thenomaniqbal/Traffic-flow-prediction
Long Short-Term Memory(LSTM) is a particular type of Recurrent Neural Network(RNN) that can retain important information over time using memory cells. This project includes understanding and implementing LSTM for traffic flow prediction along with the introduction of traffic flow prediction, Literature review, methodology, etc.
This project helps traffic managers and urban planners predict traffic volumes two hours into the future based on six hours of historical traffic and weather data. It takes in various traffic data like vehicle counts, speeds, and types, along with road network information and weather conditions, to output accurate forecasts of future traffic flow. This tool is designed for professionals managing transportation systems in urban environments.
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Use this if you need to proactively manage urban traffic congestion and optimize transportation efficiency by predicting future traffic patterns.
Not ideal if you require real-time guidance for immediate navigation, as this focuses on predicting future conditions rather than current ones.
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Jan 22, 2025
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