The Datasets

MBTA Datasets

Our first MBTA dataset is titled MBTA_Monthly_Ridership_By_Mode_and_Line. This dataset tracks MBTA ridership across all transit modes and lines on a monthly basis. Each record includes the month of service, the type of day (weekday or weekend), the route or line name, and both total and average ridership figures for that period. The data spans multiple years and covers all major MBTA services including subway lines (Red, Orange, Blue, Green, Silver), bus routes, commuter rail, and ferry services. This granular breakdown allows for detailed analysis of ridership patterns across different transit modes, seasonal trends, and the impact of major events like the COVID-19 pandemic. We got this data from MBTA Data portal and can be found here: MBTA Monthly Ridership Dataset

Our second MBTA dataset is titled Rail_Ridership_by_Season_Time_Period_RouteLine_and_Stop. This dataset provides a more detailed view of rail ridership by breaking down passenger activity at individual stations. Each record includes the season, route name, direction of travel, day type (weekday or weekend), time period (such as morning peak, midday, or evening), and the specific stop where passengers board or exit. The dataset tracks total and average boardings (ons), exits (offs), and overall flow for each station, allowing for precise analysis of how ridership patterns vary throughout the day and across different times of the year. This level of detail is especially useful for identifying the busiest stations, understanding commuter behavior during peak hours, and analyzing how seasonal changes affect specific stops on the MBTA rail network. We got this data from MBTA Data portal and can be found here: MBTA Rail Ridership by Stop Dataset

Our third MBTA dataset is titled Gated_Station_Entries and includes data from 2019, 2020, and 2021. This dataset focuses specifically on subway stations with fare gates, tracking the number of entries at each station throughout the day. Each record includes the service date, time period (recorded in 30-minute intervals), station name, route or line, and the number of gated entries during that time window. With over three million rows of data across the three years, this dataset provides an incredibly detailed view of how ridership flows through individual stations at different times of day. This granular, time-stamped data is essential for identifying the busiest stations, analyzing peak travel times, and understanding how ridership patterns changed dramatically during the COVID-19 pandemic. The ability to track entries by specific time periods makes this dataset particularly valuable for studying commuter behavior and the impact of major events on station-level traffic. We got this data from MBTA Data portal and can be found here: MBTA Gated Station Entries Dataset

Red Sox Data

Our fifth dataset is titled Red Sox Data and contains game schedule information for the 2019 Boston Red Sox season. This dataset includes 163 games with details such as the game date, opponent, game time, whether the game was played during the day or night (D/N), and attendance figures. Most importantly for our analysis, the dataset indicates whether each game was played at home or away, allowing us to identify when Fenway Park hosted games and attracted large crowds to the Kenmore area. This data is crucial for analyzing the impact of major sporting events on local MBTA ridership, particularly at Kenmore Station, which is the closest subway stop to Fenway Park. By comparing game dates with ridership data, we can observe clear spikes in station entries on days with home games, demonstrating how special events drive transit usage patterns in Boston. We got this data from Baseball Reference and can be found here: 2019 Red Sox Schedule

Weather Data

Our fourth dataset is titled NOAABostonWeather and provides comprehensive monthly weather data for the Boston area. This dataset comes from the National Oceanic and Atmospheric Administration (NOAA) and includes 88 months of weather observations with 29 different weather metrics. Key variables include average temperature (TAVG), total precipitation (PRCP), snowfall (SNOW), maximum and minimum temperatures (TMAX, TMIN), and various temperature threshold indicators such as days below freezing (DT32) or above 90°F (DX90). The dataset also tracks wind speed, heating and cooling degree days, and other atmospheric conditions. This weather data is essential for analyzing how environmental factors influence MBTA ridership patterns— for example, whether extreme cold, heavy precipitation, or snowfall correlates with changes in transit usage. By combining weather data with ridership data, we can better understand how Bostonians adapt their travel behavior in response to different weather conditions throughout the year. We got this data from NOAA's Climate Data Online portal and can be found here: NOAA Climate Data Online