ewha-parking 0.1.3

Analyze frequent and illegal parking patterns using trajectory and GIS data

ewha-parking

A Python module for detecting Frequent Parking spots from CCTV trajectory data
and identifying Illegal Parking events using GIS layers.

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How to use

from ewha_parking.frequent_parking import FrequentParking
from ewha_parking.illegal_parking import IllegalParking

# df: trajectory data (required fields: snr_id, traj_id, dtct_dt, lon/lat or geometry)
# road_df: road data (required fields: CCTV_ID, ufid, geometry)

# frequent parking
frequent_result = FrequentParking(df.copy(), road_df).call()

# illegal parking
illegal_parking = IllegalParking(
    zip_path="data/illegal_parking.zip",          # ZIP archive containing SHP files
    extract_dir="data/illegal_parking_extracted"  # Directory to extract SHP files
)
illegal_result = illegal_parking.call(frequent_result)

Output

• FrequentParking result

  • Columns: CCTV_ID, time, Geometry, Leaving_time, Traj_ID, Duration, ufid

• IllegalParking result

  • Subset of the above events flagged as illegal
  • Rules:
    • ≥ 5 minutes: within custom_area, yellow solid line, yellow dashed line, etc.
    • ≥ 1 minute: within crosswalk, sidewalk, yellow double line (excluding custom_area)

Requirements

pandas, numpy, geopandas, shapely

All SHP layers are assumed to use EPSG:4326 (WGS84). If your data uses a different CRS, please reproject it before analysis.

License

This project is licensed under JiwonKim License.

Contact

[e-mail]