A Model to Harness Heterogeneous Data for Urban Traffic Services

Gang-min LI, Yi-lin ZHAO, Yong YUE


The real-time traffic information provides significant convenience to road users and city traffic managers. However, the causes for traffic congestion are rarely and fully unexplored. This paper presents a model to harness heterogeneous data from different sources, explains the potential reasons of traffic phenomena and predict the future traffic flow. A prototype system has been built, which works with real-time, heterogeneous data stream including basic traffic data, planned road works, dynamically events, inclement weather or unplanned street closures. Open data source and social media are captured to diagnose road congestion and explore the underlying causes of traffic congestions. The test result illustrates the feasibility of use proposed model in urban traffic control.


Semantic web, OWL, Smart city


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