https://github.com/amauryval/gtfs_builder

https://github.com/amauryval/gtfs_builder

Last synced: 24 days ago
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• Host: GitHub
• URL: https://github.com/amauryval/gtfs_builder
• Owner: amauryval
• License: gpl-3.0
• Created: 2019-11-18T20:30:33.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2023-01-25T21:38:47.000Z (almost 2 years ago)
• Last Synced: 2023-03-03T21:22:37.944Z (over 1 year ago)
• Language: Python
• Size: 438 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
GTFS BUILDER

[![RunTest](https://github.com/amauryval/gtfs_builder/actions/workflows/main.yml/badge.svg?branch=master)](https://github.com/amauryval/gtfs_builder/actions/workflows/main.yml)

[![codecov](https://codecov.io/gh/amauryval/gtfs_builder/branch/master/graph/badge.svg)](https://codecov.io/gh/amauryval/gtfs_builder)

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)

A tool to compute all the intermediate stops (geometry and time range) between each trips from a GTFS in order to emulate their travel 

## Install the environment with pyenv and poetry

```bash

pyenv local 3.10.7

poetry env use 3.10.7

poetry install

```

## Configure parameters files

### params.json

Each block are used to compute all the moving stops from the specified GTFS:

1. set the name of your gtfs without space ("study_area_name")

2. Copy paste your data into the 'input_data' (for example) directory ("input_data_dir")

3. Select the transport modes to compute in a list ("transport_modes")

4. Select the date filter mode: based on "calendar_dates.txt" or "calendar.text" ("date_mode")

5. Define the date to compute ("date")

6. Set if you want to build the "shapes.txt" file ("build_shape_id")

7. Set the interpolation nodes values: 100 = 1 nodes interpolated each 100 meters ("interpolation_threshold")

8. Set if you want to proceed data with multiprocessing mode. Good only if you have a lot of trips! ("multiprocess")

```

[

    {

        "study_area_name": "your_area_name",

        "input_data_dir": "input_data",

        "transport_modes": ["train"],

        "date_mode": "calendar_dates",

        "date": "20211125",

        "build_shape_id": true,

        "interpolation_threshold": 3500,

        "multiprocess": false

    },

    {

        "study_area_name": "your_area_name_2",

        "input_data_dir": "input_data_2",

        "transport_modes": ["tramway", "metro"],

        "date_mode": "calendar",

        "date": "20191115",

        "build_shape_id": false,

        "interpolation_threshold": 1000,

        "multiprocess": true

    }

]

```

### inputs_attrs.json

Copy paste the 'inputs_attrs.json' file into your data direction (ex: 'input_data')

This file is used to map column types for each gtfs files

## Run the process

Let's go to compute your data

```

python db_run.py

```

You'll get 3 parquet files:

* [study_area_name]_moving_stops.parq:

Contains all the stops interpolated

* [study_area_name]_base_stops_data.parq

On going.... [TODO]

* [study_area_name]_base_lines_data.parq

On going.... [TODO]