Abstract
The problem of trip planning has received wide concerns in recent years. More and more people require the service of automatically confirming the optimal tour path. When users assign the source, the destination and the permitted time of the tour, how do we help them find the optimal path with the maximum popularity score? The multi-constrained optimal path search solutions have been used to solve the trip planning problem widely. However, when the permitted time is not enough to visit all attractions in any path, existing methods can not find the path satisfying the constraints. The time and the popularity score are different when we select the different attractions to visit in the same path. So we propose a new search rule to answer above issue, making a choice on any node (visit or pass by) according to the tradeoff between permitted time and popularity score of attractions. As our search rule need to make a choice on any attraction (visit or pass by), the search cost will be larger. This problem is NP hard. In this paper, we propose an exact algorithm to find the optimal path in relatively small data sets, and we also present a heuristic algorithm which is efficient and scalable to large data sets. The experimental results on real data sets reveal that our algorithms are able to find the optimal path efficiently.
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References
Bao, J., Yang, X., Wang, B., Wang, J.: An efficient trip planning algorithm under constraints. WISA, 429–434 (November 2013)
Chen, Z., Shen, H., Zhou, X., et al.: Searching trajectories by locations-an efficiency study. In: SIGMOD, pp. 255–266 (2010)
Chen, Z., Shen, H.T., Zhou, X.: Discovering popular routes from trajectories. In: ICDE, pp. 900–911 (2011)
Cong, G., Lu, H., Ooi, B.-C., et al.: Efficient spatial keyword search in trajectory databases. Arxiv Preprint, pp. 1–12 (2012)
Li, F., Cheng, D., Hadjieleftheriou, M., Kollios, G., Teng, S.: On trip planning queries in spatial databases. ASTD, 273–290 (2005)
Lu, E., Lin, C.Y., Tseng, V.S.: Trip-mine:an efficient trip planning approach with travel time constraints. MDM, 152–161 (November 2011)
Lu, X., Wang, C., Yang, J., Pang, Y., Zhang, L.: Photo2trip: generating travel routes from geotagged photos for trip planning. MM, 143–152 (2010)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman and Company, San Francisco (1979)
Cao, X., Chen, L., Cong, G., Xiao, X.: Keyword aware optimal route search. VLDB 5(11), 1136–1147 (2012)
Huang, Y., Bian, L.: A bayesian network and analyti hierarchy process based personalized recommendations for tourist attractionsover the internet. Expert Systems with Applications 36(1), 933–943 (2009)
Zheng, Y., Zhang, L., Xie, X., Ma., W.: Mining interesting locations and travel sequences from gps trajectories. In: WWW, pp. 791–800 (2009)
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Bao, J., Wang, B., Yan, S., Yang, X. (2014). Multi-constrained Optimal Path Search Algorithms. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8709. Springer, Cham. https://doi.org/10.1007/978-3-319-11116-2_31
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DOI: https://doi.org/10.1007/978-3-319-11116-2_31
Publisher Name: Springer, Cham
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