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Code-Blooded · MTA Datathon 2025

This repository contains our project submission for the MTA Datathon 2025, where we analyzed Automated Camera Enforcement (ACE) bus-lane violations in relation to the launch of congestion pricing in Manhattan’s Central Business District (CBD).

We built a data analysis pipeline (Python: pandas, numpy, matplotlib) and a presentation website (Next.js, TypeScript) to communicate our findings.

🎥 Project Video Overview

Watch our project video overview below: https://www.youtube.com/watch?v=x-W0gc3OESs

Project website

https://mhc-datathon.github.io/Code-Blooded/

📊 Project Overview

Challenge Question:
“Some automated camera-enforced routes travel within or cross Manhattan’s Central Business District. How have violations on these routes changed alongside the implementation of congestion pricing?”

Key Findings:

  • Violations increased overall: Average monthly ACE violations rose sharply after congestion pricing began in January 2025.
  • Camera rollout effect: Much of this rise is explained by the phased installation of new enforcement cameras across routes in 2025.
  • Shift in violation types:
    • Bus lane violations decreased by 71% (showing cameras work for lanes).
    • Bus stop violations increased by 61%.
    • Double-parked violations rose by 52%.
  • Route-level results:
    • CBD-only routes (M34+, M42) saw a 35.7% decrease.
    • Some Partial-CBD routes (M2, M4, M101, M15+) increased—but the effect is confounded by late camera installations.

📄 Read the full draft report: Datathon Research Paper (PDF) 🌐 Explore visuals and analysis on the deployed website.

🛠 Tech Stack

  • Frontend Website: Next.js, TypeScript, Tailwind CSS
  • Data Analysis: Python (pandas, numpy, matplotlib)
  • Collaboration: GitHub (issues, commits, version control)

🚀 Getting Started

Clone the repository:

```bash
git clone https://github.com/MHC-Datathon/Code-Blooded.git
cd Code-Blooded

📂 Repository Structure

├── backend/ # Python analysis pipeline
│ ├── cleaning.py # Cleans raw violations data
│ ├── analysis.py # Aggregates + generates figures
│ └── data/ # Input/output CSV files
│
├── frontend/ # Next.js website (presentation)
│ ├── pages/ # Website pages
│ ├── components/ # Reusable UI components
│ └── public/ # Static assets (charts, visuals)
│
├── docs/ # Draft report + supporting materials
└── README.md

🔄 Reproducibility

Our workflow can be replicated in two steps:

  1. Data Cleaning: 
    python backend/cleaning.py
  2. Analysis & Figures 
    python backend/analysis.py

📢 Team

Team Code-Blooded – Maruf Azad, Aabid Dewan, Farjan Halim, and Nahin Khan in the MTA Datathon 2025.

📜 License

This project is open-source and available under the MIT License.