Inspiration
Alongside its 2022 Chicago Climate Action Plan (CCAP), the Chicago government outlined strategies to reduce the city's greenhouse gas emissions by increasing CTA ridership by 20% by 2030. In 2024, the Chicago government affirmed its commitment this plan, announcing its $5.7 Billion 5.5-mile Red Line Extension (RLE) project that will commence in late 2025 and include four new fully-accessible stations near 103rd street, 111th street, Michigan Avenue, and 130th Street.
Our project looks to aid the government with decisions–constructing and destructing stations to maximize cost-efficiency and increase CTA ridership–with our data visualization that maps bus, train, and taxi ridership data across different stops, stations, and areas respectively.
What it does
We retrieved bus, taxi, and train data from the Chicago Data Portal and created an interactive Folium map that plots all bus stops, train stations, taxi pickups, and taxi drop-offs as circle markers with color gradients to show how busy each point is. We also created a visualization showing the bottom 10% of bus and train stations in utilization to provide public policy and transportation infrastructure makers with insights on how to better optimize the Chicago public transport system.
How we built it
We built our project by combining multiple publicly available datasets from the City of Chicago, including bus boarding data, taxi pickup and dropoff data, and 'L' train station ridership. To access the latest data, we integrated with API endpoints and locally saved CSV files. After cleaning and normalizing all datasets to daily usage rates, we visualized the results using Folium, an interactive Python mapping library. Each transportation mode was layered onto a single interactive map with color-coded markers and selectable layers. We also overlaid official CTA bus and train routes from KMZ files to provide real-world context. Finally, we packaged everything into a standalone HTML file that anyone can open and explore without needing any specialized software.
Challenges we ran into
Due to the limited time constraints we struggled to quickly identify a problem that we could find data and realistically implement an innovative solution for. We especially struggled to find specific ridership data for each bus stop in Chicago. In addition, because our datasets were very large and the limited time to process all the data, we had to decide on a reasonable sample size that'd allow us to build our visualization on.
Accomplishments that we're proud of
We were able to create a polished visualization within the given time constraints.
What we learned
We learned the inefficiencies that exist in the current Chicago public transit system and the roots of residents' dissatisfaction.
What's next for Ridership
Making the visualization more adaptive to current data, being able to call from the public transit APIs and handle larger datasets; include other forms of transportation data such as overhead and delay times to drive additional insights for the government.
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