Name
Abstract | ||
|---|---|---|
Diagnosis, or the method to connect causes to its effects, is an important reasoning task for obtaining insight on cities and reaching the concept of sustainable and smarter cities that is envisioned nowadays. This paper, focusing on transportation and its road traffic, presents how road traffic congestions can be detected and diagnosed in quasi real-time. We adapt pure Artificial Intelligence diagnosis techniques to fully exploit knowledge which is captured through relevant semantics-augmented stream and static data from various domains. Our prototype of semantic-aware diagnosis of road traffic congestions, experimented in Dublin Ireland, works efficiently with large, heterogeneous information sources and delivers value-added services to citizens and city managers in quasi real-time. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-35173-0_8 | International Semantic Web Conference |
Keywords | Field | DocType |
diagnosing road traffic congestion,city manager,heterogeneous information source,relevant semantics-augmented stream,important reasoning task,dublin ireland,semantic-aware diagnosis,static data,road traffic,pure artificial intelligence diagnosis,road traffic congestion,semantic web technology | World Wide Web,Static data,Computer science,Semantic Web,Road traffic,Exploit,Database | Conference |
Citations | PageRank | References |
17 | 1.09 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Freddy Lécué | 1 | 634 | 50.52 |
| Anika Schumann | 2 | 103 | 13.12 |
| Marco Luca Sbodio | 3 | 223 | 20.52 |