GTFS Data Made Simple: A Practical Guide to Transit Feed Specification and Performance Analysis.pptx

"GTFS Data Made Simple: A Practical Guide to Transit Feed Specification and Performance Analysis" is an essential resource designed to simplify the complex world of General Transit Feed Specification (GTFS) data for researchers, developers, and planners. This presentation, created by experts from Universiti Malaysia Perlis and Shahjalal University of Science and Technology, offers a comprehensive overview of GTFS, detailing the differences between static and real-time data, the structure of GTFS feeds, and the algorithms used for data analysis. It also provides a step-by-step guide for creating and sharing GTFS feeds, aiming to enhance transit planning and performance evaluation through effective data management and analysis.

1. "GTFS Data Made Simple: A Practical Guide to Transit Feed Specification and Performance Analysis" Presenter a : Mahbub Hassan Creators b : Mahbub Hassan, Abdullah Al Nafees, Hridoy Deb Mahin , Saikat Sarkar Shraban , Arpita Paul Affiliations: a Faculty of Civil Engineering & Technology, Universiti Malaysia Perlis ( UniMAP ), Arau, 02600, Perlis, Malaysia. b Sylhet Engineering College (SEC), School of Applied Sciences & Technology, Shahjalal University of Science and Technology, Tilagarh , Alurtol Road, Sylhet 3100, Bangladesh. For Contact: [email protected]

2. Introduction to GTFS Data What is GTFS? GTFS (General Transit Feed Specification) is a standardized format used by public transit agencies to share their schedule and route information with developers. It enables developers to create applications that provide users with accurate transit information, such as trip planners and schedule viewers. Fig. 1. GTFS Data Feed

3. Static vs. Realtime GTFS Understanding the Difference GTFS Static: Contains scheduled information like bus stops, routes, and times. GTFS Realtime: Provides live updates such as delays, service alerts, and vehicle positions. Fig. 2. Realtime Traffic Incidents.

4. Purpose of the GTFS Data Framework Why Develop a GTFS Data Framework? To simplify the extraction, storage, and analysis of GTFS data, both static and real-time. To assist researchers, planners, and developers in creating effective transit solutions and performance evaluations. Fig. 1. Workflow for GTFS data processing

5. What is a GTFS Feed? GTFS Feed: A collection of text files zipped together containing information about public transit, such as bus stops, routes, schedules, and fare information. Example Files in a GTFS Feed: agency.txt: Transit agency details (e.g., name, website, contact info). Example: " FunBus , The Fun Bus, http://www.thefunbus.org, America/ Los_Angeles , [phone], en ” stops.txt: Lists transit stops with names, locations, and types. Example: "F12, 5 Av/53 St, 40.760167, -73.975224, 1” routes.txt: Describes available routes with short and long names. Example: "A, 17, Mission, The 'A' route travels from lower Mission to Downtown.

6. How GTFS Works Feeds: The Core of GTFS Transit agencies create GTFS feeds, which are collections of text files (e.g., stops, routes, trips) compressed into a ZIP file. Developers use these feeds to build apps that help people plan trips and view schedules.

7. Additional GTFS Feed Files Understanding Other Key Files: trips.txt: Details specific trips along routes (e.g., trip IDs, route IDs). Example: "AWE1, Downtown“ stop_times.txt: Specifies arrival and departure times for each stop. Example: "AWE1, 0:06:10, S1, 1, 0, 0“ calendar.txt: Defines regular service schedules and their valid date ranges. Example: "WE, 0, 0, 0, 0, 0, 1, 1, 20060701, 20060731“ fare_attributes.txt: Lists fare details like price and currency. Example: "1, 0.00, USD, 0, 0, 0"

8. Framework Overview Systematic Approach to GTFS Data Modular design with four logic units: acquisition, elaboration, storage, and visualization. Facilitates the seamless handling of large volumes of GTFS data for analysis and visualization. Fig. 1. Architectural Schema.

9. Algorithms and Methodologies Key Algorithms Used Linear interpolation for estimating vehicle positions in real-time. Storage solutions using relational databases like MySQL. Data integration with visualization tools like Tableau to enhance decision-making.

10. Getting Started with GTFS Steps to Create and Share Your GTFS Feed Look at example GTFS feeds to understand the structure. Create your own GTFS feed using provided references. Test your feed with validation tools to ensure accuracy. Publish your feed online for developers to access and use.

11. Conclusion and Future Work Summary The GTFS data framework bridges the gap in data accessibility, enabling enhanced transit planning and performance evaluation. It provides a foundation for developers and researchers to create innovative transit solutions. Next Steps Future integration of machine learning to enhance predictive analytics based on historical transit data.