Estimating the potential number of daily visitors in urban areas. Methodical evaluation of the points of interest of open data services using the example of transport mobility in Cologne

Georg Moog; Frauke Kraas

doi:10.14512/rur.3091

Authors

Georg Moog SSP Consult Beratende Ingenieure GmbH https://orcid.org/0009-0004-6305-8506
Frauke Kraas Universität zu Köln https://orcid.org/0000-0002-3498-6758

DOI:

https://doi.org/10.14512/rur.3091

Keywords:

Traffic modelling, OpenStreetMap, Open Data, estimation of visitors, GIS, Cologne

Abstract

Accurate estimates of the potential number of daily visitors are an important parameter in the planning of transportation infrastructure, allowing the development of appropriate solutions for specific planning cases. In this paper, previous methods for recording or estimating potential daily visitor volumes in urban areas in spatially differentiated analysis are extended by points of interest from open data services and supplemented by cartographic representations. The methodology addresses existing research gaps and areas for further development. For the first time, the explicit spatial contextualization of the calculated visitor volumes is addressed. The results of the calculated daily visitor volumes enable case-based planning of transport infrastructure in urban areas. Moreover, spatial visualizations of high-density hotspots can be produced to support planners lacking local knowledge. In order to critically reflect on the methodology used, the transport model of the City of Cologne serves as a benchmark for the study. Nevertheless, the relevance of the further development of the procedure is emphasized, especially with regard to the application of adequate estimates of the potential number of daily visitors.

Downloads

Download data is not yet available.

References

Arase, K.; Wu, Z.; Migita, T.; Takahashi, N. (2022): Deep Learning of OpenStreetMap Images Labelled Using Road Traffic Accident Data. In: Institute of Electrical and Electronics Engineers (ed.): Proceedings of 2022 IEEE Region 10 International Conference, TENCON 2022. Hong Kong, 1–6. https://doi.org/10.1109/TENCON55691.2022.9977529

Balac, M.; Hörl, S. (2021): Synthetic population for the state of California based on open data: examples of San Francisco Bay area and San Diego County. Zürich. https://doi.org/10.3929/ethz-b-000481954

Bechtel, B.; Hüser, C. (2023): Das Stadtklima: Ursachen, Effekte und Erfassung. In: Geographische Rundschau 75, 7/8, 10–15.

BMDV – Bundesministerium für Digitales und Verkehr (2023): Nationaler Radverkehrsplan 3.0. Berlin.

Bosserhoff, D. (2022): Programm Ver_Bau: Abschätzung des Verkehrsaufkommens durch Vorhaben der Bauleitplanung mit Excel-Tabellen am PC. https://www.dietmar-bosserhoff.de/Programm.html (13.11.2025).

Briem, L.; Heilig, M.; Klinkhardt, C.; Vortisch, P. (2019): Analyzing OpenStreetMap as data source for travel demand models case study in Karlsruhe. A case study in Karlsruhe. In: Transportation Research Procedia 41, 104–112. https://doi.org/10.1016/j.trpro.2019.09.021

Busch-Geertsema, A.; Klinger, T.; Lanzendorf, M. (2019): Geographien der Mobilität. In: Gebhardt, H.; Glaser, R.; Radtke, U.; Reuber, P.; Vött, A. (eds.): Geographie – Physische Geographie und Humangeographie. Berlin, 1015–1032.

Cai, P.; Lee, Y.; Luo, Y.; Hsu, D. (2020): SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic. In: 2020 IEEE International Conference on Robotics and Automation. Paris, 4023–4029. https://doi.org/10.1109/ICRA40945.2020.9197228

Camargo, C.Q.; Bright, J.; Hale, S.A. (2019): Diagnosing the performance of human mobility models at small spatial scales using volunteered geographical information. In: Royal Society Open Science 6, 11, 1–15. https://doi.org/10.1098/rsos.191034

Chmielewski, J.; Kempa, J. (2020): Hexagonal Zones in Transport Demand Models. In: KnE Engineering: International Congress on Engineering — Engineering for Evolution, 103–116. https://doi.org/10.18502/keg.v5i6.7025

Cohen, A.; Dalyot, S. (2020): Machine-learning prediction models for pedestrian traffic flow levels: Towards optimizing walking routes for blind pedestrians. In: Transactions in GIS 24, 5, 1264–1279. https://doi.org/10.1111/tgis.12674

Dangschat, J.S. (2022): Verkehrswende – sozial und räumlich ausgewogen. In: Journal für Mobilität und Verkehr 14, 2–10. https://doi.org/10.34647/jmv.nr14.id87

Ferster, C.; Fischer, J.; Manaugh, K.; Nelson, T.; Winters, M. (2020): Using OpenStreetMap to inventory bicycle infrastructure: A comparison with open data from cities. In: International Journal of Sustainable Transportation 14, 1, 64–73. https://doi.org/10.1080/15568318.2018.1519746

FGSV – Forschungsgesellschaft für Straßen- und Verkehrswesen (2022): Empfehlungen zum Einsatz von Verkehrsnachfragemodellen für den Personenverkehr (EVNM-PV). Köln.

Göttsche, F.; Brinkmann, J. (2023): Wie passen wir unsere Städte in Zeiten des Klimawandels an Hitze an? In: Geographische Rundschau 75, 7/8, 16–21.

Hertig, E.; Keck, M. (2023): Deutschlands Städte im Klimawandel. In: Geographische Rundschau 75, 7/8, 4–9.

Kagerbauer, M. (2022): Integration von neuen Mobilitätsformen in Verkehrserhebungen und Verkehrsmodellierung. Karlsruhe. = Schriftenreihe des Instituts für Verkehrswesen 77. https://doi.org/10.5445/KSP/1000144791

Keler, A.; Grigoropoulos, G.; Mussack, D. (2019): Enriching complex road intersections from OSM with traffic-related behavioral information. In: 29th International Cartographic Conference 2, 61. https://doi.org/10.5194/ica-proc-2-61-2019

Klinkhardt, C.; Woerle, T.; Briem, L.; Heilig, M.; Kagerbauer, M.; Vortisch, P. (2021): Using OpenStreetMap as a Data Source for Attractiveness in Travel Demand Models. In: Transportations Research Record: Journal of the Transportation Research Board 2675, 8, 294–303. https://doi.org/10.1177/0361198121997415

Klinkhardt, C.; Kühnel, F.; Heilig, M.; Lautenbach, S.; Wörle, T.; Vortisch, P.; Kuhnimhof, T. (2023): Quality Assessment of OpenStreetMap’s Points of Interest with Large-Scale Real Data. In: Transportation Research Record: Journal of the Transportation Research Board 2677, 12, 661–674. https://doi.org/10.1177/03611981231169280

Köhler, U. (2014): Einführung in die Verkehrsplanung. Stuttgart.

de Lange, N. (2020): Geoinformatik in Theorie und Praxis. Grundlagen von Geoinformationssystemen, Fernerkundung und digitaler Bildverarbeitung. Berlin. https://doi.org/10.1007/978-3-662-60709-1

Liu, X.; Long, Y. (2016): Automated identification and characterization of parcels with OpenStreetMap and points of interest. In: Environment and Planning B: Urban Analytics and City Science 43, 2, 341–360. https://doi.org/10.1177/0265813515604767

Loo, B.; Tsoi, K.H. (2018): The sustainable transport pathway: A holistic strategy of Five Transformations. In: Journal of Transport and Land Use 11, 1, 961–980. https://doi.org/10.5198/jtlu.2018.1354

Mahajan, V.; Kühnel, N.; Intzevidou, A.; Cantelmo, G.; Moeckel, R.; Antoniou, C. (2022): Data to the people: a review of public and proprietary data for transport models. In: Transport Reviews 42, 4, 415–440. https://doi.org/10.1080/01441647.2021.1977414

Martinelli, L. (2018): Can we validate every change on OSM? https://2018.stateofthemap.org/2018/T079-Can_we_validate_every_change_on_OSM_/ (13.11.2025).

Peter, M. (2021): Die Berechnung kleinräumiger und multimodaler Erreichbarkeiten auf regionaler Ebene. Hamburg. = Harburger Berichte zur Verkehrsplanung und Logistik 22. https://doi.org/10.15480/882.3673

Rau, H.; Scheiner, J. (2020): Sustainable Mobility: Interdisciplinary Approaches. In: Sustainability 12, 23, 9995. https://doi.org/10.3390/su12239995

Reynard, D. (2018): Five classes of geospatial data and the barriers to using them. In: Geography Compass 12, 4, e12364. https://doi.org/10.1111/gec3.12364

Sallard, A.; Balac, M.; Hörl, S. (2020): A synthetic population for the greater São Paulo metropolitan region. Zürich. = Arbeitsberichte Verkehrs- und Raumplanung 1545. https://doi.org/10.3929/ethz-b-000429951

SSP Consult – SSP Consult Beratende Ingenieure GmbH (2021a): Bevölkerung je INSPIRE-Rasterzelle. (December 2020).

SSP Consult – SSP Consult Beratende Ingenieure GmbH (2021b): Beschäftigte je INSPIRE-Rasterzelle. (December 2020).

SSP Consult – SSP Consult Beratende Ingenieure GmbH (2021c): Bildungseinrichtungen und deren Nutzer für das Land NRW. (December 2020).

Stadt Köln (2020): Kölner Perspektiven 2030+. Köln.

Stadt Köln (2021): Unternehmensregister Köln (Registerabzug September 2021). Köln.

Stadt Köln (2022): Die Kommunale Gebietsgliederung. Ein räumlicher Bezug für statistische Daten. Köln.

Stadt Köln (2023a): Kölner Stadtteilinformationen. Bevölkerungszahlen 2022. Köln. = Kölner Statistische Nachrichten 5/2023.

Stadt Köln (2023b): Netzentwicklung Mobilität – Attraktive Verkehrsnetze für Köln. Köln.

Stadt Köln (2023c): Verkehrsmodell der Stadt Köln. Stand: Mai 2023. Köln.

Stadt Köln (2023d): Points of Interest der Stadt Köln. Stand: Februar 2023. Köln.

Steiniger, S.; Poorazizi, M.E.; Scott, D.R.; Fuentes, C.; Crespo, R. (2016): Can we use OpenStreetMap POIs for the Evaluation of Urban Accessibility? In: International Conference on GIScience Short Paper Proceedings 1, 1, 272–275. https://doi.org/10.21433/B31167f0678p

Stengel, S.; Pumplun, S. (2011): Die freie Weltkarte OpenStreetMap – Potenziale und Risiken. In: Kartographische Nachrichten – Journal of Cartography and Geographic Information 61, 3, 115-120. https://doi.org/10.1007/BF03544072

Surahman, I.; Wegner, G. (2022): Integration of Open Data in Disaggregate Transport Modelling. A Case Study of Uppsala. Stockholm.

Topp, H. (2023): Von der autogerechten Stadt zur menschengerechten Stadt. In: Straßenverkehrstechnik 67, 1, 31–37.

Treiber, M.; Kesting, A. (2010): Verkehrsdynamik und -simulation. Daten, Modelle und Anwendungen der Verkehrsflussdynamik. Heidelberg.

UN-Habitat – United Nations Human Settlements Programme (2022): World Cities Report 2022. Envisaging the Future of Cities. Nairobi.

Vierø, A.R.; Vybornova, A.; Szell, M. (2024): BikeDNA: A tool for bicycle infrastructure data and network assessment. In: Environment and Planning B: Urban Analytics and City Science 51, 2, 512–528. https://doi.org/10.1177/23998083231184471

Xu, F.F.; Lin, B.Y.; Lu, Q.; Huang, Y.; Zhu, K.Q. (2016): Cross-region Traffic Prediction for China on OpenStreetMap. In: Winter, S. (ed.): IWCTS ’16: Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science. New York, 37–42. https://doi.org/10.1145/3003965.3003972

Yan, Y.; Feng, C.-C.; Huang, W.; Fan, H.; Wang, Y.-C.; Zipf, A. (2020): Volunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience. In: International Journal of Geographical Information Science 34, 9, 1765–1791. https://doi.org/10.1080/13658816.2020.1730848

Yeow, L.W.; Low, R.; Tan, Y.X.; Cheah, L. (2021): Points-of-Interest (POI) Data Validation Methods: An Urban Case Study. In: International Journal of Geo-Information 10, 11, 735. https://doi.org/10.3390/ijgi10110735

Zhang, L.; Pfoser, D. (2019): Using OpenStreetMap point-of-interest data to model urban change – A feasibility study. In: PLoS One 14, 2, e0212606. https://doi.org/10.1371/journal.pone.0212606

Ziemke, T.; Braun, S. (2021): Automated generation of traffic signals and lanes for MATSim based on OpenStreetMap. In: Procedia Computer Science 184, 745–752. https://doi.org/10.1016/j.procs.2021.03.093

Ziemke, D.; Kaddoura, I.; Nagel, K. (2019): The MATSim Open Berlin Scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. In: Procedia Computer Science 151, 870–877. https://doi.org/10.1016/j.procs.2019.04.120

Zilske, M.; Neumann, A.; Nagel, K. (2011): OpenStreetMap for traffic simulation. In: Proceedings of the 1st European state of the map: OpenStreetMap conference. Wien, 126–134.