Understanding Historical GTFS Data for Research Purposes

Introduction to GTFS

GTFS (General Transit Feed Specification) is an open standard for the format of public transportation schedules and routes. It provides a way for transit agencies to share their information with others, making it easier for researchers and developers to access and analyze transportation data.

The GTFS feed consists of several files: agency.txt, routes.txt, stop_times.txt, and trips.txt. Each file contains specific information about the agency, its routes, stops, and trips. The feed is usually hosted on a transit agency’s website or a third-party data provider.

GTFS has become an essential tool for urban planners, researchers, and developers working with public transportation data. Its flexibility and openness make it an attractive choice for anyone looking to analyze or visualize transportation patterns.

Obtaining Historical GTFS Data

In this article, we’ll explore how to obtain historical GTFS data using the gtfsway package in R. We’ll also discuss some common challenges and limitations when working with historical data.

Installing the gtfsway Package

To begin, you’ll need to install the gtfsway package. You can do this by running the following command in your R console:

install.packages("gtfsway")

Preparing for Historical Data Retrieval

Before we dive into retrieving historical data, it’s essential to understand some key concepts and terminology.

GTFS Version: The GTFS feed can be either version 1.0 or version 1.4. Version 1.4 is considered the standard for modern feeds.
Feed URL: The feed URL is where you’ll find the historical data. It usually looks like https://gtfsrt.api.translink.com.au/Feed/SEQ.
Agency ID: Each agency has a unique ID, which is used to identify its data in the GTFS feed.

Retrieving Historical Data with gtfsway

Now that we have our package installed and we understand some key concepts, let’s dive into retrieving historical data using gtfsway.

library(gtfsway)

First, you’ll need to specify your agency ID. You can find this in the GTFS feed. In our case, the URL is:

url <- "https://gtfsrt.api.translink.com.au/Feed/SEQ"

This is for TransLink’s Brisbane data.

Next, we’ll use httr::GET() to retrieve the feed:

response <- httr::GET(url)

We then need to convert the response into a GTFS-compatible object using gtfs_realtime():

FeedMessage <- gtfs_realtime(response)

Extracting Trip Updates

Now that we have our data, let’s extract the trip updates. We can do this with the following function call:

lst <- gtfs_tripUpdates(FeedMessage)

This will return a list containing all the trip update information in the feed.

Specifying Dates

To retrieve historical data for a specific date range, we’ll need to modify our URL and query parameters. The GTFS API allows us to specify a date by adding the date parameter to the URL.

# Specify the start and end dates as YYYY-MM-DD strings
start_date <- "2022-01-01"
end_date <- "2022-01-14"

# Construct the full URL with query parameters
url <- paste0("https://gtfsrt.api.translink.com.au/Feed/SEQ", "?date=", start_date, "&endDate=", end_date)

We’ll also need to adjust our function call to reflect this new date range:

lst <- gtfs_tripUpdates(FeedMessage, 
                         start_date = as.Date(start_date),
                         end_date = as.Date(end_date))

Note that the as.Date() conversion is necessary because the GTFS API expects dates in a specific format.

Specifying Station IDs

To retrieve data for a specific station ID, we can use the stops object within our feed. Let’s say we want to retrieve data for Central Station (id = 600016…600024):

# Extract the stops information from the feed
stops <- gtfs_stop_times(FeedMessage)

# Find the stop with the matching station ID
central_station <- stops[stops$stop_id %in% c("600016", "600017", "600018"), ]

We can then use the central_station object to filter our trip updates data:

lst <- lst[with(central_station, trip_update %in% c(1, 2)), ]

Note that we’re using the %in% operator to create a logical vector indicating which trips are associated with Central Station.

Additional Considerations

When working with historical GTFS data, keep in mind the following:

Data Quality: Historical data may be less accurate or complete than current data.
API Limitations: The GTFS API has usage limits and requirements for authentication. Be sure to review these before retrieving large amounts of data.
Data Formats: GTFS feeds can be formatted in different ways, depending on the agency and feed version.

Conclusion

In this article, we explored how to obtain historical GTFS data using the gtfsway package in R. We discussed some key concepts, such as GTFS versions, feed URLs, and agency IDs. We also showed how to retrieve historical data for a specific date range and station ID. Remember to consider data quality, API limitations, and formatting requirements when working with historical GTFS data.

Common Use Cases

Urban Planning: Analyze transportation patterns to inform urban planning decisions.
Research Studies: Study the effects of changes in public transportation on traffic congestion or air pollution.
Data Visualization: Visualize GTFS data to create interactive maps, dashboards, or reports.

Last modified on 2024-05-02