Intro to GTFS

GTFS Data: Empowering emerging cities

History

GTFS, or General Transit Feed Specification, emerged in 2005 as a collaborative project between Google and the city of Portland, Oregon. Originally intended to improve public transit information for Google Maps users, it has since become the global standard for sharing public transportation data. GTFS was developed to provide a common format for transit agencies to publish their schedules, routes, and geographic information, making it easier for developers to create applications that utilise public transit data.

Utility

GTFS data serves as the backbone for numerous transit-related applications and services, enabling commuters to efficiently plan their journeys, reducing wait times, and facilitating smoother travel experiences. From journey planning apps to real-time transit trackers, GTFS data fuels a variety of tools that enhance public transportation accessibility and usability.

Global Coverage

While initially popularised in developed urban centres, GTFS has seen increasing adoption in emerging cities worldwide. As urbanisation accelerates and public transportation becomes more critical in addressing congestion and environmental concerns, emerging cities are recognizing the value of standardised transit data. Governments, transit agencies, and tech companies in these regions are actively working to implement GTFS to improve public transit systems and enhance urban mobility.

Collection and Sourcing

Collecting GTFS data typically involves collaboration between transit agencies, local governments, and technology partners. Transit agencies compile information about routes, schedules, stops, and other relevant data according to the GTFS specifications. This data is then published in a standardised format, often through official transit agency websites or data portals.

In emerging cities, where digital infrastructure and data management practices may still be evolving, collecting GTFS data might require capacity-building efforts and technical assistance. However, the benefits of standardised transit data in improving urban mobility make the investment worthwhile.

GTFS Datasets

The primary datasets included in a GTFS dataset are:

1. Stops: Information about the stops where vehicles pick up or drop off passengers. This dataset typically includes the stop ID, name, location coordinates (latitude and longitude), and possibly other attributes such as wheelchair accessibility.
2. Routes: Details about the routes operated by the transit agency, including route ID, name, and type (e.g., bus, tram, subway).
3. Trips: Information about specific trips along each route, including trip ID, route ID, and schedule information such as departure times and stop sequence.
4. Stop Times: Timetable information specifying the arrival and departure times for each trip at each stop along its route. This dataset includes the trip ID, stop ID, arrival time, departure time, and sequence order.
5. Calendar: Defines the dates when a service is available, including start and end dates and any exceptions such as holidays.
6. Calendar Dates: Specifies additional service dates beyond the regular calendar, such as exceptions or modifications to the regular service schedule.
7. Shapes: Optional dataset providing the geographic shape of each trip’s route. This dataset includes a sequence of latitude and longitude coordinates representing the path followed by the vehicle.
8. Fare Attributes: Information about the fares associated with each route or trip, including fare ID, price, and fare rules.
9. Fare Rules: Defines the rules and conditions for fares, such as which fare applies to a particular trip or combination of trips.

These datasets collectively provide comprehensive information about the structure, schedule, and operation of a public transit system. They enable developers and users to create applications and services for trip planning, real-time arrival information, and other transit-related functions.

Apps Utilising GTFS Data

Numerous applications leverage GTFS data to provide valuable transit-related services to commuters in both developed and emerging cities. These apps play a pivotal role in improving accessibility and usability of public transportation systems. Some notable examples include:

• Google Maps: Google Maps utilises GTFS data to provide transit directions, real-time arrival information, and service alerts for buses, trains, and other public transit modes. Users can plan their journeys seamlessly using Google Maps’ transit feature, which relies on accurate GTFS data provided by transit agencies.
• Transit App: Transit App is a popular mobile application that helps users navigate public transit systems in cities worldwide. It integrates GTFS data to offer real-time arrival predictions, trip planning, and service alerts. Transit App’s user-friendly interface and comprehensive coverage make it a go-to tool for commuters in many urban areas.
• Moovit: Moovit is another transit app that relies on GTFS data to provide trip planning, real-time arrival information, and service alerts. It boasts a large user base and offers features such as crowdsourced updates and multimodal trip  planning, making it particularly useful for commuters in diverse urban environments.
• Citymapper: Citymapper is known for its innovative features and intuitive interface, utilising GTFS data to offer transit directions, real-time updates, and multimodal trip planning in cities around the world. Its user-centric design and focus on urban mobility make it a valuable tool for commuters in emerging cities seeking efficient transit solutions.

These apps demonstrate the versatility and widespread adoption of GTFS data in powering transit-related services for commuters. By leveraging standardised transit information, these applications contribute to enhanced urban mobility, reduced congestion, and improved accessibility to public transportation in emerging cities and beyond.

Conclusion

In summary, GTFS data plays a crucial role in enhancing public transportation systems in emerging cities by providing standardised, accessible information for commuters and developers alike. As these cities continue to grow and modernise their transit infrastructure, embracing GTFS can contribute to more efficient, sustainable, and inclusive urban mobility solutions.

In the next unit we cover how GTFS data for informal or popular transport can be collected using a case study from Cambodia.

Suggested readings

• TUMI GTFS Analyzer
• Gaining Insights into the Public Transport with the TUMI GTFS Analyzer