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Visualizing the T: MBTA Rider Satisfaction Score

About

An interactive analytics platform that computes a comprehensive Rider Satisfaction Score (RSS) for the Massachusetts Bay Transportation Authority (MBTA) by combining operational performance metrics with passenger survey data.

Team Members: Dhanrithii Deepa, Eric Fu, Yuchen Cai
Live Demo: MBTA RSS Streamlit App

Project Goal

Develop a data-driven Rider Satisfaction Score that:

  • Reflects both perceived experience (survey data) and operational performance (travel times, restrictions, crowding)
  • Enables equity analysis across demographic groups and service areas
  • Provides actionable insights at station/line/time-period granularity for MBTA service planning

How to Run

Prerequisites

  • Python 3.9+
  • pip or conda

Installation

# Clone repository
git clone https://github.com/urverse/Visualizing-the-T.git
cd Visualizing-the-T

# Install dependencies
pip install -r requirements.txt

Run Analysis Notebooks

# Start Jupyter
jupyter notebook

# Run notebooks in order:
# 1. notebooks/01_data_exploration.ipynb
# 2. notebooks/02_indicator_calculation.ipynb
# 3. notebooks/03_rss_computation.ipynb
# 4. notebooks/04_advanced_analytics.ipynb
# 5. notebooks/05_statistical_validation.ipynb

Run Streamlit Dashboard

streamlit run app/streamlit_app.py

Methodology

Data Sources

  1. MBTA 2024 Passenger Survey - Rider satisfaction ratings and demographics
  2. Rapid Transit Travel Times - Operational performance metrics
  3. Fall 2024 SDP Ridership - Exposure weights by station/line/time
  4. Speed Restrictions by Day - Service disruptions (slow zones)

Approach

1. Indicator Calculation

  • Travel time reliability (P90/median ratio)
  • Travel time volatility (coefficient of variation)
  • Speed restriction coverage (% of line restricted)
  • Crowding levels (ridership/capacity ratio)
  • Actual survey satisfaction scores

2. RSS Computation

  • Literature-based weights applied to normalized indicators
  • Ridge regression validates weights against survey satisfaction
  • Exposure-weighted aggregation using ridership data

3. Statistical Validation

  • ANOVA hypothesis testing for line comparisons
  • Bootstrap confidence intervals (95% CI)
  • Equity analysis across income/race demographics

Key Findings

  • Orange Line achieved highest RSS (73.2) despite serving predominantly low-income riders
  • Methodology is equity-neutral - no systematic bias against underserved communities
  • Reliability is strongest satisfaction driver (β = 0.42, p < 0.001)
  • Statistical validation confirms robust model performance (R² = 0.99)

Results & Outputs

Generated Artifacts

  • data/final/rss_scores.csv - RSS by station/line/time period
  • reports/figures/ - Visualizations (heatmaps, time series, equity charts)
  • reports/presentation.pdf - Final academic presentation

Dashboard Features

  • Interactive line/station filtering
  • Time-period comparisons
  • Equity analysis breakouts
  • Indicator contribution charts

Repository Structure

Visualizing-the-T/
├── data/
│   ├── raw/              # Original MBTA datasets
│   ├── processed/        # Cleaned data
│   └── final/            # RSS scores
├── notebooks/            # Analysis notebooks (01-05)
├── src/                  # Python modules
├── app/                  # Streamlit dashboard
├── reports/              # Figures and presentation
├── config/               # Weights configuration
└── requirements.txt

Dependencies

  • Data Processing: pandas, pyspark, numpy
  • Modeling: scikit-learn, scipy, statsmodels
  • Visualization: matplotlib, seaborn, plotly
  • Dashboard: streamlit
  • Configuration: pyyaml

See requirements.txt for full list with versions.

Team Contributions

  • Eric Fu: Data engineering, PySpark ETL pipelines, infrastructure setup
  • Yuchen Cai: Feature engineering, Streamlit app development, dashboard design
  • Dhanrithii Deepa: Statistical validation, equity analysis, hypothesis testing

License

MIT License - see LICENSE file for details

Acknowledgments

Data provided by MBTA Open Data Portal