Skip to main content
SpringerLink
Log in

TRAJEDI: Trajectory Dissimilarity

  • Chapter
  • First Online:
Sustainable Interdependent Networks II

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 186))

Abstract

The vast increase in our ability to obtain and store trajectory data necessitates trajectory analytics techniques to extract useful information from this data. In fact, trajectory analysis is an essential function in intelligent transportation systems (ITS) and by applying it for the spatial trajectory data, a wide range of transportation problems can be solved. Pair-wise distance functions are a foundation building block for common operations on trajectory datasets including constrained SELECT queries, k-nearest neighbors, and similarity and diversity algorithms. The accuracy and performance of these operations depend heavily on the speed and accuracy of the underlying trajectory distance function, which is in turn affected by trajectory calibration. Current methods either require calibrated data or perform calibration of the entire relevant dataset first, which is expensive and time consuming for large datasets. We present TRAJEDI, a calibration aware pair-wise distance calculation scheme that outperforms naive approaches while preserving accuracy. We also provide analyses of parameter tuning to trade off between speed and accuracy. Our scheme is usable with any diversity, similarity or k-nearest neighbor algorithm.

Kenrick Fernande, Pedram Gharani, and Vineet Raghu are contributed equally to this work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Amini, M. H., Boroojeni, K. G., Iyengar, S., Blaabjerg, F., Pardalos, P. M., & Madni, A. M. (2018). A panorama of future interdependent networks: From intelligent infrastructures to smart cities. In Sustainable interdependent networks (pp. 1–10). Cham: Springer.

    Chapter  Google Scholar 

  2. Amini, M. H., Boroojeni, K. G., Iyengar, S., Pardalos, P. M., Blaabjerg, F., & Madni, A. M. (2018). Sustainable interdependent networks: From theory to application. Cham: Springer.

    Book  Google Scholar 

  3. Bao, J., Zheng, Y., & Mokbel, M. F. (2012). Location-based and preference-aware recommendation using sparse geo-social networking data. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems (pp. 199–208). New York: ACM.

    Google Scholar 

  4. Bogorny, V., Kuijpers, B., & Alvares, L. O. (2009). ST-DMQL: A semantic trajectory data mining query language. International Journal of Geographical Information Science, 23(10), 1245–1276.

    Article  Google Scholar 

  5. Chen, L., Özsu, M. T., & Oria, V. (2005). Robust and fast similarity search for moving object trajectories. In Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data (pp. 491–502). New York: ACM.

    Chapter  Google Scholar 

  6. Chen, Z., Shen, H. T., & Zhou, X. (2011). Discovering popular routes from trajectories. In 2011 IEEE 27th International Conference on Data Engineering (ICDE) (pp. 900–911). Piscataway: IEEE.

    Chapter  Google Scholar 

  7. Chen, Z., Shen, H. T., Zhou, X., Zheng, Y., & Xie, X. (2010). Searching trajectories by locations: An efficiency study. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data (pp. 255–266). New York: ACM.

    Chapter  Google Scholar 

  8. Gharani, P., & Karimi, H. A. (2017). Context-aware obstacle detection for navigation by visually impaired. Image and Vision Computing, 64, 103–115.

    Article  Google Scholar 

  9. Gharani, P., Suffoletto, B., Chung, T., & Karimi, H. A. (2017). An artificial neural network for movement pattern analysis to estimate blood alcohol content level. Sensors, 17(12), 2897.

    Article  Google Scholar 

  10. Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F., Renso, C., Rinzivillo, S., et al. (2011). Unveiling the complexity of human mobility by querying and mining massive trajectory data. The VLDB Journal: The International Journal on Very Large Data Bases, 20(5), 695–719.

    Article  Google Scholar 

  11. Giannotti, F., Nanni, M., Pinelli, F., & Pedreschi, D. (2007). Trajectory pattern mining. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 330–339). New York: ACM.

    Chapter  Google Scholar 

  12. Javadi, S. H. S., Gharani, P., & Khadivi, S. (2018). Detecting community structure in dynamic social networks using the concept of leadership. In Sustainable interdependent networks (pp. 97–118). Cham: Springer.

    Chapter  Google Scholar 

  13. Liu, S., & Meng, X. (2015). A location-based business information recommendation algorithm. Mathematical Problems in Engineering, 2015, Article ID 345480.

    Google Scholar 

  14. Mohammadi, A., & Dehghani, M. J. (2014). Spectrum allocation using fuzzy logic with optimal power in wireless network. In 2014 4th International eConference on Computer and Knowledge Engineering (ICCKE) (pp. 532–536). Piscataway: IEEE.

    Chapter  Google Scholar 

  15. Potamias, M., Patroumpas, K., & Sellis, T. (2006). Sampling trajectory streams with spatiotemporal criteria. In 2006 18th International Conference on Scientific and Statistical Database Management (pp. 275–284). Piscataway: IEEE.

    Google Scholar 

  16. Rao, K. V., Govardhan, A., & Rao, K. C. (2012). Spatiotemporal data mining: Issues, tasks and applications. International Journal of Computer Science and Engineering Survey, 3(1), 39.

    Article  Google Scholar 

  17. Shen, Y., Kwan, M.-P., & Chai, Y. (2013). Investigating commuting flexibility with GPS data and 3d geovisualization: A case study of Beijing, China. Journal of Transport Geography, 32, 1–11.

    Article  Google Scholar 

  18. Su, H., Zheng, K., Huang, J., Wang, H., & Zhou, X. (2015). Calibrating trajectory data for spatio-temporal similarity analysis. The VLDB Journal, 24(1), 93–116.

    Article  Google Scholar 

  19. Suffoletto, B., Gharani, P., Chung, T., & Karimi, H. (2018). Using phone sensors and an artificial neural network to detect gait changes during drinking episodes in the natural environment. Gait & Posture, 60, 116–121.

    Article  Google Scholar 

  20. Vlachos, M., Kollios, G., & Gunopulos, D. (2002). Discovering similar multidimensional trajectories. In 2002 Proceedings 18th International Conference on Data Engineering (pp. 673–684). Piscataway: IEEE.

    Google Scholar 

  21. Wang, Y., Zheng, Y., & Xue, Y. (2014). Travel time estimation of a path using sparse trajectories. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 25–34). New York: ACM.

    Google Scholar 

  22. Yin, Z., Cao, L., Han, J., Luo, J., & Huang, T. S. (2011). Diversified trajectory pattern ranking in geo-tagged social media. In SDM (pp. 980–991). Philadelphia: SIAM.

    Google Scholar 

  23. Yu, X., Pu, K. Q., & Koudas, N. (2005). Monitoring k-nearest neighbor queries over moving objects. In Proceedings 21st International Conference on Data Engineering. ICDE 2005 (pp. 631–642). Piscataway: IEEE.

    Google Scholar 

  24. Yuan, J., Zheng, Y., Xie, X., & Sun, G. (2011). Driving with knowledge from the physical world. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 316–324). New York: ACM.

    Chapter  Google Scholar 

  25. Yuan, J., Zheng, Y., Xie, X., & Sun, G. (2013). T-drive: Enhancing driving directions with taxi drivers’ intelligence. IEEE Transactions on Knowledge and Data Engineering, 25(1), 220–232.

    Article  Google Scholar 

  26. Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., et al. (2010). T-drive: Driving directions based on taxi trajectories. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 99–108). New York: ACM.

    Google Scholar 

  27. Yuan, J., Zheng, Y., Zhang, L., Xie, X., & Sun, G. (2011). Where to find my next passenger. In Proceedings of the 13th International Conference on Ubiquitous Computing (pp. 109–118). New York: ACM.

    Google Scholar 

  28. Yuan, N. J., Zheng, Y., & Xie, X. (2012). Segmentation of urban areas using road networks. MSR-TR-2012-65, Tech. Rep.

    Google Scholar 

  29. Zheng, Y., Li, Q., Chen, Y., Xie, X., & Ma, W.-Y. (2008). Understanding mobility based on GPS data. In Proceedings of the 10th International Conference on Ubiquitous Computing (pp. 312–321). New York: ACM.

    Google Scholar 

  30. Zheng, Y., Liu, L., Wang, L., & Xie, X. (2008). Learning transportation mode from raw GPS data for geographic applications on the web. In Proceedings of the 17th International Conference on World Wide Web (pp. 247–256). New York: ACM.

    Chapter  Google Scholar 

  31. Zheng, Y., Liu, Y., Yuan, J., & Xie, X. (2011). Urban computing with taxicabs. In Proceedings of the 13th International Conference on Ubiquitous Computing (pp. 89–98). New York: ACM.

    Google Scholar 

  32. Zheng, Y., Xie, X., & Ma, W.-Y. (2010). Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data(base) Engineering Bulletin, 33(2), 32–39.

    Google Scholar 

  33. Zheng, Y., & Zhou, X. (2011). Computing with spatial trajectories. New York: Springer.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing and Information, Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA

    Kenrick Fernande & Vineet Raghu

  2. School of Computing and Information, Department of Informatics and Networked Systems, University of Pittsburgh, Pittsburgh, PA, USA

    Pedram Gharani

Authors
  1. Kenrick Fernande
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pedram Gharani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vineet Raghu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pedram Gharani .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    M. Hadi Amini

  2. School of Computing and Information Sciences, Florida International University, Miami, FL, USA

    Kianoosh G. Boroojeni

  3. School of Computing and Information Sciences, Florida International University, Miami, FL, USA

    S. S. Iyengar

  4. Department of Industrial & Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  5. Department of Energy Technology, Aalborg University, Aalborg, Denmark

    Frede Blaabjerg

  6. Department of Electrical & Computer Engineering, University of California Los Angeles, Los Angeles, CA, USA

    Asad M. Madni

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Fernande, K., Gharani, P., Raghu, V. (2019). TRAJEDI: Trajectory Dissimilarity. In: Amini, M., Boroojeni, K., Iyengar, S., Pardalos, P., Blaabjerg, F., Madni, A. (eds) Sustainable Interdependent Networks II. Studies in Systems, Decision and Control, vol 186. Springer, Cham. https://doi.org/10.1007/978-3-319-98923-5_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-98923-5_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98922-8

  • Online ISBN: 978-3-319-98923-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation