Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
184 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Distributed Solution for Efficient K Shortest Paths Computation over Dynamic Road Networks (2312.12687v1)

Published 20 Dec 2023 in cs.DB

Abstract: The problem of identifying the k-shortest paths KSPs for short in a dynamic road network is essential to many location-based services. Road networks are dynamic in the sense that the weights of the edges in the corresponding graph constantly change over time, representing evolving traffic conditions. Very often such services have to process numerous KSP queries over large road networks at the same time, thus there is a pressing need to identify distributed solutions for this problem. However, most existing approaches are designed to identify KSPs on a static graph in a sequential manner, restricting their scalability and applicability in a distributed setting. We therefore propose KSP-DG, a distributed algorithm for identifying k-shortest paths in a dynamic graph. It is based on partitioning the entire graph into smaller subgraphs, and reduces the problem of determining KSPs into the computation of partial KSPs in relevant subgraphs, which can execute in parallel on a cluster of servers. A distributed two-level index called DTLP is developed to facilitate the efficient identification of relevant subgraphs. A salient feature of DTLP is that it indexes a set of virtual paths that are insensitive to varying traffic conditions in an efficient and compact fashion, leading to very low maintenance cost in dynamic road networks. This is the first treatment of the problem of processing KSP queries over dynamic road networks. Extensive experiments conducted on real road networks confirm the superiority of our proposal over baseline methods.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. S. Shang, L. Chen, Z. Wei, C. S. Jensen, J.-R. Wen, and P. Kalnis, “Collective travel planning in spatial networks,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 5, pp. 1132–1146, 2016.
  2. L. Chen, Y. Gao, Z. Fang, X. Miao, C. S. Jensen, and C. Guo, “Real-time distributed co-movement pattern detection on streaming trajectories,” Proceedings of the VLDB Endowment, vol. 12, no. 10, pp. 1208–1220, 2019.
  3. L. Chen, Q. Zhong, X. Xiao, Y. Gao, P. Jin, and C. S. Jensen, “Price-and-time-aware dynamic ridesharing,” in 2018 IEEE 34th international conference on data engineering (ICDE).   IEEE, 2018, pp. 1061–1072.
  4. Z. Yu, X. Yu, N. Koudas, Y. Liu, Y. Li, Y. Chen, and D. Yang, “Distributed processing of k shortest path queries over dynamic road networks,” in SIGMOD, 2020, pp. 665–679.
  5. H. Liu, C. Jin, B. Yang, and A. Zhou, “Finding top-k shortest paths with diversity,” IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 3, pp. 488–502, 2018.
  6. J. Y. Yen, “Finding the k shortest loopless paths in a network,” Management Science, vol. 17, no. 11, pp. 712–716, 1971.
  7. N. Katoh, T. Ibaraki, and H. Mine, “An efficient algorithm for k shortest simple paths,” Networks, vol. 12, no. 4, pp. 411–427, 1982.
  8. D. Eppstein, “Finding the k shortest paths,” SIAM Journal on computing, vol. 28, no. 2, pp. 652–673, 1998.
  9. J. Hershberger, M. Maxel, and S. Suri, “Finding the k shortest simple paths: A new algorithm and its implementation,” ACM Transactions on Algorithms, vol. 3, no. 4, pp. 45–es, 2007.
  10. J. Gao, H. Qiu, X. Jiang, T. Wang, and D. Yang, “Fast top-k simple shortest paths discovery in graphs,” in CIKM, 2010, pp. 509–518.
  11. J. Gao, J. Yu, H. Qiu, X. Jiang, T. Wang, and D. Yang, “Holistic top-k simple shortest path join in graphs,” IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 4, pp. 665–677, 2012.
  12. B. Zheng, C. Huang, C. S. Jensen, L. Chen, N. Q. V. Hung, G. Liu, G. Li, and K. Zheng, “Online trichromatic pickup and delivery scheduling in spatial crowdsourcing,” in ICDE.   IEEE, 2020, pp. 973–984.
  13. B. Zheng, K. Zheng, C. S. Jensen, N. Q. V. Hung, H. Su, G. Li, and X. Zhou, “Answering why-not group spatial keyword queries,” IEEE Trans. Knowl. Data Eng., vol. 32, no. 1, pp. 26–39, 2020.
  14. B. Zheng, L. Bi, J. Cao, H. Chai, J. Fang, L. Chen, Y. Gao, X. Zhou, and C. S. Jensen, “Speaknav: Voice-based route description language understanding for template driven path search,” Proc. VLDB Endow., vol. 14, no. 12, pp. 3056–3068, 2021.
  15. K. M. Chandy and J. Misra, “Distributed computation on graphs: Shortest path algorithms,” Programming Techniques and Data Structures, pp. 833–837, 1982.
  16. B. Awerbuch, “Randomized distributed shortest paths algorithms,” in STOC, 1989, pp. 490–500.
  17. M. Elkin, “Distributed exact shortest paths in sublinear time,” in STOC, 2017, pp. 757–770.
  18. M. Ghaffari and J. Li, “Improved distributed algorithms for exact shortest paths,” in STOC, 2018, p. 431–444.
  19. S. Aridhi, P. Lacomme, L. Ren, and B. Vincent, “A mapreduce-based approach for shortest path problem in large-scale networks,” Engineering Applications of Artificial Intelligence, vol. 41, pp. 151–165, 2015.
  20. K. Qiu, Y. Zhu, J. Yuan, J. Zhao, X. Wang, and T. Wolf, “Parapll: Fast parallel shortest-path distance query on large-scale weighted graphs,” in ICPP, 2018, pp. 1–10.
  21. W. Fan, X. Wang, and Y. Wu, “Performance guarantees for distributed reachability queries,” in VLDB, 2012, pp. 1304–1315.
  22. D. Yang, D. Zhang, K.-L. Tan, J. Cao, and F. Le Mouël, “Cands: continuous optimal navigation via distributed stream processing,” in VLDB, 2014, pp. 137–148.
  23. S. Shang, L. Chen, Z. Wei, C. S. Jensen, K. Zheng, and P. Kalnis, “Trajectory similarity join in spatial networks,” in VLDB, 2017, pp. 1178–1189.
  24. B. Yao, W. Zhang, Z.-J. Wang, Z. Chen, S. Shang, K. Zheng, and M. Guo, “Distributed in-memory analytics for big temporal data,” in DASFAA, 2018.
  25. L. Chen, S. Shang, C. S. Jensen, B. Yao, and P. Kalnis, “Parallel semantic trajectory similarity join,” in ICDE, 2020, pp. 997–1008.
  26. D. Ajwani, E. Duriakova, N. Hurley, U. Meyer, and A. Schickedanz, “An empirical comparison of k-shortest simple path algorithms on multicores,” in ICPP, 2018.
  27. G. Feng, “Finding k shortest simple paths in directed graphs: A node classification algorithm,” Networks, pp. 6–17, 2014.
  28. “Apache storm,” http://storm.apache.org/, 2019.
  29. DIMACS, “http://users.diag.uniroma1.it/challenge9,” 2005.
  30. F. Bernhard, G. Martin, and G. Stefan, “Time-varying travel times in vehicle routing,” Transportation Science, vol. 38, no. 2, pp. 121–255, 2004.
  31. B. Hannah, D. Daniel, G. Andrew, M.-H. Matthias, P. Thomas, S. Peter, W. Dorothea, and R. F. Werneck, “Route planning in transportation networks,” Algorithm Engineering, pp. 19–80, 2016.
  32. J. Hershberger and S. Suri, “Vickrey prices and shortest paths: What is an edge worth?” in IEEE International Conference on Cluster Computing, 2001, pp. 252–259.
  33. L. Chang, X. Lin, L. Qin, J. X. Yu, and J. Pei, “Efficiently computing top-k shortest path join,” in EDBT, 2015, pp. 133–144.
  34. Z. Luo, L. Li, M. Zhang, W. Hua, Y. Xu, and X. Zhou, “Diversified top-k route planning in road network,” in VLDB, 2022, pp. 3199–3212.
  35. S. Forster and D. Nanongkai, “A faster distributed single-source shortest paths algorithm,” in IEEE 59th Annual Symposium on Foundations of Computer Science, 2018, pp. 686–697.
  36. Y. Li, Y. Yuan, Y. Wang, X. Lian, Y. Ma, and G. Wang, “Distributed multimodal path queries,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 7, pp. 3196–3210, 2022.
  37. T. Akiba, Y. Iwata, and Y. Yoshida, “Fast exact shortest-path distance queries on large networks by pruned landmark labeling,” in SIGMOD, 2013, pp. 349–360.
  38. S. Shang, L. Chen, Z.-W. Wei, D.-H. Guo, and J.-R. Wen, “Dynamic shortest path monitoring in spatial networks,” Journal of Computer Science and Technology, vol. 31, pp. 637–648, 2016.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now