The New York City subway system is one of the busiest in the world, with over 5 million riders per day. However, it is also one of the most congested, with delays and overcrowding a common occurrence. This is due in part to the difficulty of predicting periods of increased ridership, which can lead to overcrowding and delays.
We propose to develop a system that combines the MTA dataset, geographical patterns of temperature, different card types stacking at each station, and traffic footfall to predict periods of increased ridership. This system will use machine learning to identify patterns in the data that can be used to forecast future ridership levels.
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- By predicting periods of increased ridership, the MTA can run more trains at those times to help prevent overcrowding.
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- By understanding how ridership patterns vary over time and space, the MTA can improve its service planning and delivery (effective labor resources)
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- By reducing congestion and improving service, our system will make the subway a more pleasant and reliable experience for riders. One trip at a time.
- Time Series Prediction
- Pandas
- Seaborn
- Numpy
- Python
- HTML
- CSS
- JavaScript
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Shikhar Johri
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Shivam Shekhar
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Mohsin Chougale
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