https://github.com/ppintosilva/congestion18tynewear
Flow traffic data for several congestion events occurring in the region of Tyne and Wear in the year of 2018.
https://github.com/ppintosilva/congestion18tynewear
dataset spatial-data traffic traffic-congestion traffic-data traffic-flow
Last synced: 2 months ago
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Flow traffic data for several congestion events occurring in the region of Tyne and Wear in the year of 2018.
- Host: GitHub
- URL: https://github.com/ppintosilva/congestion18tynewear
- Owner: ppintosilva
- License: other
- Created: 2020-02-13T15:05:06.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-02-18T18:39:45.000Z (about 5 years ago)
- Last Synced: 2025-01-23T07:26:31.540Z (4 months ago)
- Topics: dataset, spatial-data, traffic, traffic-congestion, traffic-data, traffic-flow
- Language: R
- Size: 14.5 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE
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README
# congestion18tynewear
[](https://travis-ci.org/ppintosilva/congestion18tynewear)
This package contains data of several events that affected
traffic flow in the region of Tyne and Wear in the year 2018:
- `metadata` contains information about the events as tweeted by the regional
[Urban Traffic Management & Control facility](https://twitter.com/NELiveTraffic).
- `events` contains the following data for each event:
- traffic `flow` captured around the epicentre of the traffic event,
within a time window of several hours before and after the incident.
- `spatial` features cropped to the area of interest.
Includes data about monitored locations, shortest paths between
locations, the enclosing primary (A,B and C roads) and arterial road networks
and nearby amenities.
- a `network` capturing the relationships between flows.
The main goal of this package is to provide resources to study and profile
traffic congestion under different urban scenarios, using a type of data
which is often not readily available to researchers but whose underlying
technology is becoming more widespread to monitor traffic flows
within cities - **Automatic Number Plate Recognition**.
The data are ready to be used with the
[anprflows](https://github.com/ppintosilva/anprflows) package
(which was also used to crop it).
Refer to its vignettes for examples on how to do visualise and
manipulate the data.
## A note on traffic flow data
Flow data refers to origin-destination (OD) traffic flows between pairs of
locations in the road network. This includes the number, and speed statistics
of vehicles passing between each pair of locations during each time period.
At these locations,
one or more Automatic Number Plate Recognition (ANPR) cameras have been deployed,
so that individual and aggregate travel times can be recorded and inform
traffic bodies of real-time traffic state and network performance.
Flow data is derived from raw ANPR data – a table containing the columns
`vehicle_id | location | timestamp` – using the
[anprx](https://github.com/ppintosilva/anprx) python package.
A link to the associated research paper and underlying methodology is hopefully
coming soon.
These can then be further spatially cropped, visualised and analysed using the
[anprflows](https://github.com/ppintosilva/anprflows).
## Installation
The package is not yet available in CRAN.
You can install the development version from Github:
``` r
devtools::install_github("ppintosilva/congestion18tynewear")
```