The VLDB Journal

, Volume 26, Issue 3, pp 399–419 | Cite as

Distributed shortest path query processing on dynamic road networks

  • Dongxiang Zhang
  • Dingyu Yang
  • Yuan Wang
  • Kian-Lee Tan
  • Jian Cao
  • Heng Tao Shen
Regular Paper

Abstract

Shortest path query processing on dynamic road networks is a fundamental component for real-time navigation systems. In the face of an enormous volume of customer demand from Uber and similar apps, it is desirable to study distributed shortest path query processing that can be deployed on elastic and fault-tolerant cloud platforms. In this paper, we combine the merits of distributed streaming computing systems and lightweight indexing to build an efficient shortest path query processing engine on top of Yahoo S4. We propose two types of asynchronous communication algorithms for early termination. One is first-in-first-out message propagation with certain optimizations, and the other is prioritized message propagation with the help of navigational intelligence. Extensive experiments were conducted on large-scale real road networks, and the results show that the query efficiency of our methods can meet the real-time requirement and is superior to Pregel and Pregel+. The source code of our system is publicly available at https://github.com/yangdingyu/cands.

Keywords

Shortest path query Dynamic road networks Navigational intelligence Yahoo S4 

References

  1. 1.
    Abraham, I., Fiat, A., Goldberg, AV., Werneck, RF.: Highway dimension, shortest paths, and provably efficient algorithms. In: SODA, pp. 782–793 (2010)Google Scholar
  2. 2.
    Akiba, T., Iwata, Y., Yoshida, Y.: Fast exact shortest-path distance queries on large networks by pruned landmark labeling. In: SIGMOD, pp. 349–360 (2013)Google Scholar
  3. 3.
    Bast, H., Funke, S., Sanders, P., Schultes, D.: Fast routing in road networks with transit nodes. Science 316(5824), 566–566 (2007)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Bast, H., Delling, D., Goldberg, AV., Müller-Hannemann, M., Pajor, T., Sanders, P., Wagner, D., Werneck, RF.: Route planning in transportation networks. arXiv:1504.05140v1 [cs.DS] (2015)
  5. 5.
    Biem, A., Bouillet, E., Feng, H., Ranganathan, A., Riabov, A., Verscheure, O., Koutsopoulos, H., Moran, C.: Ibm infosphere streams for scalable, real-time, intelligent transportation services. In: SIGMOD, ACM, pp. 1093–1104 (2010)Google Scholar
  6. 6.
    Cheng, J., Ke, Y., Chu, S., Cheng, C.: Efficient processing of distance queries in large graphs: a vertex cover approach. In: SIGMOD, pp. 457–468 (2012)Google Scholar
  7. 7.
    Delling, D., Werneck, RF.: Faster customization of road networks. In: Experimental Algorithms, Springer, Berlin, pp. 30–42 (2013)Google Scholar
  8. 8.
    Delling, D., Goldberg, AV., Pajor, T., Werneck, RF.: Customizable Route Planning. In: Pardalos, PM., Rebennack, S., (Eds.) Proceedings of the 10th International Symposium on Experimental Algorithms (SEA’11), Springer, Lecture Notes in Computer Science, vol. 6630, pp. 376–387 (2011)Google Scholar
  9. 9.
    Delling, D., Goldberg, AV., Pajor, T., Werneck, RF.: Customizable route planning in road networks. Transportation Science (2015). doi:10.1287/trsc.2014.0579
  10. 10.
    Fan, Q., Zhang, D., Wu, H., Tan, K.: A general and parallel platform for mining co-movement patterns over large-scale trajectories. PVLDB 10(4), 313–324 (2016)Google Scholar
  11. 11.
    Geisberger, R., Sanders, P., Schultes, D., Delling, D.: Contraction hierarchies: Faster and simpler hierarchical routing in road networks. In: Experimental Algorithms, pp. 319–333. Springer, Berlin (2008)Google Scholar
  12. 12.
    Goldberg, AV., Harrelson, C.: Computing the shortest path: A search meets graph theory. In: SODA, pp. 156–165 (2005)Google Scholar
  13. 13.
    Goldberg, A.V., Kaplan, H., Werneck, R.F.: Reach for a*: Efficient point-to-point shortest path algorithms. ALENEX 6, 129–143 (2006)Google Scholar
  14. 14.
    Gonzalez, H., Han, J., Li, X., Myslinska, M., Sondag, JP.: Adaptive fastest path computation on a road network: a traffic mining approach. In: VLDB, VLDB Endowment, pp. 794–805 (2007)Google Scholar
  15. 15.
    Gonzalez, JE., Low, Y., Gu, H., Bickson, D., Guestrin, C.: Powergraph: Distributed graph-parallel computation on natural graphs. In: OSDI, pp. 17–30 (2012)Google Scholar
  16. 16.
    Guerrero-Ibáñez, A., Flores-Cortés, C., Damián-Reyes, P., Andrade-Aréchiga, M., Pulido, J.: Emerging technologies for urban traffic management. Tech. rep. (2012)Google Scholar
  17. 17.
    Hunter, T., Moldovan, TM., Zaharia, M., Merzgui, S., Ma, J., Franklin, MJ., Abbeel, P., Bayen, AM.: Scaling the mobile millennium system in the cloud. In: SOCC, p. 28 (2011)Google Scholar
  18. 18.
    Jin, R., Ruan, N., Xiang, Y., Lee, VE.: A highway-centric labeling approach for answering distance queries on large sparse graphs. In: SIGMOD, pp. 445–456 (2012)Google Scholar
  19. 19.
    Jin, R., Ruan, N., You, B., Wang, H.: Hub-accelerator: Fast and exact shortest path computation in large social networks. arXiv:1305.0507v1 [cs.SI] (2013)
  20. 20.
    Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20(1), 359–392 (1998)MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Kieritz, T., Luxen, D., Sanders, P., Vetter, C.: Distributed time-dependent contraction hierarchies. ISEA, LNCS 6049, 83–93 (2010)Google Scholar
  22. 22.
    Low, Y., Gonzalez, J., Kyrola, A., Bickson, D., Guestrin, C., Hellerstein, J.M.: Distributed graphlab: a framework for machine learning in the cloud. PVLDB 5(8), 716–727 (2012)Google Scholar
  23. 23.
    Malewicz, G., Austern, MH., Bik, AJ., Dehnert, JC., Horn, I., Leiser, N., Czajkowski, G.: Pregel: a system for large-scale graph processing. In: SIGMOD, ACM, pp. 135–146 (2010)Google Scholar
  24. 24.
    Maue, J., Sanders, P., Matijevic, D.: Goal directed shortest path queries using precomputed cluster distances. ACM J. Exp. Algorithmics (2007)Google Scholar
  25. 25.
    Rice, M., Tsotras, V.J.: Graph indexing of road networks for shortest path queries with label restrictions. VLDB 4(2), 69–80 (2010)Google Scholar
  26. 26.
    Salihoglu, S., Widom, J.: GPS: a graph processing system. In: SSDBM, pp. 22:1–22:12 (2013)Google Scholar
  27. 27.
    Sanders, P., Schultes, D.: Highway hierarchies hasten exact shortest path queries. In: ESA, pp. 568–579, Springer, Berlin (2005)Google Scholar
  28. 28.
    Thiagarajan, A., Ravindranath, L., LaCurts, K., Madden, S., Balakrishnan, H., Toledo, S., Eriksson, J.: Vtrack: accurate, energy-aware road traffic delay estimation using mobile phones. In: SenSys, pp. 85–98, ACM (2009)Google Scholar
  29. 29.
    Thomsen, JR., Yiu, ML., Jensen, CS.: Effective caching of shortest paths for location-based services. In: SIGMOD, pp. 313–324 (2012)Google Scholar
  30. 30.
    Wang, Y., Zhang, D., Hu, L., Yang, Y., Lee, LH.: A data-driven and optimal bus scheduling model with time-dependent traffic and demand. IEEE Trans. Intell. Transp. Syst. (99):1–10, (2017) doi:10.1109/TITS.2016.2644725
  31. 31.
    Wei, H., Wang, Y., Forman, G., Zhu, Y., Guan, H.: Fast Viterbi map matching with tunable weight functions. In: SIGSPATIAL GIS, pp. 613–616, ACM (2012)Google Scholar
  32. 32.
    Wu, L., Xiao, X., Deng, D., Cong, G., Zhu, A.D., Zhou, S.: Shortest path and distance queries on road networks: an experimental evaluation. PVLDB 5(5), 406–417 (2012)Google Scholar
  33. 33.
    Yan, D., Cheng, J., Lu, Y., Ng, W.: Blogel: a block-centric framework for distributed computation on real-world graphs. PVLDB 7(14), 1981–1992 (2014)Google Scholar
  34. 34.
    Yan, D., Cheng, J., Xing, K., Lu, Y., Ng, W., Bu, Y.: Pregel algorithms for graph connectivity problems with performance guarantees. PVLDB 7(14), 1821–1832 (2014)Google Scholar
  35. 35.
    Yan, D., Cheng, J., Lu, Y., Ng, W.: Effective techniques for message reduction and load balancing in distributed graph computation. In: WWW, pp. 1307–1317 (2015)Google Scholar
  36. 36.
    Yan, D., Cheng, J., Özsu, MT., Yang, F., Lu, Y., Lui, JCS., Zhang, Q., Ng, W.: Quegel: A general-purpose query-centric framework for querying big graphs. arXiv:1601.06497v1 [cs.DC] (2016)
  37. 37.
    Yang, D., Zhang, D., Tan, K., Cao, J., Mouël, F.L.: CANDS: continuous optimal navigation via distributed stream processing. PVLDB 8(2), 137–148 (2014)Google Scholar
  38. 38.
    Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., Huang, Y.: T-drive: driving directions based on taxi trajectories. In: SIGSPATIAL GIS, pp. 99–108 , ACM (2010)Google Scholar
  39. 39.
    Zheng, Y., Liu, Y., Yuan, J., Xie, X.: Urban computing with taxicabs. In: Ubicomp, pp. 89–98 (2011)Google Scholar
  40. 40.
    Zhu, AD., Ma, H., Xiao, X., Luo, S., Tang, Y., Zhou, S.: Shortest path and distance queries on road networks: towards bridging theory and practice. In: SIGMOD, pp. 857–868 (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Dongxiang Zhang
    • 1
  • Dingyu Yang
    • 2
  • Yuan Wang
    • 3
  • Kian-Lee Tan
    • 4
  • Jian Cao
    • 5
  • Heng Tao Shen
    • 1
  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.School of Electronics and InformationShanghai Dian Ji UniversityShanghaiChina
  3. 3.Department of Industrial System EngineeringNational University of SingaporeSingaporeSingapore
  4. 4.Department of Computer ScienceNational University of SingaporeSingaporeSingapore
  5. 5.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina

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