Impromptu Rendezvous Based Multi-threaded Algorithm for Shortest Lagrangian Path Problem on Road Networks

  • Kartik Vishwakarma
  • Venkata M. V. GunturiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11944)


Input to the shortest lagrangian path (SLP) problem consists of the following: (a) road network dataset (modeled as a time-varying graph to capture its temporal variation in traffic), (b) a source-destination pair and, (c) a departure-time (\(t_{dep}\)). Given the input, the goal of the SLP problem is to determine a fastest path between the source and destination for the departure-time \(t_{dep}\) (at the source). The SLP problem has value addition potential in the domain of urban navigation. SLP problem has been studied extensively in the research literature. However, almost all of the proposed algorithms are essentially serial in nature. Thus, they fail to take full advantage of the increasingly available multi-core (and multi-processor) systems. However, developing parallel algorithms for the SLP problem is non-trivial. This is because SLP problem requires us to follow Lagrangian reference frame while evaluating the cost of a candidate path. In other words, we need to relax an edge (whose cost varies with time) only for the time at which the candidate path (from source) arrives at the head node of the edge. Otherwise, we would generate meaningless labels for nodes. This constraint precludes use of any label correcting based approaches (e.g., parallel version of Delta-Stepping at its variants) as they do not relax edges along candidate paths. Lagrangian reference frame can be implemented in label setting based techniques, however, they are hard to parallelize. In this paper, we propose a novel multi-threaded label setting algorithm called IMRESS which follows Lagrangian reference frame. We evaluate IMRESS both analytically and experimentally. We also experimentally compare IMRESS against related work to show its superior performance.



Authors would like to thank DST SERB (grant# ECR/2016/001053) and IIT Ropar for their support in this work.


  1. 1.
    Chakaravarthy, V.T., et al.: Scalable single source shortest path algorithms for massively parallel systems. IEEE Trans. PDS 28(7), 2031–2045 (2017)Google Scholar
  2. 2.
    Davidson, A., et al.: Work-efficient parallel GPU methods for single-source shortest paths. In: Proceedings of the IPDPS, pp. 349–359 (2014)Google Scholar
  3. 3.
    Delling, D.: Time-dependent SHARC-routing. In: Halperin, D., Mehlhorn, K. (eds.) ESA 2008. LNCS, vol. 5193, pp. 332–343. Springer, Heidelberg (2008). Scholar
  4. 4.
    Delling, D., et al.: Phast: hardware-accelerated shortest path trees. J. Parallel Distrib. Comput. 73(7), 940–952 (2013)CrossRefGoogle Scholar
  5. 5.
    Demiryurek, U., Banaei-Kashani, F., Shahabi, C., Ranganathan, A.: Online computation of fastest path in time-dependent spatial networks. In: Pfoser, D., et al. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 92–111. Springer, Heidelberg (2011). Scholar
  6. 6.
    Ding, B., et al.: Finding time-dependent shortest paths over large graphs. In: Proceedings of the 11th International Conference on Extending Database Technology, pp. 205–216. ACM (2008)Google Scholar
  7. 7.
    George, B., Kim, S., Shekhar, S.: Spatio-temporal network databases and routing algorithms: a summary of results. In: Papadias, D., Zhang, D., Kollios, G. (eds.) SSTD 2007. LNCS, vol. 4605, pp. 460–477. Springer, Heidelberg (2007). Scholar
  8. 8.
    George, B., Shekhar, S.: Time-aggregated graphs for modeling spatio-temporal networks. In: Spaccapietra, S., et al. (eds.) Journal on Data Semantics XI. LNCS, vol. 5383, pp. 191–212. Springer, Heidelberg (2008). Scholar
  9. 9.
    Grama, A., et al.: Introduction to Parallel Computing. Pearson (2003)Google Scholar
  10. 10.
    Gunturi, V., et al.: A critical-time-point approach to all-departure-time lagrangian shortest paths. IEEE trans. KDE 27(10), 2591–2603 (2015)Google Scholar
  11. 11.
    Gunturi, V.M.V., Shekhar, S.: Spatio-Temporal Graph Data Analytics. Springer, Cham (2017). 978-3-319-67770-5CrossRefGoogle Scholar
  12. 12.
    Henke, N., et al.: The age of analytics: competing in a data-driven world, Mckinsey Global Institute, December 2016.
  13. 13.
    Karduni, A., et al.: A protocol to convert spatial polyline data to network formats and applications to world urban road networks. Sci. Data 3, Article no. 160046 (2016)Google Scholar
  14. 14.
    Maleki, S., et al.: DSMR: a shared and distributed memory algorithm for single-source shortest path problem. In: Proceedings of the 21st PPoPP, pp. 39:1–39:2 (2016)Google Scholar
  15. 15.
    Nannicini, G., et al.: Bidirectional a* search on time-dependent road networks. Networks 59(2), 240–251 (2012)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Simmhan, Y., et al.: Distributed programming over time-series graphs. In: 2015 IEEE International Parallel and Distributed Processing Symposium, pp. 809–818 (2015)Google Scholar
  17. 17.
    Simmhan, Y., et al.: GoFFish: a sub-graph centric framework for large-scale graph analytics. In: Silva, F., Dutra, I., Santos Costa, V. (eds.) Euro-Par 2014. LNCS, vol. 8632, pp. 451–462. Springer, Cham (2014). Scholar
  18. 18.
    Yuan, J., et al.: T-drive: driving directions based on taxi trajectories. In: Proceedings of the SIGSPATIAL International Conference on Advances in GIS, GIS 2010, pp. 99–108 (2010)Google Scholar

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Authors and Affiliations

  1. 1.IIT RoparPunjabIndia

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