Towards Distributed Convoy Pattern Mining

Mining movement data to reveal interesting behavioral patterns has gained attention in recent years. One such pattern is the convoy pattern which consists of at least m objects moving together for at least k consecutive time instants where m and k are user-defined parameters. Existing algorithms for detecting convoy patterns, however do not scale to real-life dataset sizes. Therefore a distributed algorithm for convoy mining is inevitable. In this paper, we discuss the problem of convoy mining and analyze different data partitioning strategies to pave the way for a generic distributed convoy pattern mining algorithm.Comment: SIGSPATIAL'15 November 03-06, 2015, Bellevue, WA, US

Co-Clustering Network-Constrained Trajectory Data

Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajectories of vehicles whose movement is restricted by the underlying road network. We model relations between these trajectories and road segments as a bipartite graph and we try to cluster its vertices. We demonstrate our approaches on synthetic data and show how it could be useful in inferring knowledge about the flow dynamics and the behavior of the drivers using the road network

Trayectorias con ruta predefinida en una bodega de datos: un caso de estudio sobre transporte público

Hoy, gracias a los sistemas de posicionamiento global y dispositivos móviles equipados con sensores se puede capturar información acerca de la trayectoria seguida por objetos móviles tales como personas, animales, vehículos, entre otros. En este artículo se examinan en particular las trayectorias seguidas por objetos móviles en una ruta predefinida, por ejemplo un sistema de transporte público en el que los vehículos deben recorrer una ruta preestablecida. Se presenta un caso de estudio donde este tipo de trayectorias se incorporan como un sujeto de análisis en una bodega de datos

Inferring Unusual Crowd Events From Mobile Phone Call Detail Records

The pervasiveness and availability of mobile phone data offer the opportunity of discovering usable knowledge about crowd behaviors in urban environments. Cities can leverage such knowledge in order to provide better services (e.g., public transport planning, optimized resource allocation) and safer cities. Call Detail Record (CDR) data represents a practical data source to detect and monitor unusual events considering the high level of mobile phone penetration, compared with GPS equipped and open devices. In this paper, we provide a methodology that is able to detect unusual events from CDR data that typically has low accuracy in terms of space and time resolution. Moreover, we introduce a concept of unusual event that involves a large amount of people who expose an unusual mobility behavior. Our careful consideration of the issues that come from coarse-grained CDR data ultimately leads to a completely general framework that can detect unusual crowd events from CDR data effectively and efficiently. Through extensive experiments on real-world CDR data for a large city in Africa, we demonstrate that our method can detect unusual events with 16% higher recall and over 10 times higher precision, compared to state-of-the-art methods. We implement a visual analytics prototype system to help end users analyze detected unusual crowd events to best suit different application scenarios. To the best of our knowledge, this is the first work on the detection of unusual events from CDR data with considerations of its temporal and spatial sparseness and distinction between user unusual activities and daily routines.Comment: 18 pages, 6 figure

Key concepts of group pattern discovery algorithms from spatio-temporal trajectories

Over the years, the increasing development of location acquisition devices have generated a significant amount of spatio-temporal data. This data can be further analysed in search for some interesting patterns, new information, or to construct predictive models such as next location prediction. The goal of this paper is to contribute to the future research and development of group pattern discovery algorithms from spatio-temporal data by providing an insight into algorithms design in this research area which is based on a comprehensive classification of state-of-the-art models. This work includes static, big data as well as data stream processing models which to the best of authors’knowledge is the first attempt of presenting them in this context.Furthermore, the currently available surveys and taxonomies in this research area do not focus on group pattern mining algorithms nor include the state-of-the-art models. The authors conclude with the proposal of a conceptual model of Universal,Streaming, Distributed and Parameter-light (UDSP) algorithm that addresses current challenges in this research area

Clustering par optimisation de la modularité pour trajectoires d'objets mobiles

National audienceAnalysis and data mining of moving objects trajectories have gained a considerable amount of interest in the last few years. In this article, we present a clustering approach tailored for trajectories of vehicles moving on a road network. First, we introduce a similarity measure that makes it possible to compare such trajectories while taking into account the constraints of the underlying network. Then, this measure is used to construct a graph that models the interactions among the trajectories w.r.t. their similarity. A community detection algorithm based on modularity optimization is applied to the graph in order to discover groups of trajectories that behaved similarly and that moved along the same portions of the road network. We implemented the proposed approach and tested it on multiple synthetic datasets in order to show its feasibility and its efficiency

DoSTra: Discovering common behaviors of objects using the duration of staying on each location of trajectories

Since semantic trajectories can discover more semantic meanings of a user\u27s interests without geographic restrictions, research on semantic trajectories has attracted a lot of attentions in recent years. Most existing work discover the similar behavior of moving objects through analysis of their semantic trajectory pattern, that is, sequences of locations. However, this kind of trajectories without considering the duration of staying on a location limits wild applications. For example, Tom and Anne have a common pattern of Home→Restaurant → Company → Restaurant, but they are not similar, since Tom works at Restaurant, sends snack to someone at Company and return to Restaurant while Anne has breakfast at Restaurant, works at Company and has lunch at Restaurant. If we consider duration of staying on each location we can easily to differentiate their behaviors. In this paper, we propose a novel approach for discovering common behaviors by considering the duration of staying on each location of trajectories (DoSTra). Our approach can be used to detect the group that has similar lifestyle, habit or behavior patterns and predict the future locations of moving objects. We evaluate the experiment based on synthetic dataset, which demonstrates the high effectiveness and efficiency of the proposed method