Paper Info
Title
Smart traffic analytics in the semantic web with STAR-CITY: Scenarios, system and lessons learned in Dublin City.
Abstract
This paper gives a high-level presentation of 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. Our prototype of semantics-aware traffic analytics and reasoning, illustrated and experimented in Dublin Ireland, but also tested in Bologna Italy, Miami USA and Rio Brazil works and scales efficiently 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.
Year
DOI
Venue
2014
10.1016/j.websem.2014.07.002
Journal of Web Semantics
Keywords
DocType
Volume
Semantic web,Reasoning system,Intelligent system,Traffic analytics,Smart traffic
Journal
27
Issue
ISSN
Citations 
C
1570-8268
8
PageRank 
References 
Authors
0.45
1
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