SocialMedia2Traffic: derivation of traffic information from social media data - Zia, Mohammed; Fürle, Johannes; Ludwig, Christina; Lautenbach, Sven; Gumbrich, Stefan; Zipf, Alexander (ISPRS International Journal of Geo-Information, 2022)
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.
Gesehen am 15.11.2022