Interactive Visualization for the GTFS and GTFS-RT Data of Budapest
Bibliographic Data
| ID | 22031689 |
|---|---|
| Authors | Robert Toth (0000-0002-7583-7433, University of Debrecen), Márton Ispány (0000-0003-2198-1816, University of Debrecen), Marianna Zichar (0000-0002-1943-6053, University of Debrecen, corresponding author) |
| Year | 2025 |
| Volume | 14 |
| Issue | 7 |
| Pages | 245 |
| Publication date | 2025-06-25 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi14070245 |
| OpenAlex | W4411700235 |
| Language | EN |
| References cited | 29 |
Various platforms, such as Google Maps, provide information about the services of public transport companies worldwide. Operators publish the planned (static) timetable using the General Transit Feed Specification (GTFS) format, while the GTFS Realtime (GTFS-RT) specification provides live (dynamic) information about the services. In this paper, we present our dataset that was built by retrieving and pre-processing the data sources of the open data platform of BKK Futár, hosted by the Centre for Budapest Transport Company (BKK). The paper contains a well-detailed description of our methods for retrieving and pre-processing the data among statistical features. The dataset covers a one-year period in which the data collection mechanism used for realtime data was continuously improved from collecting only live vehicle positions to covering all the available feeds and increasing the query frequency. We merged the static data with the vehicle positions to filter them, yielding a clean set of tracked trips. As a result, more than 90% of the daily planned trips could be reconstructed from the responses. We provide an interactive web-based visualization for the analysis of the GTFS schedule’s, and the GTFS-RT Vehicle Positions feed’s, geospatial features. The dataset and also our methodology can serve as input for various research studies to investigate the common characteristics of delays and disruptions or predict real departure times based on the current vehicle positions and historical data
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| Highly cited | No |
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