MHC++ x MTA Datathon 2025 — DataStorm

Team

• Arsenii
• Diana
• Bekbol
• Lin

Datasets Used

• MTA Bus Automated Camera Enforcement (ACE) Violations (data.ny.gov)
• MTA Bus Route Segment Speeds (2023–2024) (data.ny.gov)
• Additional CUNY-circulated resource (link)

Questions Answered

1. MTA bus routes used by CUNY students: how have camera‑enforced speeds changed over time?
   • Compare cross‑campus routes; compare ACE vs non‑ACE on campus routes
2. Which vehicles are exempt from fines, are they repeated, and which vehicles frequently violate?
   • Visualize exemption breakdown and repeat violators
3. How have violations on routes traveling in the Manhattan Central Business District changed with congestion pricing?
   • Compare before vs after pricing; map/visual comparisons

Video Summary

• DataStorm submission overview (link)

Quick Start

• Place input CSVs under data/ (flat folder). For large files, prefer Git LFS or download at runtime.
• Run each analysis from the repo root:
  • Q1 (Speeds):

    python question_1/q1_analysis.py

  • Q2 (Exemptions & Repeat Violators):

    python question_2/q2_analysis.py

  • Q3 (CBD & Congestion Pricing):

    python question_3/q3_analysis.py

Outputs

• question_1/results/: q1_ace_vs_nonace.png, q1_top_route_changes.png, q1_summary_report.md
• question_2/results/: q2_dashboard_YYYYMMDD_HHMM.png, q2_summary_report.md
• question_3/results/: q3_congestion_pricing_analysis.png, route_comparison_chart.png, q3_summary_report.md

Notes

• Data loaders scan data/ (and legacy data/raw/) for expected CSVs and normalize columns.
• If no pre‑pricing data exists for Q3, the report indicates that net change isn’t computable and charts adapt accordingly.