Abstract
While the problems of finding the shortest path and k-shortest paths have been extensively researched, the research community has been shifting its focus towards discovering and identifying paths based on user preferences. Since users naturally follow some of the paths more than other paths, the popularity of a given path often reflects such user preferences. Given a set of user traversals in a road network and a set of paths between a given source and destination pair, we address the problem of performing top-k ranking of the paths in that set based on path popularity. In this paper, we introduce a new model for computing the popularity scores of paths. Our main contributions are threefold. First, we propose a framework for modeling user traversals in a road network as transactions. Second, we present an approach for efficiently computing the popularity score of any path based on the itemsets extracted from the transactions using pattern mining techniques. Third, we conducted an extensive performance evaluation with two real datasets to demonstrate the effectiveness of the proposed scheme.
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References
Aggarwal, C.C., Han, J. (eds.): Frequent Pattern Mining. Springer (2014)
Chang, K.P., Wei, L.Y., Yeh, M.Y., Peng, W.C.: Discovering personalized routes from trajectories. In: Proceedings of the International Workshop on Location-Based Social Networks, pp. 33–40. ACM (2011)
Chen, Z., Shen, H.T., Zhou, X.: Discovering popular routes from trajectories. In: Proceedings of the International Conference on Data Engineering, pp. 900–911. IEEE (2011)
Chondrogiannis, T., Bouros, P., Gamper, J., Leser, U.: Alternative routing: k-shortest paths with limited overlap. In: Proceedings of the International Conference on Advances in Geographic Information Systems, p. 68. ACM (2015)
Chondrogiannis, T., Bouros, P., Gamper, J., Leser, U.: Exact and approximate algorithms for finding k-shortest paths with limited overlap. In: Proceedings of the International Conference on Extending Database Technology, pp. 414–425 (2017)
Chondrogiannis, T., Gamper, J.: ParDiSP: A partition-based framework for distance and shortest path queries on road networks. In: Proceedings of the International Conference on Mobile Data Management, vol. 1, pp. 242–251. IEEE (2016)
Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1(1), 269–271 (1959)
Feng, Z., Zhu, Y.: A survey on trajectory data mining: techniques and applications. IEEE Access 4, 2056–2067 (2016)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD Record, 2, pp. 1–12. ACM (2000)
He, Z., Chen, K., Chen, X.: A collaborative method for route discovery using taxi drivers experience and preferences. IEEE Trans. Intell. Transp. Syst. 19(8), 2505–2514 (2017)
Hendawi, A.M., Rustum, A., Ahmadain, A.A., Hazel, D., Teredesai, A., Oliver, D., Ali, M., Stankovic, J.A.: Smart personalized routing for smart cities. In: Proceedings of the International Conference on Data Engineering, pp. 1295–1306. IEEE (2017)
Hershberger, J., Maxel, M., Suri, S.: Finding the k shortest simple paths: a new algorithm and its implementation. ACM Trans. Algorithms 3(4), 45 (2007)
Hu, G., Shao, J., Ni, Z., Zhang, D.: A graph based method for constructing popular routes with check-ins. World Wide Web 21(6), 1689–1703 (2018)
Koide, S., Tadokoro, Y., Yoshimura, T., Xiao, C., Ishikawa, Y.: Enhanced indexing and querying of trajectories in road networks via string algorithms. ACM Trans. Spatial Algorithms Syst. 4(1), 1–41 (2018)
Letchner, J., Krumm, J., Horvitz, E.: Trip router with individualized preferences (trip): incorporating personalization into route planning. In: Proceedings of the National Conference on Artificial Intelligence and the Innovative Applications of Artificial Intelligence Conference, pp. 1795–1800. AAAI Press (2006)
Li, X., Han, J., Lee, J.G., Gonzalez, H.: Traffic density-based discovery of hot routes in road networks. In: Proceedings of the International Symposium on Spatial and Temporal Databases, pp. 441–459. Springer (2007)
Liu, H., Jin, C., Yang, B., Zhou, A.: Finding top-k shortest paths with diversity. IEEE Trans. Knowl. Data Eng. 30(3), 488–502 (2018)
Lo, C.L., Chen, C.H., Hu, J.L., Lo, K.R., Cho, H.J.: A fuel-efficient route plan method based on game theory. J. Internet Technol. 20(3), 925–932 (2019)
Martins, E.Q., Pascoal, M.M.: A new implementation of Yens ranking loopless paths algorithm. Q. J. Belgian French Ital. Operat. Res. Soc. 1(2), 121–133 (2003)
Potamias, M., Bonchi, F., Castillo, C., Gionis, A.: Fast shortest path distance estimation in large networks. In: Proceedings of the ACM Conference on Information and Knowledge Management, pp. 867–876. ACM (2009)
Rathan, P.R., Reddy, P.K., Mondal, A.: Discovering diverse popular paths using transactional modeling and pattern mining. In: Proceedings of the International Conference on Database and Expert Systems Applications, pp. 327–337. Springer (2019)
Sacharidis, D., Bouros, P., Chondrogiannis, T.: Finding the most preferred path. In: Proceedings of the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1–10 (2017)
Sommer, C.: Shortest-path queries in static networks. ACM Comput. Surv. 46(4), 1–31 (2014)
Wang, Z., Che, O., Chen, L., Lim, A.: An efficient shortest path computation system for real road networks. In: Proceedings of the International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 711–720. Springer (2006)
Wei, L.Y., Chang, K.P., Peng, W.C.: Discovering pattern-aware routes from trajectories. Distrib. Parallel Databases 33(2), 201–226 (2015)
Wei, L.Y., Zheng, Y., Peng, W.C.: Constructing popular routes from uncertain trajectories. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining, pp. 195–203. ACM (2012)
Yen, J.Y.: Finding the k shortest loopless paths in a network. Manag. Sci. 17(11), 712–716 (1971)
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 the International Conference on Advances in Geographic Information Systems, pp. 99–108. ACM (2010)
Zheng, Y., Xie, X., Ma, W.Y.: Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2), 32–39 (2010)
Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the International Conference on World Wide Web, pp. 791–800. ACM (2009)
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Rathan, P.R., Reddy, P.K. & Mondal, A. A framework for discovering popular paths using transactional modeling and pattern mining. Distrib Parallel Databases 40, 109–133 (2022). https://doi.org/10.1007/s10619-021-07366-7
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DOI: https://doi.org/10.1007/s10619-021-07366-7