Spatio-temporal trajectory analysis of mobile objects following the same itinerary

More and more mobile objects are now equipped with sensors allowing real time monitoring of their movements. Nowadays, the data produced by these sensors can be stored in spatio-temporal databases. The main goal of this article is to perform a data mining on a huge quantity of mobile object’s positions moving in an open space in order to deduce its behaviour. New tools must be defined to ease the detection of outliers. First of all, a zone graph is set up in order to define itineraries. Then, trajectories of mobile objects following the same itinerary are extracted from the spatio-temporal database and clustered. A statistical analysis on this set of trajectories lead to spatio-temporal patterns such as the main route and spatio-temporal channel followed by most of trajectories of the set. Using these patterns, unusual situations can be detected. Furthermore, a mobile object’s behaviour can be defined by comparing its positions with these spatio-temporal patterns. In this article, this technique is applied to ships’ movements in an open maritime area. Unusual behaviours such as being ahead of schedule or delayed or veering to the left or to the right of the main route are detected. A case study illustrates these processes based on ships’ positions recorded during two years around the Brest area. This method can be extended to almost all kinds of mobile objects (pedestrians, aircrafts, hurricanes, ...) moving in an open area.

[1]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

[2]  Dino Pedreschi,et al.  Time-focused clustering of trajectories of moving objects , 2006, Journal of Intelligent Information Systems.

[3]  Raymond T. Ng,et al.  Distance-based outliers: algorithms and applications , 2000, The VLDB Journal.

[4]  Sridhar Ramaswamy,et al.  Efficient algorithms for mining outliers from large data sets , 2000, SIGMOD '00.

[5]  S. Zlatanova,et al.  Geomatics Solutions for Disaster Management , 2010 .

[6]  D. Pedreschi,et al.  Time-focused density-based clustering of trajectories of moving objects , 1986 .

[7]  Christos Faloutsos,et al.  LOCI: fast outlier detection using the local correlation integral , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[8]  Dino Pedreschi,et al.  Trajectory pattern mining , 2007, KDD '07.

[9]  Sami Faïz,et al.  Clustering Algorithm for Network Constraint Trajectories , 2008, SDH.

[10]  Ralf Hartmut Güting Dr.rer.nat An introduction to spatial database systems , 2005, The VLDB Journal.

[11]  Nirvana Meratnia,et al.  Spatiotemporal Compression Techniques for Moving Point Objects , 2004, EDBT.

[12]  Philip S. Yu,et al.  Outlier detection for high dimensional data , 2001, SIGMOD '01.

[13]  Jae-Gil Lee,et al.  Trajectory Outlier Detection: A Partition-and-Detect Framework , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[14]  W. Cao,et al.  The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVIII-4/C7 CLASSIFICATION OF HIGH RESOLUTION OPTICAL AND SAR FUSION IMAGE USING FUZZY KNOWLEDGE AND OBJECT-ORIENTED PARADIGM , 2010 .

[15]  Josef Kittler Web and Wireless Geographical Information Systems , 2012, Lecture Notes in Computer Science.

[16]  Ralf Hartmut Güting,et al.  An introduction to spatial database systems , 1994, VLDB J..

[17]  O. Baud,et al.  Trajectory Comparison for Civil Aircraft , 2007, 2007 IEEE Aerospace Conference.

[18]  Ouri Wolfson,et al.  Spatio-temporal data reduction with deterministic error bounds , 2003, DIALM-POMC '03.

[19]  Jörg Sander,et al.  A Trajectory Splitting Model for Efficient Spatio-Temporal Indexing , 2005, VLDB.

[20]  Vic Barnett,et al.  Outliers in Statistical Data , 1980 .