https://github.com/tmelliott/transitr

An R package for constructing and modelling a transit network in real time to obtain vehicle ETAs
https://github.com/tmelliott/transitr

gtfs gtfs-realtime r transit

Last synced: 2 months ago
JSON representation

An R package for constructing and modelling a transit network in real time to obtain vehicle ETAs

• Host: GitHub
• URL: https://github.com/tmelliott/transitr
• Owner: tmelliott
• Created: 2018-07-16T02:25:19.000Z (over 6 years ago)
• Default Branch: develop
• Last Pushed: 2023-06-06T11:05:35.000Z (over 1 year ago)
• Last Synced: 2024-08-01T00:38:30.900Z (5 months ago)
• Topics: gtfs, gtfs-realtime, r, transit
• Language: C++
• Size: 5.22 MB
• Stars: 22
• Watchers: 4
• Forks: 0
• Open Issues: 18
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# transitr

[![Travis build status](https://travis-ci.org/tmelliott/transitr.svg?branch=develop)](https://travis-ci.org/tmelliott/transitr)

[![codecov](https://codecov.io/gh/tmelliott/transitr/branch/develop/graph/badge.svg)](https://codecov.io/gh/tmelliott/transitr)

The goals of `transitr` are to make it easy to __load GTFS data__ into a database,

construct a __transit network__ of roads and intersections,

and __model vehicles in real-time__ from an API feed to update the network

and __generate ETAs__.

# install

`transitr` is not (yet) on CRAN, so for you would need to use `devtools`:

```r

devtools::install_github('tmelliott/transitr')

```

# usage

__Still under development!__

This here is just for demonstration of what it could be like at some point

in the future.

```r

library(transitr)

library(magrittr)

## Create a database, construct network, and connect to a realtime feed

dbname <- "realtime.db"

nw <- create_gtfs("https://cdn01.at.govt.nz/data/gtfs.zip", db = dbname) %>%

    construct() %>%

    realtime_feed("https://api.at.govt.nz/v2/public/realtime/vehiclelocations",

                  with_headers("Ocp-Apim-Subscription-Key" = "mykey"),

                  response = "protobuf")

## Set the parameters and then run the model

nw %>% 

    set_parameters(n_core = 2, 

                   n_particles = 500, 

                   gps_error = 5) %>%

    model()

```

Once running, you can launch a new R session and view the shiny app:

```r

transitr::view_realtime("realtime.db")

```

# mock data server

In order to facilitate model development and checking, there's also a mock data server

in the `simulations` directory.

To install:

```bash

cd simulations

yarn 

## or if you don't use yarn

npm install

```

To start the server, you need first an archive of vehicle position feeds,

```bash

ls archive | grep vehicle | head -n 5

# vehicle_locations_20180911050001.pb

# vehicle_locations_20180911050031.pb

# vehicle_locations_20180911050102.pb

# vehicle_locations_20180911050132.pb

# vehicle_locations_20180911050201.pb

yarn start

# yarn run v1.9.4

# $ node mock_server.js

# Mock GTFS server running on port 3000!

```

Now you can run the model with the local server, which will automatically serve 

the next file with each request.

```r

## assumeing you've constructed with simulation flag:

## $ make FLAGS="-DSIMULATION"

## simulation history will be saved in a `history` directory

dir.create("history")

## set some process ID for the server to recognise (allows running multiple simulations simultaneously)

pid <- "test1"

nw <- load_gtfs("fulldata.db") %>%

    realtime_feed(sprintf("http://localhost:3000/%s/vehicle_positions", pid),

                  response = "protobuf") %>%

    set_parameters(n_core = 1,

                   n_particles = 2000,

                   gps_error = 10)

nw %>% model()

```