To study the similarity between moving object trajectories is important in many applications, e.g., to find the clusters of moving objects which share the same moving pattern, and infer the future locations of a moving object from its similar trajectories. To define the similarity between moving objects is a challenging task, since not only their locations change but also their speed and semantic features vary. In this paper, we propose a novel approach to measure the similarity between trajectories. The similarity is defined based on both geographic and semantic features of movements. Our approach can be used to detect trajectory clusters and infer future locations of moving objects.
Abstract-Moving objects in the physical world usually generate many uncertain trajectories for some reasons such as the consideration of energy consumption, leaving the route passing two consecutive sampling points unknown. While such trajectories imply rich knowledge about the mobility of moving objects, they are less useful individually. This paper introduces an online trip planning system that mines collective knowledge (i.e., most possible routes between given locations) from massive uncertain trajectories following a paradigm of "uncertain+uncertain→certain". This system first builds a routable graph from uncertain trajectories, and then answers a user's online query (a sequence of point locations) by searching top-k routes on the graph. Two large-scale datasets consisting of "check-in" records from FourSquare and a trajectory dataset of taxis have been used to evaluate our system. As a result, our system provides a user with effective routes according to the user's query efficiently.
Abstract. Many geographical applications need to model spatial phenomena with vague or indeterminate boundaries and interiors. A popular paradigm adopted by the GIS community for this task at the modeling level is fuzzy set theory. A spatial object is fuzzy if locations exist that cannot be assigned completely to the object or to its complement. In previous work, we have proposed an abstract data model of fuzzy spatial data types for fuzzy points, fuzzy lines, and fuzzy regions to represent the indeterminacy of spatial data. This paper focuses on the problem of finding an appropriate implementation approach to fuzzy regions. The idea is to approximate a fuzzy region by a so-called plateau region consisting of a finite number of crisp regions that are all adjacent or disjoint to each other and associated with different membership values determining the degree of belonging to the fuzzy region. Geometric union, geometric intersection, and geometric difference on fuzzy regions are expressed by corresponding operations on the underlying crisp regions. We leverage that several implementations are already available for crisp regions.
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