Nashville Public Transit Analysis

Public transit efficiency isn’t just about running buses—it’s about running them on time. In this project, I analyzed bus stop performance across Nashville using WeGo’s GPS-tracked adherence data. The result is an interactive map that visualizes which stops consistently serve riders on schedule and which are prone to delays.

🔸 Data Source:

The dataset includes GPS-based stop-level logs from WeGo Public Transit, collected between August 1 and September 30, 2023. Each entry includes adherence (difference between actual and scheduled departure), adjusted early/late/on-time flags, stop location, operator ID, route, and trip metadata. The data for this project can be downloaded from here.

• Adherence (minutes): Positive = early, Negative = late.

• Lat/Long: Used for geographic stop clustering.

🔸 Methodology:

1. Data Cleaning:

   • Dropped null or irrelevant adherence rows.

   • Rounded lat/long values to group duplicate GPS locations.

   • Aggregated data at each unique bus stop.

2. On-Time Performance Calculation:

   • For each stop, I summed:

     • ADJUSTED_ONTIME_COUNT

     • ADJUSTED_LATE_COUNT

     • ADJUSTED_EARLY_COUNT

   • Calculated the percentage of on-time departures.

3. Color Thresholds:

   • 🟢 Green: > 85% on time

   • 🟠 Orange: 70–85% on time

   • 🔴 Red: < 70% on time

4. Mapping:

   • Built using Python’s Folium library.

   • Stops are plotted as color-coded circles with tooltips showing full stop performance.

   • A custom HTML legend was added for clarity.

🔸 Why It Matters:

This visualization makes transit data accessible—not just for data scientists, but for riders, planners, and community advocates. It reveals not just where buses go, but how well they perform.

🔸 Tools Used:

• Python (Pandas, Folium, Matplotlib)

• Jupyter Notebook

• GitHub Pages (for web publishing)