Mining and correlating traffic events from human sensor observations with official transport data using self-organizing-maps. (December 2016) - Steiger, Enrico; Resch, Bernd; de Albuquerque, João Porto; Zipf, Alexander
Highlights: SOM result revealed latent temporal relationships and varying daily traffic disruption patterns. Strong correlation of traffic-related, georeferenced tweets with special events ( r = 0.73), and traffic incidents ( r = 0.59) from official TIMS traffic data. No correlation of traffic-related, georeferenced tweets with traffic volume ( r = −0.19) and works ( r = −0.10) disruptions. 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.