TY - GEN
T1 - Traffic incident validation and correlation using text alerts and images
AU - Yan, Wye Huong
AU - Ong, Justin
AU - Ho, Shen Shyang
AU - Cherian, Jim
PY - 2014/11/4
Y1 - 2014/11/4
N2 - One of the major challenges during the process of extracting infor-mation from multiple spatio-temporal data sources of diverse data types is the matching and fusion of extracted knowledge (e.g. inter-esting nearby events detected from text, estimated density or flow from a set of geo-coded images). In this demonstration, we present PETRINA ("PErsonalized TRaffic INformation Analytics"), a sys-tem that provides traffic-related incident monitoring, mapping, and analytics services. In particular, we showcase two main functional-ities: (1) text traffic alert validation based on traffic condition infor-mation derived from traffic camera images and (2) traffic incident correlation based on spatio-temporal proximity of different inci-dent types (e.g., accidents and heavy traffic). Despite the fact that the images are sparse (available every three minutes), the regularity makes it possible to validate whether a text traffic alert is outdated or not, and to more accurately estimate the time elapsed and total incident time. Multiple traffic incidents can be grouped together as a single event based on the traffic incident correlation to reduce information redundancy. Such enhanced real-time traffic informa-tion enables PETRINA to offer services such as dynamic routing with traffic incident advices, spatiotemporal traffic incident visual analytics, and congestion analysis.
AB - One of the major challenges during the process of extracting infor-mation from multiple spatio-temporal data sources of diverse data types is the matching and fusion of extracted knowledge (e.g. inter-esting nearby events detected from text, estimated density or flow from a set of geo-coded images). In this demonstration, we present PETRINA ("PErsonalized TRaffic INformation Analytics"), a sys-tem that provides traffic-related incident monitoring, mapping, and analytics services. In particular, we showcase two main functional-ities: (1) text traffic alert validation based on traffic condition infor-mation derived from traffic camera images and (2) traffic incident correlation based on spatio-temporal proximity of different inci-dent types (e.g., accidents and heavy traffic). Despite the fact that the images are sparse (available every three minutes), the regularity makes it possible to validate whether a text traffic alert is outdated or not, and to more accurately estimate the time elapsed and total incident time. Multiple traffic incidents can be grouped together as a single event based on the traffic incident correlation to reduce information redundancy. Such enhanced real-time traffic informa-tion enables PETRINA to offer services such as dynamic routing with traffic incident advices, spatiotemporal traffic incident visual analytics, and congestion analysis.
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U2 - 10.1145/2666310.2666379
DO - 10.1145/2666310.2666379
M3 - Conference contribution
AN - SCOPUS:84961207552
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 601
EP - 604
BT - 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2014
A2 - Schneider, Markus
A2 - Gertz, Michael
A2 - Huang, Yan
A2 - Sankaranarayanan, Jagan
A2 - Krumm, John
PB - Association for Computing Machinery
T2 - 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2014
Y2 - 4 November 2014 through 7 November 2014
ER -