Skip to main content

Simulate Transit Distance After Pedestrianization

Project description

Simulate Transit Distance After Pedestrianization (STDAP)

Supply and Demand based pedestrianization, moving bus-stops accordingly

This package simulates the effects of pedestrianization upon the average distance to transit stops in Dutch cities. Pedestrianization can be performed based on the population and the number of interesting destinations nearby. Bus stops can be moved by either moving them to the nearest valid location, or by completely redesigning the bus-network. This package is build to be capable of large scale simulations in a reasonable time frame. Simulations can be run from the provided gui, or in Python code directly.

Features

  • Highly scalable simulations
  • Great visualization of results
  • Highly configurable

Getting started

The package can be used to simulate pedestrianization in the following way:

from STDAP.core.main_class import Simulator
sim = Simulator(datasets)
city_options = sim.get_cities()
sim.choose_city(city_name)
### Run a single pedestrianization for detailed results.
sim.Sim_trans_dist_single(percentage, options)
### Simulate for a range of pedestrianization percentages for fast results.
sim.Sim_trans_dist_single(start, stop, count, options)

Datasets can be downloaded from the Dutch Central Bureau of Statistics (CBS).

The dashboard gui can be started with the following code:

from STDAP.gui.dashboard import show_dashboard
show_dashboard()

A more detailed manual is available on the GitHub page (see Project links).

Configuration

The package is designed to be highly configurable. This can be done easily with the settings class.

from STDAP.config.settings import get_settings 
settings = get_settings()
settings.parameter = value

Configurable parameters and their default values can be obtained with the describe method:

print(settings.describe())

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

stdap-1.0.4.tar.gz (35.4 kB view details)

Uploaded Source

Built Distribution

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

stdap-1.0.4-py3-none-any.whl (37.6 kB view details)

Uploaded Python 3

File details

Details for the file stdap-1.0.4.tar.gz.

File metadata

  • Download URL: stdap-1.0.4.tar.gz
  • Upload date:
  • Size: 35.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for stdap-1.0.4.tar.gz
Algorithm Hash digest
SHA256 91c05cf8be3c1d489d73771fbe0fbb1fbd86410a731a7e883cb8839569a7b2ec
MD5 347d04dabd881cd25a739caff000928f
BLAKE2b-256 fa056ebef4ee9807b86df7e7d25a4b59b84a8accc55cc524179e4b23353c5a0e

See more details on using hashes here.

File details

Details for the file stdap-1.0.4-py3-none-any.whl.

File metadata

  • Download URL: stdap-1.0.4-py3-none-any.whl
  • Upload date:
  • Size: 37.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for stdap-1.0.4-py3-none-any.whl
Algorithm Hash digest
SHA256 603823af9fd50c4af8c65754121d12df9565868a86b56aa219fb85fa94dbb724
MD5 8ff7cbff118aa820dd51911d75f54364
BLAKE2b-256 f95a598e5f9df701a09c66df0de6134a26c47d681061686a1a26e08c98cff098

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