bus-metrics-england

:warning: This repository is still in the development phase. Caution should be taken before using or referencing this work in any way - use it at your own risk.

Introduction

About

This repo aims to provide other developers with tools to interrogate bus services in England by combining realtime (from the Bus Open Data Service) and timetable data (from Department for Transport). We aim to build flexibility for analysis at different geography levels and across a range of metrics, including service coverage, punctuality and quality of service (e.g. excess wait time).

Installation

This project was developed in a Python 3.11 environment; this is recommended. Specific versions of some packages are required so it is recommended to set up a virtual environment, using both venv and pip:

python3.11 -m venv <name>
source <name>/bin/activate
conda deactivate

python -m pip install --upgrade pip
python -m pip install -r requirements.txt

You will also require a .env file in the format:

BODS_API_KEY="<api key for the BODS service>"

Data ingest scripts are now available. All resources (including geography and timetable data) and a sample 1 minute cut of real time data can be obtained. Punctuality can be acquired for any of 5 geographies using the --geography attribute:

python setup.py

# to be merged into setup
python src/bus_metrics/aggregation/build_schedules.py

python run.py -g lsoa

Pre-commit actions

This repository contains a configuration of pre-commit hooks. These are language agnostic and focussed on repository security (such as detection of passwords and API keys). If approaching this project as a developer, you are encouraged to install and enable pre-commits by running the following in your shell:

1. Install pre-commit:

     pip install pre-commit

2. Enable pre-commit:

     pre-commit install

   Once pre-commits are activated, whenever you commit to this repository a series of checks will be executed. The pre-commits include checking for security keys, large files and unresolved merge conflict headers. The use of active pre-commits are highly encouraged and the given hooks can be expanded with Python or R specific hooks that can automate the code style and linting. For example, the flake8 and black hooks are useful for maintaining consistent Python code formatting.

NOTE: Pre-commit hooks execute Python, so it expects a working Python build.

Usage

Further information will follow...

Workflow

Further information will follow but this is an early outline of the intended workflow for this project.

flowchart LR
   id1a([Select geography]) --> id2{Begin ingest}
   id1b([Select required metrics]) --> id2
   id2 ==> id3a[Ingest geography data]
   id2 ==> id3b[Ingest NAPTAN stops data]
   id2 ==> id3c[Ingest realtime bus data]
   id2 ==> id3d[Ingest bus timetable data]
   id3a & id3b & id3c & id3d --> id4{Process data}
   id4 ==> id5a([Export metrics by geography])
   id4 ==> id5b([Visualise metrics on England map])

Data Science Campus

At the Data Science Campus we apply data science, and build skills, for public good across the UK and internationally. Get in touch with the Campus at datasciencecampus@ons.gov.uk.

License

The code, unless otherwise stated, is released under the MIT Licence.

The documentation for this work is subject to © Crown copyright and is available under the terms of the Open Government 3.0 licence.