Name
Title | ||
|---|---|---|
TrafficWatch: Real-Time Traffic Incident Detection and Monitoring Using Social Media. |
Abstract | ||
|---|---|---|
Social media has become a valuable source of real-time information. Transport Management Centre (TMC) in Australian state government of New South Wales has been collaborating with us to develop TrafficWatch, a system that leverages Twitter as a channel for transport network monitoring, incident and event managements. This system utilises advanced web technologies and state-of-the-art machine learning algorithms. The crawled tweets are first filtered to show incidents in Australia, and then divided into different groups by online clustering and classification algorithms. Findings from the use of TrafficWatch at TMC demonstrated that it has strong potential to report incidents earlier than other data sources, as well as identifying unreported incidents. TrafficWatch also shows its advantages in improving TMC's network monitoring capabilities to assess network impacts of incidents and events. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-31753-3_43 | ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2016, PT I |
Keywords | Field | DocType |
Social media,Incident detection,Classification | Data mining,Social media,State government,Computer science,Communication channel,Network monitoring,Statistical classification,Cluster analysis,Transport network | Conference |
Volume | ISSN | Citations |
9651 | 0302-9743 | 5 |
PageRank | References | Authors |
0.43 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hoang Nguyen | 1 | 5 | 0.43 |
| Wei Liu | 2 | 468 | 37.36 |
| Paul Rivera | 3 | 5 | 0.43 |
| Fang Chen | 4 | 156 | 49.84 |