Replication Data for: Urban Public Transit Frequency Indicator in Germany

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Replication Data for: Urban Public Transit Frequency Indicator in Germany
Sikder, Sujit Kumar, 2025, "Replication Data for: Urban Public Transit Frequency Indicator in Germany", https://doi.org/10.71830/ABPCUS, ioerDATA, V1
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Description
EN: This dataset provides local public transit frequency indicator for all German cities with populations exceeding 50,000. The spatial resolution is based on 1 km grid cells. Open-source public transit feed data (GTFS) served as input for an advanced geoprocessing workflow. A spatio-temporal filtering approach was applied to model GTFS data on a weekly basis—averaging values for weekdays, Saturdays, and Sundays—as well as during peak traffic hours (morning: 06:00–08:59; afternoon: 14:00–16:59). The resulting feature layers contain hourly departure counts for each public transit service mode (local and regional) at every transit stop within the selected time periods. This point-based dataset was used to calculate a public transit frequency indicator. Regional transit services were assigned a higher weight than local modes, based on the assumption that regional transport typically offers greater capacity than local options such as buses and trams. A maximum walking distance of 500 meters was assumed for transit users. The input Iso-Area size was set to 1,000 meters in the point-based network interpolation algorithm, implemented using the QNEAT3 plugin in QGIS. This algorithm utilized OpenStreetMap road network data and shortest-path optimization criteria. Additionally, open source data from the INSPIRE grid was employed as a standardized raster mask for geoprocessing and adminitrative boundary of cites to provide aggregated statistics at city scale. (2025)
DE: Dieses Datensatz enthält Informationen zur Häufigkeit des öffentlichen Nahverkehrs für alle deutschen Städte mit mehr als 50.000 Einwohnern. Die räumliche Auflösung basiert auf einem Raster mit 1-km-Zellen. Als Grundlage für den fortgeschrittenen Geoverarbeitungsprozess dienten offene Datenquellen aus dem öffentlichen Verkehrsfeed (GTFS). Ein raum-zeitliches Filterverfahren wurde angewendet, um die GTFS-Daten auf Wochenbasis zu modellieren – mit Durchschnittswerten für Werktage, Samstage und Sonntage sowie für Stoßzeiten (morgens: 06:00–08:59; nachmittags: 14:00–16:59). Die daraus resultierenden Feature-Layer enthalten stündliche Abfahrtszahlen für jede Verkehrsart (lokal und regional) an allen Haltestellen innerhalb der ausgewählten Zeiträume. Dieses punktbasierte Datenset wurde verwendet, um einen Indikator für die Häufigkeit des öffentlichen Verkehrs zu berechnen. Regionalverkehrsdienste erhielten ein höheres Gewicht als lokale Verkehrsmittel, basierend auf der Annahme, dass der Regionalverkehr in der Regel eine höhere Kapazität bietet als lokale Optionen wie Busse und Straßenbahnen. Für die Nutzer des öffentlichen Verkehrs wurde eine maximale Gehentfernung von 500 Metern angenommen. Die Eingabegröße der Iso-Area wurde im punktbasierten Netzwerkinterpolationsalgorithmus auf 1.000 Meter festgelegt, der mit dem QNEAT3-Plugin in QGIS implementiert wurde. Dieser Algorithmus nutzte das Straßennetz von OpenStreetMap sowie Kriterien zur Optimierung der kürzesten Wege. Zusätzlich wurden offene Daten aus dem INSPIRE-Raster als standardisierte Rastermaske für die Geoverarbeitung und die administrativen Stadtgrenzen verwendet, um aggregierte Statistiken auf Stadtebene bereitzustellen. (2025)
Subject Computer and Information Science; Engineering
Keyword Sustainable Mobility, Open data, Public Transit Service, sustainable transformation
Related Publication References: Sikder, Sujit Kumar; Ehrig, Nico; Herold, Hendrik; Meinel, Gotthard Analyse der ÖPNV-Versorgung mittels offener Fahrplandaten – Potenziale, Herausforderungen und Lösungsansätze In: Meinel, Gotthard; Schumacher, Ulrich; Behnisch, Martin; Krüger, Tobias (Hrsg.) : Flächennutzungsmonitoring XII mit Beiträgen zum Monitoring von Ökosystemleistungen und SDGs. Berlin : Rhombos-Verlag, 2020, (IÖR-Schriften; 78), S.263-270.doi: https://doi.org/10.26084/12dfns-p026
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Curation Status History
StatusDateAssigner
2025-11-12 11:36:53Riyan Habeeb
Final Approval2025-10-28 10:16:27Sujit Sikder
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