Skip to main content

A Python module for calculating and analyzing three types of congestion—Road, PM, and Pedestrian congestion.

Project description

ewha-cong_dynamicP

A Python module for calculating and analyzing three types of congestion—Road, PM, and Pedestrian congestion—using trajectory data collected from CCTV systems. It includes safety-aware adjustments based on pedestrian-vehicle and pedestrian-pedestrian interactions.

How to use

'''python from ewha_congestions.three_congestions import processing_congestions

df = your data. data index must be DatetimeIndex as "dtct_dt". data must has point geometry as "geomatry", "snr_id", "distance", "speed", "acceleration", "traj_id", "direction", "mf_type", and "apr_code"

road_shp = your road data. road data must has Polygon geometry of roads as "geometry" and CCTV ID as "snr_id".

road_shp = road_shp.to_crs("EPSG:5179") for calculate road area

road_cong, ped_cong, pm_cong = processing_congestions(df, road_shp, 3600).call()

''' df: includes object tracking data with fields like snr_id, mf_type, apr_code, geometry, dtct_dt, speed, and acceleration.

road_df: includes road information with fields snr_id and geometry.

interval: time window (in seconds) over which congestion is aggregated and computed.

Output

Each output (road_cong, ped_cong, pm_cong) is a DataFrame with:

CCTV_ID: unique identifier for each camera

time: timestamp of the congestion record

congestion_level: integer from 0 to 4

0: Excellent (≤ 0.2)

1: Good (≤ 0.4)

2: Normal (≤ 0.6)

3: Crowded (≤ 0.8)

4: Very Crowded (> 0.8)

Congestion Types Type | Description Road Congestion | Combined area occupied by all moving objects divided by the road polygon area PM Congestion | PM area only, normalized by unoccupied space (excluding pedestrian & vehicle) Pedestrian Congestion | Road congestion adjusted using pedestrian-pedestrian and pedestrian-vehicle conflicts

Requirements

numpy pandas geopandas movingpandas tqdm shapely geopy

License

This project is licensed under the JiyoonLee License.

Contact

0197black@gmail.com

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ewha_cong_dynamicp-0.1.0.tar.gz (10.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ewha_cong_dynamicp-0.1.0-py3-none-any.whl (9.6 kB view details)

Uploaded Python 3

File details

Details for the file ewha_cong_dynamicp-0.1.0.tar.gz.

File metadata

  • Download URL: ewha_cong_dynamicp-0.1.0.tar.gz
  • Upload date:
  • Size: 10.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.2

File hashes

Hashes for ewha_cong_dynamicp-0.1.0.tar.gz
Algorithm Hash digest
SHA256 646177ba0ff2c54e13d3b1aef8f328d370ff4a1432e52ef469309a8460d8b806
MD5 aacc1b538f369946cf4a8d7be1de9032
BLAKE2b-256 cc33d2975a87b5367c3621238710155225dcf7dcfd72e4390ee41cfac2d6b477

See more details on using hashes here.

File details

Details for the file ewha_cong_dynamicp-0.1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for ewha_cong_dynamicp-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 d60e707152f7c6d65253efa99ec233e535bed767c1ea70cea38ef99e0f9f29e7
MD5 f04890dfd14ad386f50bebb7f019da58
BLAKE2b-256 37a2df8711b2a6b387cbabbf894614a568cb6a272cdd2f19b7d2afcf46195a1a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page