SMoT+NCS: Algorithm for Detecting Non-Continuous Stops

Several algorithms have been proposed in the last years for discovering stops in trajectories of moving objects. Some methods consider as stops the subtrajectories that i) have speed lower than the average trajectory speed, ii) present significant direction changes, iii) have gaps, or iv) intersect a given spatial region. In these approaches a time constraint should be met for the subtrajectory to be considered as a stop, and this constraint is absolute (it is met or not). Indeed, these approaches consider stops as a continuous subtrajectory. In this paper, we show that for several application domains the stops do not need to be continuous, and the time constraint should be relaxed. In summary, we present the definitions of non-continuous stops and present an algorithm to discover a new kind of stops. We evaluate the proposed algorithm with a running example and real trajectory data, comparing it to the most similar approach in the literature, the SMoT algorithm.

[1]  Valéria Cesário Times,et al.  DB-SMoT: A direction-based spatio-temporal clustering method , 2010, 2010 5th IEEE International Conference Intelligent Systems.

[2]  Ross Purves,et al.  How fast is a cow? Cross‐Scale Analysis of Movement Data , 2011, Trans. GIS.

[3]  Stefano Spaccapietra,et al.  Trajectory Ontologies and Queries , 2008 .

[4]  Vania Bogorny,et al.  CONSTAnT – A Conceptual Data Model for Semantic Trajectories of Moving Objects , 2014, Trans. GIS.

[5]  Francisco Javier Moreno Arboleda,et al.  SMoT+: Extending the SMoT Algorithm for Discovering Stops in Nested Sites , 2014, Comput. Informatics.

[6]  Jonathan Leape The London Congestion Charge , 2006 .

[7]  M. Walker,et al.  Tracking fish using 'buoy-based' GPS telemetry , 2009 .

[8]  Stefano Secci,et al.  Estimating human trajectories and hotspots through mobile phone data , 2014, Comput. Networks.

[9]  Cyril Ray,et al.  Semantic management of moving objects: A vision towards smart mobility , 2015, Expert Syst. Appl..

[10]  Stefano Spaccapietra,et al.  Semantic trajectories modeling and analysis , 2013, CSUR.

[11]  Chinya V. Ravishankar,et al.  Finding Regions of Interest from Trajectory Data , 2011, 2011 IEEE 12th International Conference on Mobile Data Management.

[12]  Fabio Porto,et al.  A conceptual view on trajectories , 2008, Data Knowl. Eng..

[13]  Vania Bogorny,et al.  A clustering-based approach for discovering interesting places in trajectories , 2008, SAC '08.

[14]  Ralf Hartmut Güting,et al.  Moving Objects Databases , 2005 .

[15]  Joachim Gudmundsson,et al.  Finding Popular Places , 2007, Int. J. Comput. Geom. Appl..

[16]  Seung-Bum Ahn CONTAINER TRACKING AND TRACING SYSTEM TO ENHANCE GLOBAL VISIBILITY , 2005 .

[17]  Vania Bogorny,et al.  A model for enriching trajectories with semantic geographical information , 2007, GIS.

[18]  Joachim Gudmundsson,et al.  Detecting Regular Visit Patterns , 2009, Algorithmica.

[19]  Joachim Gudmundsson,et al.  Algorithms for hotspot computation on trajectory data , 2013, SIGSPATIAL/GIS.

[20]  Robert Weibel,et al.  Discovering relative motion patterns in groups of moving point objects , 2005, Int. J. Geogr. Inf. Sci..