Smart Trac Analytics in the Semantic Web with STAR-CITY: Scenarios, System and Lessons Learned in Dublin City

Freddy Lécué, Simone Tallevi-Diotallevi, Jer Hayes, Robert Tucker, Veli Bicer, Marco Sbodio, Pierpaolo Tommasi

Abstract


This paper gives a high-level presentation of STAR-CITY, a system supporting semantic trac analytics and reasoning for city. STAR-CITY, which integrates (human and machine-based) sensor data using variety of formats, velocities and volumes, has been designed to provide insight on historical and real-time trac conditions, all supporting ecient urban planning. Our system demonstrates how the severity of road trac congestion can be smoothly analyzed, diagnosed, explored and predicted using semantic web technologies. Our prototype of semantics-aware trac analytics and reasoning, illustrated and experimented in Dublin Ireland, but also tested in Bologna Italy, Miami USA and Rio Brazil works and scales eciently with real, historical together with live and heterogeneous stream data. This paper highlights the lessons learned from deploying and using a system in Dublin City based on Semantic Web technologies.

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Type of Paper: Research Paper
Keywords: Semantic web, Reasoning system, Intelligent system, Trac analytics, Smart trac
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