Abstract
Traffic behavior analysis based on moving object trajectories is a basic technique for intelligent transportation system (ITS) applications like traffic control. In this chapter, we firstly propose a new model for objects moving on dynamic transportation networks (MODTN). In the MODTN system, moving objects are modeled as moving graph points that move only within predefined transportation networks. To express general events of the system, such as traffic jams, temporary constructions, and insertion and deletion of junctions or routes, the underlying transportation networks are modeled as dynamic graphs so that the state and the topology of the graph system at any time instant can be tracked and queried. Based on this model, we secondly introduce a real-time traffic flow statistical analysis method called NMOD-TFSA. By analyzing the spatio-temporal trajectories of moving objects, NMOD-TFSA can get the real-time traffic parameter values of the transportation network.
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References
Benkert M, Gudmundsson J, Hubner F, Wolle T (2008) Reporting flock patterns. Comput Geom Theory Appl 41(3):111–125 (2008)
Dieker S, Güting RH (2000) Plug and play with query algebras: SECONDO. A generic DBMS development environment. In: Proceedings of international database engineering and applications symposium (IDEAS 2000), Yokohoma, pp 380–392
Ding Z, Güting RH (2004) Managing moving objects on dynamic transportation networks. In: Proceedings of the 16th international conference on scientific and statistical database management (SSDBM 2004), Santorini Island, pp 287–296
Ding Z, Güting RH (2004) Modeling temporally variable transportation networks. In: Proceedings of the 9th international conference on database systems for advanced applications (DASFAA 2004), Jeju Island, pp 154–168
Ding Z, Huang G (2009) Real-time traffic flow statistical analysis based on network-constrained moving object trajectories. In: Proceedings of the 20th international workshop on database and expert systems applications (DEXA 2009), Linz, pp 173–183
Fouladvand M, Darooneh AH (2005) Statistical analysis of floating-car data: an empirical study. Eur Phys J 47(2):319–328
Gidofalvi G, Pedersen TB (2009) Mining long, sharable patterns in trajectories of moving objects. Geoinformatica 13(1):27–55
Güting RH, De Almeida VT, Ding Z (2006) Modeling and querying moving objects in networks. VLDB J 15(2):165–190
Lahrmann H (2007) Floating car data for traffic monitoring. In: Proceedings of the i2TERN conference, Aalborg, June 2007
Lee JG, Han J, Li X, Gonzalez H (2008) TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering. Proc VLDB Endow 1(1):441–459. (PVLDB 2008)
Lee J, Han J, Li X (2008) Trajectory outlier detection: a partition-and-detect framework. In: Proceedings of the 24th international conference on data engineering (ICDE 2008), Cancun, pp 140–149
Li X, Han J, Lee JG, Gonzalez H (2007) Traffic density-based discovery of hot routes in road networks. In: Proceedings of international conference on advances in spatial and temporal databases (SSTD 2007), Boston, pp 441–459
Lo CH, Peng WC, Chen CW et al (2008) CarWeb: a traffic data collection platform. In: Proceedings of the 9th international conference on mobile data management (MDM 2008), Beijing, pp 221–222
Monreale A, Pinelli F, Trasarti R, Giannotti F (2009) Wherenext: a location predictor on trajectory pattern mining. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery & data mining (KDD 2009), Paris, pp 637–646
Park CG, Oh J, Kim S (1998) Determination of optimal number of probe vehicles for real-time traffic flow information. In: Proceedings of the 5th world congress on intelligent transport systems (ITS 1998), Seoul, p 4088
Sarvi M, Horiguchi R, Kuwahara M, Shimizu Y (2003) A methodology to identify traffic condition using intelligent probe vehicles. In: Proceedings of the 10th world congress on intelligent transport systems (ITS 2003), Madrid, pp 17–21
Speicys L, Jensen CS, Kligys A (2003) Computational data modeling for network-constrained moving objects. In: Proceedings of the 11th ACM international symposium on advances in geographic information systems (GIS 2003), Louisiana, pp 118–125
Yoon J, Noble B, Liu M (2007) Surface street traffic estimation. In: Proceedings of the 5th international conference on mobile systems, applications and services, San Juan, pp 220–232
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© 2014 Tsinghua University Press, Beijing and Springer-Verlag Berlin Heidelberg
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Meng, X., Ding, Z., Xu, J. (2014). Statistical Analysis on Moving Object Trajectories. In: Moving Objects Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38276-5_9
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DOI: https://doi.org/10.1007/978-3-642-38276-5_9
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