atharva-hukkeri/Traffic-Prediction-using-Machine-Learning
The project aims to develop models that can forecast traffic congestion, aiding in effective traffic management and planning.
This project helps urban planners and traffic managers forecast congestion by analyzing historical traffic patterns, day of the week, weather conditions, and temperature for specific zones. It takes in structured data about past traffic levels and environmental factors to predict future traffic volume on a five-level scale, ranging from 'less than 5 cars' to 'more than 50 cars'.
No commits in the last 6 months.
Use this if you need to predict traffic congestion levels for different urban zones based on historical data, day of the week, and local weather conditions to inform resource allocation or urban planning.
Not ideal if you require real-time traffic predictions or need to account for dynamic events like accidents, road closures, or special events not captured in historical weather and day-of-week data.
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Aug 20, 2022
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