Vandana Pursnani Janeja
ABSTRACT
Often, it is required to identify anomalous windows along a linear path that reflect unusual rate of occurrence of a specific event of interest. Such examples include: determination of places with high number of occurrences of road accidents along a highway, leaks in natural gas transmission pipelines, pedestrian fatalities on roads, etc. In this paper, we propose a <u>L</u>inear <u>S</u>emantic <u>S</u>can <u>S</u>tatistic (LS3) approach to identify such anomalous windows along a linear path. We assume that a linear path is comprised of one-dimensional spatial locations called markers, where each marker is associated with a set of structural and behavioral attributes. We divide the linear path into linear semantic segments such that each semantic segment contains markers associated with similar structural attributes. Our goal is to identify the windows within a semantic segment whose behavioral attributes are anomalous in some sense. We accomplish this by applying the scan statistic to the behavioral attributes of the markers. We have implemented our approach by considering the real datasets of certain highways in New Jersey, USA. Our results validate that LS3 is effective in identifying high traffic accident windows.
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