Physical-cyber-social similarity analysis in smart cities

The inter-departmental interactions and coordination of resources are two essential components for realising a smart city platform. In this study, we investigated citizens' role in enhancing and facilitating the delivery of services by merging three key aspects of the smart city research field, namely Internet of People, Internet of Things and Web of Data. To this end, we developed a hybrid approach to extract meaningful information and to find physical-cyber-social similarity in smart cities. The three specific data sources used in this study were Twitter, road traffic disruptions collected from Transport for London API, and events parsed from Time Out London. With the proposed hybrid approach, we found that 49.5% of the Twitter traffic comments are reported approximately five hours prior to the authority's official records. Moreover, we discovered that amongst the pre-scheduled sociocultural events topics; transportation, cultural and social event topics are 31.75% more likely to influence the distribution of the Twitter comments than sport, weather and crime topics.

[1]  Nello Cristianini,et al.  Nowcasting Events from the Social Web with Statistical Learning , 2012, TIST.

[2]  Alexandra Moraru COMPLEX EVENT PROCESSING AND DATA MINING FOR SMART CITIES , 2012 .

[3]  Xing Chen,et al.  Extracting Key Entities and Significant Events from Online Daily News , 2008, IDEAL.

[4]  Jakub Piskorski,et al.  Real-Time News Event Extraction for Global Crisis Monitoring , 2008, NLDB.

[5]  Ralph Grishman,et al.  Real-time event extraction for infectious disease outbreaks , 2002 .

[6]  Ralf Tönjes,et al.  CityPulse: Large Scale Data Analytics Framework for Smart Cities , 2016, IEEE Access.

[7]  Krishnaprasad Thirunarayan,et al.  Extracting City Traffic Events from Social Streams , 2015, ACM Trans. Intell. Syst. Technol..

[8]  Xiaofeng Wang,et al.  Automatic Crime Prediction Using Events Extracted from Twitter Posts , 2012, SBP.

[9]  Roland Memisevic,et al.  On multi-view feature learning , 2012, ICML.

[10]  Masaaki Kikuchi,et al.  Discovering Volatile Events in Your Neighborhood: Local-Area Topic Extraction from Blog Entries , 2009, AIRS.

[11]  Xavier Carreras,et al.  Semantic Role Labeling: An Introduction to the Special Issue , 2008, Computational Linguistics.

[12]  Oren Etzioni,et al.  Open domain event extraction from twitter , 2012, KDD.

[13]  Jason Weston,et al.  Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..

[14]  María Bermúdez-Edo,et al.  On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data , 2016, IEEE Internet of Things Journal.

[15]  Hila Becker,et al.  Beyond Trending Topics: Real-World Event Identification on Twitter , 2011, ICWSM.