Skip to main content
Top

2020 | OriginalPaper | Chapter

Temporal Similarity of Trajectories in Graphs

Author : Shima Moghtasedi

Published in: Similarity Search and Applications

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The analysis of similar trajectories in a network provides useful information for different applications. In this study, we are interested in algorithms to efficiently retrieve similar trajectories. Many studies have focused on retrieving similar trajectories by extracting the geometrical and geographical information of trajectories. We provide a similarity function by making use of both the temporal aspect of trajectories and the structure of the underlying network. We propose an approximation technique offering the top-k similar trajectories with respect to a query in a specified time interval in an efficient way. We also investigate how our idea can be applied to similar behavior of the tourists, so as to offer a high-quality prediction of their next movements.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Boukhechba, M., Bouzouane, A., Gaboury, S., Gouin-Vallerand, C., Giroux, S., Bouchard, B.: Prediction of next destinations from irregular patterns. J. Ambient Intell. Hum. Comput. 9, 1345–1357 (2017)CrossRef Boukhechba, M., Bouzouane, A., Gaboury, S., Gouin-Vallerand, C., Giroux, S., Bouchard, B.: Prediction of next destinations from irregular patterns. J. Ambient Intell. Hum. Comput. 9, 1345–1357 (2017)CrossRef
2.
go back to reference De Almeida, V.T., Güting, R.H.: Indexing the trajectories of moving objects in networks. GeoInformatica 9(1), 33–60 (2005)CrossRef De Almeida, V.T., Güting, R.H.: Indexing the trajectories of moving objects in networks. GeoInformatica 9(1), 33–60 (2005)CrossRef
3.
go back to reference Grossi, R., Marino, A., Moghtasedi, S.: Finding structurally and temporally similar trajectories in graphs. In: 18th International Symposium on Experimental Algorithms (SEA 2020). Schloss Dagstuhl-Leibniz-Zentrum für Informatik (2020) Grossi, R., Marino, A., Moghtasedi, S.: Finding structurally and temporally similar trajectories in graphs. In: 18th International Symposium on Experimental Algorithms (SEA 2020). Schloss Dagstuhl-Leibniz-Zentrum für Informatik (2020)
5.
go back to reference Luo, W., Tan, H., Chen, L., Ni, L.M.: Finding time period-based most frequent path in big trajectory data. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 713–724. ACM (2013) Luo, W., Tan, H., Chen, L., Ni, L.M.: Finding time period-based most frequent path in big trajectory data. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 713–724. ACM (2013)
6.
go back to reference Moghtasedi, S.: Time-based similar trajectories on graphs. In: ICTCS, pp. 82–86 (2018) Moghtasedi, S.: Time-based similar trajectories on graphs. In: ICTCS, pp. 82–86 (2018)
7.
go back to reference Monreale, A., Pinelli, F., Trasarti, R., Giannotti, F.: WhereNext: a location predictor on trajectory pattern mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 637–646. ACM (2009) Monreale, A., Pinelli, F., Trasarti, R., Giannotti, F.: WhereNext: a location predictor on trajectory pattern mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 637–646. ACM (2009)
8.
go back to reference Muntean, C.I., Nardini, F.M., Silvestri, F., Baraglia, R.: On learning prediction models for tourists paths. ACM Trans. Intell. Syst. Technol. (TIST) 7(1), 8 (2015) Muntean, C.I., Nardini, F.M., Silvestri, F., Baraglia, R.: On learning prediction models for tourists paths. ACM Trans. Intell. Syst. Technol. (TIST) 7(1), 8 (2015)
9.
go back to reference Popa, I.S., Zeitouni, K., Oria, V., Barth, D., Vial, S.: PariNet: a tunable access method for in-network trajectories. In: 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010), pp. 177–188. IEEE (2010) Popa, I.S., Zeitouni, K., Oria, V., Barth, D., Vial, S.: PariNet: a tunable access method for in-network trajectories. In: 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010), pp. 177–188. IEEE (2010)
10.
go back to reference Shang, S., Chen, L., Wei, Z., Jensen, C.S., Zheng, K., Kalnis, P.: Trajectory similarity join in spatial networks. Proc. VLDB Endow. 10(11), 1178–1189 (2017)CrossRef Shang, S., Chen, L., Wei, Z., Jensen, C.S., Zheng, K., Kalnis, P.: Trajectory similarity join in spatial networks. Proc. VLDB Endow. 10(11), 1178–1189 (2017)CrossRef
11.
go back to reference Shang, S., Ding, R., Yuan, B., Xie, K., Zheng, K., Kalnis, P.: User oriented trajectory search for trip recommendation. In: Proceedings of the 15th International Conference on Extending Database Technology, pp. 156–167 (2012) Shang, S., Ding, R., Yuan, B., Xie, K., Zheng, K., Kalnis, P.: User oriented trajectory search for trip recommendation. In: Proceedings of the 15th International Conference on Extending Database Technology, pp. 156–167 (2012)
13.
go back to reference Shima, M., Cristina Ioana, M., Franco Maria, N., Roberto, G., Andrea, M.: High-quality prediction of tourist movements using temporal trajectories in graphs (under submission) Shima, M., Cristina Ioana, M., Franco Maria, N., Roberto, G., Andrea, M.: High-quality prediction of tourist movements using temporal trajectories in graphs (under submission)
14.
go back to reference Tiakas, E., Papadopoulos, A.N., Nanopoulos, A., Manolopoulos, Y., Stojanovic, D., Djordjevic-Kajan, S.: Trajectory similarity search in spatial networks, pp. 185–192. IEEE (2006) Tiakas, E., Papadopoulos, A.N., Nanopoulos, A., Manolopoulos, Y., Stojanovic, D., Djordjevic-Kajan, S.: Trajectory similarity search in spatial networks, pp. 185–192. IEEE (2006)
16.
go back to reference Ying, H., et al.: Time-aware metric embedding with asymmetric projection for successive poi recommendation. World Wide Web 22(5), 2209–2224 (2019)CrossRef Ying, H., et al.: Time-aware metric embedding with asymmetric projection for successive poi recommendation. World Wide Web 22(5), 2209–2224 (2019)CrossRef
Metadata
Title
Temporal Similarity of Trajectories in Graphs
Author
Shima Moghtasedi
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-60936-8_32