Fixes #672 LSTM-based Traffic Demand Forecasting #674
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@Varunshiyam add readme file with clear description. |
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🎉🎉 Thank you for your contribution! Your PR #674 has been merged! 🎉🎉 |
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Related Issues or bug
Accurately forecasting traffic demand at specific locations and times is crucial for efficient transportation, but challenging due to complex spatiotemporal patterns and limited data. This project aims to overcome these challenges using a novel deep learning approach.
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Under labels:
Fixes: #672
Proposed Changes
This project tackles traffic demand forecasting using a unique two-step deep learning approach. First, it employs Long Short-Term Memory (LSTM) networks to predict aggregated demand for different geographic clusters. Then, it uses linear regression to distribute this predicted demand across individual locations within each cluster. This method allows for accurate and granular forecasting, even with limited data. The project also includes extensive feature engineering and exploratory data analysis to understand spatiotemporal demand patterns.
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