Discovering anomalous events from urban informatics data
By Kasthuri Jayarajah, Vigneshwaran Subbaraju, Dulanga Weerakoon, Archan Misra, L. Tam, Noel Athaide. 2017
6 citations recorded by the source corpus.
Singapore’s “smart city” agenda is driving the government to provide public access to a broader variety of urban informatics sources, such as images from traffic cameras and information about buses servicing different bus stops. Such informatics data serves as probes of evolving conditions at different spatiotemporal scales. This paper explores how such multi-modal informatics data can be used to establish the normal operating conditions at different city locations, and then apply appropriate outlier-based analysis techniques to identify anomalous events at these selected locations. We will introduce the overall architecture of sociophysical analytics, where such infrastructural data sources can be combined with social media analytics to not only detect such anomalous events, but also localize and explain them. Using the annual Formula-1 race as our candidate event, we demonstrate a key difference between the discriminative capabilities of different sensing modes: while social media streams provide discriminative signals during or prior to the occurrence of such an event, urban informatics data can often reveal patterns that have higher persistence, including before and after the event. In particular, we shall demonstrate how combining data from (i) publicly available Tweets, (ii) crowd levels aboard buses, and (iii) traffic cameras can help identify the Formula-1 driven anomalies, across different spatiotemporal boundaries.
Subfields: Artificial Intelligence, Epidemiology, Transportation
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In Artificial Intelligence
Citations in the corpus
- A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion (2019, background): “5 - - - - Y N N [112] Traffic monitoring Camera and loop data Deterministic fusion Real-world monitor data N N 100 - - - - Y N N [113] Incident detection Social feeds and Decision fusion real-world Y N 90 - - - - Y N N”