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Ankara EGO Bus & Rail System Analysis

Overview

A comprehensive spatial and temporal analysis of Ankara's EGO public transit system. The study examines service-demand mismatches, route efficiency, and structural patterns using grid-based spatial analysis, anomaly detection, and regime shift analysis.

Key Findings

  • 173 days of bus data analyzed (Dec 2023 – Oct 2024)
  • 315 days of metro/rail data (Dec 2023 – Nov 2024)
  • 550 bus routes + 5 rail lines covering 1,382 grid cells (1km × 1km)
  • 13 critical grids identified (both anomalous and unstable)
  • 417 undersupply hotspots and 353 oversupply coldspots (LISA)
  • Weekday vs Weekend: -28.8% passengers, -21% occupancy

Methodology

Data Pipeline

  1. PDF Parsing → Extract daily bus & metro data from EGO PDF reports
  2. Date Correction → Match reports to actual calendar dates
  3. Grid Aggregation → 1km × 1km spatial grid, two distribution methods:
    • Connectivity-weighted: Passengers distributed by stop transfer power
    • Position-weighted: Terminal-biased distribution (30% first/last stops)
grid_doluluk_orani

Analysis Methods

  • Anomaly Detection: Autoencoder (PCA), Isolation Forest, LOF, Graph-based
  • Regime Shift: Embedding stability, Daily clustering
  • Spatial Analysis: Global/Local Moran's I (LISA)
6-grid category_radar_chart

Tech Stack

  • Python 3.13 — pandas, numpy, scikit-learn

  • Visualization — Matplotlib, Seaborn, Plotly, Folium

  • ML — Scikit-learn (Isolation Forest, LOF, K-Means, PCA)

Quick Start

pip install pandas numpy matplotlib seaborn folium plotly scikit-learn pysal networkx pdfplumber

# Create grid data
python ai_analysis/scripts/create_daily_grid_data.py

# Run anomaly analysis
python ai_analysis/scripts/grid_anomaly_and_regime_analysis.py

# Visualize results
python ai_analysis/scripts/visualize_anomaly_regime_results.py

# Generate grid maps
python scripts/visualize_raw_features_grid.py --input data/daily_grid_data.csv

Data Sources

  • EGO Genel Müdürlüğü — Ankara Metropolitan Municipality, Public Transit Authority
  • Daily bus route reports & metro/rail passenger data (PDF)
  • -Overpass and Google Geocoding API

License

Academic research use.

Citation

If you use this work, please cite:

@misc{kancan2026skeletaltrap,
  title        = {The Skeletal Trap: Mapping Spatial Inequality and Ghost Stops in Ankara's Transit Network},
  author       = {Kancan, Elifnaz},
  year         = {2026},
  eprint       = {2602.15470},
  archivePrefix= {arXiv},
  primaryClass = {physics.soc-ph},
  doi          = {10.48550/arXiv.2602.15470},
  url          = {https://arxiv.org/abs/2602.15470}
}