Mining and correlating traffic events from human sensor observations with official transport data using self-organizing-maps

Steiger, E., Resch, B., Porto de Albuquerque, J. and Zipf, A. (2016) Mining and correlating traffic events from human sensor observations with official transport data using self-organizing-maps. Transportation Research Part C: Emerging Technologies, 73, pp. 91-104. (doi: 10.1016/j.trc.2016.10.010)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Porto de Albuquerque, Professor Joao
• Authors: Steiger, E., Resch, B., Porto de Albuquerque, J., and Zipf, A.
• College/School: College of Social Sciences > School of Social and Political Sciences > Urban Studies
• Journal Name: Transportation Research Part C: Emerging Technologies
• Publisher: Elsevier
• ISSN: 0968-090X
• ISSN (Online): 1879-2359
• Published Online: 04 November 2016