Skip to main content
SpringerLink
Log in

Popular Route Planning with Travel Cost Estimation

  • Conference paper
  • First Online:
Database Systems for Advanced Applications (DASFAA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9643))

Included in the following conference series:

Abstract

With the increasing number of GPS-equipped vehicles, more and more trajectories are generated continuously, based on which some urban applications become feasible, such as route planning. In general, route planning aims at finding a path from source to destination to meet some specific requirements, i.e., the minimal travel time, fee or fuel consumption. Especially, some users may prefer popular route that has been travelled frequently. However, the existing work to find the popular route does not consider how to estimate the travelling cost. In this paper, we address this issue by devising a novel structure, called popular traverse graph, to summarize historical trajectories. Based on which an efficient route planning algorithm is proposed to search the popular route with minimal travel cost. The extensive experimental reports show that our method is both effective and efficient.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Notes

  1. 1.

    To avoid the case that \(N(slot_i)=0\), we increase the value by 1.

References

  1. Kanoulas, E., Du, Y., Xia, T., Zhang, D.: Finding fastest paths on A road network with speed patterns. In: Proceedings of ICDE (2006)

    Google Scholar 

  2. Ding, B., Yu, J.X., Qin, L.: Finding time-dependent shortest paths over large graphs. In: Proceedings of EDBT, pp. 205–216 (2008)

    Google Scholar 

  3. Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., Huang, Y.: T-drive: driving directions based on taxi trajectories. In: Proceedings of SIGSPATIAL (2010)

    Google Scholar 

  4. Yuan, J., Zheng, Y., Xie, X., Sun, G.: Driving with knowledge from the physical world. In: Proceedings of KDD (2011)

    Google Scholar 

  5. Wang, Y., Zheng, Y., Xue, Y.: Travel time estimation of a path using sparse trajectories. In Proceedings of KDD (2014)

    Google Scholar 

  6. Grünwald, P.D., Myung, I.J., Pitt, M.A.: Advances in Minimum Description Length: Theory and Applications. MIT Press, Cambridge (2005)

    Google Scholar 

  7. Zheng, K., Zheng, Y., Xie, X., Zhou, X.: Reducing uncertainty of low-sampling-rate trajectories. In: Proceedings of ICDE (2012)

    Google Scholar 

  8. Wei, L.-Y., Zheng, Y., Peng, W.-C.: Constructing popular routes from uncertain trajectories. In: Proceedings of KDD (2012)

    Google Scholar 

  9. Chen, Z., Shen, H.T., Zhou, X.: Discovering popular routes from trajectories. In: Proceedings of ICDE (2011)

    Google Scholar 

  10. Luo, W., Tan, H., Chen, L., Ni, L.M.: Finding time period-based most frequent path in big trajectory data. In: Proceedings of SIGMOD (2013)

    Google Scholar 

  11. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische mathematik 1(1), 269–271 (1959)

    Article  MathSciNet  MATH  Google Scholar 

  12. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)

    Article  Google Scholar 

  13. Kenneth, K.L., Halsey, E.: The shortest route through a network with time-dependent internodal transit times. J. Math. Anal. Appl. 14(3), 493–498 (1966)

    Article  MathSciNet  MATH  Google Scholar 

  14. Gonzalez, H., Han, J., Li, X., Myslinska, M., Sondag, J.P.: Adaptive fastest path computation on a road network: a traffic mining approach. In: Proceedings of VLDB (2007)

    Google Scholar 

  15. Yang, B., Guo, C., Jensen, C.S., Kaul, M., Shang, S.: Stochastic skyline route planning under time-varying uncertainty. In: Proceedings of ICDE (2014)

    Google Scholar 

  16. Mao, J., Song, Q., Jin, C., Zhang, Z., Zhou, A.: Tscluwin: trajectory stream clustering over sliding window. In: Proceedings of DASFAA (2016)

    Google Scholar 

  17. Duan, X., Jin, C., Wang, X., Zhou, A., Yue, K.: Real-time personalized taxi-sharing. In: Proceedings of the DASFAA (2016)

    Google Scholar 

  18. Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of KDD (1996)

    Google Scholar 

  19. Ranu, S., Deepak, P., Aditya, D. Telang, A., Deshpande, P., Raghavan, S.: Indexing and matching trajectories under inconsistent sampling rates. In: Proceedings of ICDE (2015)

    Google Scholar 

  20. Lee, J.-G., Han, J., Whang, K.-Y.: Trajectory clustering: a partition-and-group framework. In: Proceedings of SIGMOD (2007)

    Google Scholar 

  21. Balan, R.K., Nguyen, K.X., Jiang, L.: Real-time trip information service for a large taxi fleet. In: Proceedings of MobiSys (2011)

    Google Scholar 

Download references

Acknowledgment

Our research is supported by the 973 program of China (No. 2012CB316203), NSFC (61370101, U1401256 and 61402180), Shanghai Knowledge Service Platform Project (No. ZF1213), Innovation Program of Shanghai Municipal Education Commission (14ZZ045), and Natural Science Foundation of Shanghai (No. 14ZR1412600).

Author information

Authors and Affiliations

  1. School of Computer Science and Software Engineering, Institute for Data Science and Engineering, East China Normal University, Shanghai, China

    Huiping Liu, Cheqing Jin & Aoying Zhou

Authors
  1. Huiping Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cheqing Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aoying Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cheqing Jin .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology , Atlanta, Georgia, USA

    Shamkant B. Navathe

  2. University of Texas at Dallas , Richardson, Texas, USA

    Weili Wu

  3. University of Minnesota , Minneapolis, Minnesota, USA

    Shashi Shekhar

  4. Renmin University , Beijing, China

    Xiaoyong Du

  5. Fudan University , Shanghai, China

    Sean X. Wang

  6. Rutgers, The State University of New Jer , New Brunswick, New Jersey, USA

    Hui Xiong

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Liu, H., Jin, C., Zhou, A. (2016). Popular Route Planning with Travel Cost Estimation. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, S., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9643. Springer, Cham. https://doi.org/10.1007/978-3-319-32049-6_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-32049-6_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32048-9

  • Online ISBN: 978-3-319-32049-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation