MTA Subway Origin-Destination Ridership Estimate for 2023.
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Updated
Aug 14, 2024 - Jupyter Notebook
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subway-ridership
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A comprehensive data analysis of the Buenos Aires subway system ridership using Python and Pandas. This project identifies peak-hour congestion patterns, explores hourly passenger distributions, and utilizes the 95th percentile to isolate extreme traffic conditions for urban mobility insights.
python pandas data-visualization statistical-analysis data-analysis matplotlib data-science-portfolio transit-data buenos-aires urban-mobility subway-ridership 95th-percentile
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Mar 13, 2026 - Jupyter Notebook
Research code and reproducibility materials for a mobility-informed SIR model using subway ridership data, particle smoothing, and mobility-reduction scenarios to evaluate influenza transmission and the instantaneous reproduction number.
reproducible-research public-health influenza mobility sir-model particle-smoother negative-binomial epidemic-modeling subway-ridership instantaneous-reproduction-number
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Jul 24, 2026 - Jupyter Notebook
EDA of the effects Major Incidents had on total citywide MTA Subway Ridership from 2020 to 2024
exploratory-data-analysis transportation jupyter-notebook eda mta new-york-city public-transport nyc-opendata 2020-2024 subway-ridership mta-subway
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Updated
Apr 4, 2025 - Jupyter Notebook
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