MTA Ridership Project

Team Members

• Haixin Liu
• Hanghai Li

Project Overview

This project analyzes MTA daily ridership trends in New York City to understand how different transit services have recovered since COVID-19. Our Streamlit dashboard compares subway, bus, LIRR, and Metro-North ridership over time and uses NYC COVID-19 case data as additional context.

Research Questions

1. How have different MTA services recovered since COVID-19?
2. How do weekday and weekend ridership patterns differ?
3. How do changes in COVID-19 cases relate to changes in transit ridership?

Data Sources

• MTA Daily Ridership Data
  https://data.ny.gov/Transportation/MTA-Daily-Ridership-Data-Beginning-2020/vxuj-8kew

• NYC COVID-19 Daily Cases
  https://data.cityofnewyork.us/Health/Coronavirus-Data/rc75-m7u3

Both datasets are loaded into BigQuery for the Streamlit app:

• sipa-adv-c-bouncing-penguin.mta_data.daily_ridership
• sipa-adv-c-bouncing-penguin.mta_data.nyc_covid_cases

Repository Structure

• streamlit_app.py - homepage and project introduction
• pages/1_MTA_Ridership.py - main MTA ridership analysis
• pages/2_Second_Dataset.py - NYC COVID-19 context page
• utils.py - helper functions for cleaning and plotting
• validation.py - Pandera schema validation
• tests/ - unit tests for utility and validation code
• load_data_to_bq.py - script for loading both datasets into BigQuery
• LAB_10_WRITEUP.md - Lab 10 notes on data loading and performance

Setup

1. Clone this repository: git clone https://github.com/advanced-computing/bouncing-penguin.git
2. Create virtual environment: python -m venv .venv
3. Activate virtual environment:
   • Mac/Linux: source .venv/bin/activate
   • Windows: .venv\Scripts\activate
4. Install dependencies: pip install -r requirements.txt
5. Load the BigQuery tables: python load_data_to_bq.py --dataset all

Usage

Run the Streamlit app locally:

streamlit run streamlit_app.py

You can still open mta_ridership_project.ipynb in Jupyter Notebook or VS Code for notebook-based exploration.

Lab 10

Lab 10 documentation lives in LAB_10_WRITEUP.md.