Abstract
Recommending suitable routes to taxi drivers for picking up passengers is helpful to raise their incomes and reduce the gasoline consumption. In this paper, a pick-up tree based route recommender system is proposed to minimize the traveling distance without carrying passengers for a given taxis set. Firstly, we apply clustering approach to the GPS trajectory data of a large number of taxis that indicates state variance from “free” to “occupied”, and take the centroids as potential pick-up points. Secondly, we propose a heuristic based on skyline computation to construct a pick-up tree in which current position is its root node that connects all centroids. Then, we present a probability model to estimate gasoline consumption of every route. By adopting the estimated gasoline consumption as the weight of every route, the weighted Round-Robin recommendation method for the set of taxis is proposed. Our experimental results on real-world taxi trajectories data set have shown that the proposed recommendation method effectively reduce the driving distance before carrying passengers, especially when the number of cabs becomes large. Meanwhile, the time-cost of our method is also lower than the existing methods.
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References
Wang, S., Wu, C.: Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system. Expert Systems with Applications (2011)
Abowd, G.D., Atkeson, C.G., Hong, J., Long, S., Kooper, R., Pinkerton, M.: Cyber-guide: A mobile context-aware tour guide. Wireless Networks 3(5), 421–433 (1997)
Staab, S., Werthner, H.: Intelligent systems for tourism. IEEE Intelligent Systems 17(6), 53–66 (2002)
Tao, Y., Ding, L., Lin, X., Pei, J.: Distance-based representative skyline. In: Proceedings of the 2009 IEEE International Conference on Data Engineering (ICDE 2009), pp. 892–903 (2009)
Liu, Q., Ge, Y., Li, Z., Chen, E., Xiong, H.: Personalized travel package recommendation. In: IEEE 11th International Conference on Data Mining (ICDM 2011), pp. 407–416 (2011)
Ge, Y., Xiong, H., Liu, C., Zhou, Z.: A taxi driving fraud detection system. In: IEEE 11th International Conference on Data Mining(ICDM 2011), pp. 181–190 (2011)
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 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 99–108 (2010)
Yuan, J., Zheng, Y., Xie, X., Sun, G.: Driving with knowledge from the physical world. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 316–324 (2011)
Ge, Y., Xiong, H., Tuzhilin, A., Xiao, K., Gruteser, M., Pazzani, M.: An energy-efficient mobile recommender system. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 899–908 (2010)
Yuan, Y., Lin, X., Liu, Q., Wang, W., Yu, J., Zhang, Q.: Efficient computation of the skyline cube. In: Proceedings of the 31st International Conference on Very Large Data Bases, pp. 241–252 (2005)
Vincenty, T.: Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations. Survey Review 23(176), 88–93 (1975)
van der Heijden, H., Kotsis, G., Kronsteiner, R.: Mobile recommendation systems for decision making. In: Proceedings of International Conference on Mobile Business (ICMB 2005), pp. 137–143 (2005)
Quercia, D., Lathia, N., Calabrese, F., Lorenzo, G.D., Crowcroft, J.: Recommending social events from mobile phone location data. In: IEEE 10th International Conference on Data Mining (ICDM 2010) (2010)
Liu, L., Andris, C., Ratti, C.: Uncovering cabdrivers’ behavior patterns from their digital traces. Computers, Environment and Urban Systems 34(6), 541–548 (2010)
Liu, S., Liu, Y., Ni, L.M., Fan, J., Li, M.: Towards mobility-based clustering. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 919–927 (2010)
Chang, H., Tai, Y., Hsu, J.: Context-aware taxi demand hotspots prediction. International Journal of Business Intelligence and Data Mining 5(1), 3–18 (2010)
Wu, J., Chen, J., Ren, Y.: GIS enabled service site selection: Environmental analysis and beyond. Information Systems Frontier (13), 337–348 (2011)
Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., Ma, W.Y.: Mining user similarity based on location history. In: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (2008)
Zheng, Y., Zhang, L., Ma, Z., Xie, X., Ma, W.: Recommending friends and locations based on individual location history. ACM Transactions on the Web (TWEB) 5(1), 5 (2011)
Liu, W., Zheng, Y., Chawla, S., Yuan, J., Xing, X.: Discovering spatio-temporal causal interactions in traffic data streams. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1010–1018 (2011)
Chen, Z., Shen, H., Zhou, X., Zheng, Y., Xie, X.: Searching trajectories by locations: an efficiency study. In: Proceedings of the 2010 International Conference on Management of Data (SIGMOD 2010), pp. 255–266 (2010)
Chen, Z., Shen, H., Zhou, X.: Discovering popular routes from trajectories. In: Proceedings of the 2009 International Conference on Data Engineering (ICDE 2011), pp. 900–911 (2011)
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Hu, H., Wu, Z., Mao, B., Zhuang, Y., Cao, J., Pan, J. (2012). Pick-Up Tree Based Route Recommendation from Taxi Trajectories. In: Gao, H., Lim, L., Wang, W., Li, C., Chen, L. (eds) Web-Age Information Management. WAIM 2012. Lecture Notes in Computer Science, vol 7418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32281-5_45
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DOI: https://doi.org/10.1007/978-3-642-32281-5_45
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