Paper Info
Title
STAR-CITY: semantic traffic analytics and reasoning for CITY
Abstract
This paper presents STAR-CITY, a system supporting semantic traffic 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 traffic conditions, all supporting efficient urban planning. Our system demonstrates how the severity of road traffic congestion can be smoothly analyzed, diagnosed, explored and predicted using semantic web technologies. We present how semantic diagnosis and predictive reasoning, both using and interpreting semantics of data to deliver useful, accurate and consistent inferences, have been exploited and adapted systematized in an intelligent user interface. Our prototype of semantics-aware traffic analytics and reasoning, experimented in Dublin City Ireland, works and scales efficiently with historical together with real live and heterogeneous stream data.
Year
DOI
Venue
2014
10.1145/2557500.2557537
IUI
Keywords
Field
DocType
semantics-aware traffic analytics,semantic traffic analytics,road traffic congestion,sensor data,heterogeneous stream data,real-time traffic condition,dublin city ireland,semantic web technology,predictive reasoning,semantic diagnosis,transportation,semantic web
Data science,Intelligent user interface,Semantic Web Stack,Computer science,Semantic Web,Human–computer interaction,Artificial intelligence,Reasoning system,Analytics,Semantic computing,Semantic analytics,Machine learning,Semantics
Conference
Citations 
PageRank 
References 
3
0.41
15
Authors
7
Name
Order
Citations
PageRank
Freddy Lécué163450.52
Simone Tallevi-Diotallevi2433.27
Jer Hayes328115.32
Robert Tucker4302.44
Veli Bicer516615.64
Marco Luca Sbodio622320.52
Pierpaolo Tommasi7396.71