time of vehicles.
Methods:
We will use a combination of computer vision and machine learning techniques to analyze traffic video feeds in real-time. We will use data from these feeds to predict traffic patterns and adjust traffic signals accordingly. We will also use GPS data from vehicles to detect congestion and redirect traffic to less congested routes. Technologies:
Python and Tensorflow for machine learning and computer vision OpenCV for image processing Google Maps API for traffic data and routing Node-RED for integrating different parts of the system
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