SocialMedia2Traffic: derivation of traffic information from social media data
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-u...
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| Main Authors: | , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
13 September 2022
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| In: |
ISPRS International Journal of Geo-Information
Year: 2022, Volume: 11, Issue: 9, Pages: 1-20 |
| ISSN: | 2220-9964 |
| DOI: | 10.3390/ijgi11090482 |
| Online Access: | Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.3390/ijgi11090482 Verlag, lizenzpflichtig, Volltext: https://www.mdpi.com/2220-9964/11/9/482 
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| Author Notes: | Mohammed Zia, Johannes Fürle, Christina Ludwig, Sven Lautenbach, Stefan Gumbrich and Alexander Zipf |
| Summary: | 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. |
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| Item Description: | Gesehen am 15.11.2022 |
| Physical Description: | Online Resource |
| ISSN: | 2220-9964 |
| DOI: | 10.3390/ijgi11090482 |