Mining periodic behaviors for moving objects

Abstract

Periodicity is a frequently happening phenomenon for moving objects. Finding periodic behaviors is essential to understanding object movements. However, periodic behaviors could be complicated, involving multiple interleaving periods, partial time span, and spatiotemporal noises and outliers.In this paper, we address the problem of mining periodic behaviors for moving objects. It involves two sub-problems: how to detect the periods in complex movement, and how to mine periodic movement behaviors. Our main assumption is that the observed movement is generated from multiple interleaved periodic behaviors associated with certain reference locations. Based on this assumption, we propose a twostage algorithm, Periodica, to solve the problem. At the first stage, the notion of reference spot is proposed to capture the reference locations. Through reference spots, multiple periods in the movement can be retrieved using a method that combines Fourier transform and autocorrelation. At the second stage, a probabilistic model is proposed to characterize the periodic behaviors. For a specific period, periodic behaviors are statistically generalized from partial movement sequences through hierarchical clustering. Empirical studies on both synthetic and real data sets demonstrate the effectiveness of our method.

Intersection Detection Based on Qualitative Spatial Reasoning on Stopping Point Clusters

“…Such a segmentation has also been used for discovering interesting places in single trajectories (Palma et al, 2008) and for enriching trajectories with semantic geographical information (Alvares et al, 2007). Our scope is to find patterns of vehicles' movement and more specifically to detect reference spots 1 (Li et al, 2010,) by observing where vehicles stop, so the extraction of the "interesting places" here is defined by massively observing the same behaviour at the same location as opposed to Palma et al (2008) where interesting locations have a more "personalised" character 2 .…”

SeTraStream: Semantic-Aware Trajectory Construction over Streaming Movement Data

“…The focus of trajectory data management includes building data models, query languages and implementation aspects, such as efficient indexing, query processing, and optimization techniques [12] [25]; whilst the analysis aims at trajectory data mining including issues like classification, clustering, outlier detection, as well as trajectory pattern discovery (e.g. sequential, periodic and convoy patterns) [9][13] [20] [21].…”

Lattitude, Longitude, and Beyond : Mining Mobile Object's Behavior

“…Existing solutions for pattern mining from mobility data can be divided into solutions addressing either frequent [163][164][165][166][167] or periodic pattern mining [162,168,169]. The former techniques focus on the "number of times" a pattern is repeated in limited duration (more representing the importance of a behavior in the number of time it repeats), while the latter focus on both the "number of times" a pattern is repeated and "the temporal trend" by which it is repeated.…”

“…Recently, mining periodic patterns from mobility data has also received attention [162,168,169]. Use of signal processing techniques such as Fourier and wavelet transform was proposed in [174,175].…”

“…Apart from uncertainties associated with mobility data (such as noise and missing samples), which make mining periodic patterns challenging, online extraction of patterns from streaming mobility data is difficult due to availability of limited processing and memory resources. The problem of identification of periodic behavioral patterns has been studied previously [162]. What distinguishes the work presented in this chapter from the existing research, however, is its focus on identification of periodic patterns from streaming mobility data through a light, and accurate technique.…”