Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4968605 | Transportation Research Part C: Emerging Technologies | 2016 | 14 Pages |
Abstract
Cities are complex systems, where related Human activities are increasingly difficult to explore within. In order to understand urban processes and to gain deeper knowledge about cities, the potential of location-based social networks like Twitter could be used a promising example to explore latent relationships of underlying mobility patterns. In this paper, we therefore present an approach using a geographic self-organizing map (Geo-SOM) to uncover and compare previously unseen patterns from social media and authoritative data. The results, which we validated with Live Traffic Disruption (TIMS) feeds from Transport for London, show that the observed geospatial and temporal patterns between special events (r = 0.73), traffic incidents (r = 0.59) and hazard disruptions (r = 0.41) from TIMS, are strongly correlated with traffic-related, georeferenced tweets. Hence, we conclude that tweets can be used as a proxy indicator to detect collective mobility events and may help to provide stakeholders and decision makers with complementary information on complex mobility processes.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Enrico Steiger, Bernd Resch, João Porto de Albuquerque, Alexander Zipf,