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sparse (available every three minutes), the regularity makes it possible to validate whether a text traffic alert is outdated or not, and to more accurately estimate the time elapsed and total incident time.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.