SocialMedia2Traffic

• Verfasst von: Zia, Mohammed [VerfasserIn]
• Verfasst von: Fürle, Johannes [VerfasserIn]
• Verfasst von: Ludwig, Christina [VerfasserIn]
• Verfasst von: Lautenbach, Sven [VerfasserIn]
• Verfasst von: Gumbrich, Stefan [VerfasserIn]
• Verfasst von: Zipf, Alexander [VerfasserIn]
• Titel: SocialMedia2Traffic
• Titelzusatz: derivation of traffic information from social media data
• Verf.angabe: Mohammed Zia, Johannes Fürle, Christina Ludwig, Sven Lautenbach, Stefan Gumbrich and Alexander Zipf
• E-Jahr: 2022
• Jahr: 13 September 2022
• Umfang: 20 S.
• Fussnoten: Gesehen am 15.11.2022
• Titel Quelle: Enthalten in: International Society for Photogrammetry and Remote SensingISPRS International Journal of Geo-Information
• Ort Quelle: Basel : MDPI, 2012
• Jahr Quelle: 2022
• Band/Heft Quelle: 11(2022), 9, Artikel-ID 482, Seite 1-20
• ISSN Quelle: 2220-9964
• DOI: doi:10.3390/ijgi11090482
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: classification
• Sach-SW: machine learning
• Sach-SW: OpenStreetMap
• Sach-SW: social media
• Sach-SW: traffic prediction
• Sach-SW: Twitter
• Sach-SW: Uber Movement
• Sach-SW: vehicle traffic
• K10plus-PPN: 182249270X
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

Traffic prediction is a topic of increasing importance for research and applications in the domain of routing and navigation. Unfortunately, open data are rarely available for this purpose. To overcome this, the authors explored the possibility of using geo-tagged social media data (Twitter), land-use and land-cover point of interest data (from OpenStreetMap) and an adapted betweenness centrality measure as feature spaces to predict the traffic congestion of eleven world cities. The presented framework and workflow are termed as SocialMedia2Traffic. Traffic congestion was predicted at four tile spatial resolutions and compared with Uber Movement data.