Recommending a Personalized Sequence of Pick-Up Points

  • Yizhi LiuEmail author
  • Jianxun Liu
  • Jianjun Wang
  • Zhuhua Liao
  • Mingdong Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10065)


The value of GPS data has generated a group of location-based services. Pick-up points recommendation by mining taxis’ trajectories can effectively both improve drivers’ profits and reduce oil consumption. However, existing methods always ignore the spatial-temporal features and the drivers’ preferences. Therefore, we propose to recommend a personalized sequence of pick-up points taking the two preceding factors into account. Firstly, we extract historical pick-up points from taxis’ trajectories and use these points to generate candidate ones by a novel approach of spatial-temporal analysis. Secondly, we devise a collaborative filtering algorithm to choose candidate points again. According to the location and the time of historical pick-up points, our system can give taxi-drivers an optimal sequence of pick-up points. Experimental results show that our method can obviously improve both the accuracy and the preference of candidate pick-up points for taxi-drivers.


Location-based services Trajectory mining Pick-up points recommendation Spatial-temporal analysis Personalized recommendation Collaborative filtering 



This work is supported by National Nature Science Foundation of China (61572187, 61370227, 61572186), Hunan Provincial Natural Science Foundation of China (2015JJ2056), Hunan Provincial University Innovation Platform Open Fund Project of China (14K037), General project of Hunan Provincial Education Department (16C0642).


  1. 1.
    Chang, H., Tai, Y., Hsu, J.Y.: Context-aware taxi demand hotspots prediction. Int. J. Bus. Intell. Data Min. 5(1), 3–18 (2010)CrossRefGoogle Scholar
  2. 2.
    Li, B., Zhang, D., Sun, L., Chen, C., Li, S.: Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset. In: Proceedings of the 8th IEEE International Workshop on Managing Ubiquitous Communications and Services, pp. 63–68 (2011)Google Scholar
  3. 3.
    Yuan, N.J., Zheng, Y., Zhang, L., Xie, X.: T-Finder: a recommender system for finding passengers and vacant taxis. IEEE Trans. Knowl. Data Eng. 25(10), 2390–2403 (2013)CrossRefGoogle Scholar
  4. 4.
    Zhang, M., Liu, J., Liu, Y., et al.: Recommending pick-up points for taxi-drivers based on spatio-temporal clustering. In: Proceedings of the 2nd IEEE International Conference on Cloud and Green Computing, pp. 67–72 (2012)Google Scholar
  5. 5.
    Yuan, J., Zheng, Y., Zhang, L., Xie, X., et al.: Where to find my next passenger. In: Proceedings of the 13th ACM International Conference on Ubiquitous Computing (2011)Google Scholar
  6. 6.
    Ge, Y., Xiong, H., Tu, A., et al.: 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)Google Scholar
  7. 7.
    Hou, Y., Li, X., Zhao, Y., et al.: Towards efficient vacant taxis cruising guidance. In: Proceedings of the IEEE Global Communications Conference, pp. 54–59 (2013)Google Scholar
  8. 8.
    Tang, H., Kerber, M., Huang, Q., et al.: Locating lucrative passengers for taxicab drivers. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 504–507 (2013)Google Scholar
  9. 9.
    Ding, Y., Liu, S., Pu, J., et al.: HUNTS: a trajectory recommendation system for effective and efficient hunting of taxi passengers. In: Proceedings of the 14th IEEE International Conference on Mobile Data Management, pp. 107–116 (2013)Google Scholar
  10. 10.
    Huang, J., Huang, X., Sun, H., et al.: Backward path growth for efficient mobile sequential recommendation. IEEE Trans. Knowl. Data Eng. 27(1), 46–60 (2015)CrossRefGoogle Scholar
  11. 11.
    Hwang, R.H., Hsueh, Y.L., Chen, Y.T.: An effective taxi recommender system based on a spatio-temporal factor analysis model. Inf. Sci. 314, 28–40 (2015)CrossRefGoogle Scholar
  12. 12.
    Powell, J.W., Huang, Y., Bastani, F., Ji, M.: Towards reducing taxicab cruising time using spatio-temporal profitability maps. In: Pfoser, D., Tao, Y., Mouratidis, K., Nascimento, M.A., Mokbel, M., Shekhar, S., Huang, Y. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 242–260. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-22922-0_15 CrossRefGoogle Scholar
  13. 13.
    Hu, H., Wu, Z., Mao, B., Zhuang, Y., Cao, J., Pan, J.: Pick-Up tree based route recommendation from taxi trajectories. In: Gao, H., Lim, L., Wang, W., Li, C., Chen, L. (eds.) WAIM 2012. LNCS, vol. 7418, pp. 471–483. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-32281-5_45 CrossRefGoogle Scholar
  14. 14.
    Zhang, D., He, T.: P-Cruise: reducing cruising miles for taxicab networks. In: Proceedings of the 2012 IEEE 33rd Real-Time Systems Symposium, pp. 85–94 (2012)Google Scholar
  15. 15.
    Dong, H., Zhang, X., Dong, Y., et al.: Recommend a profitable cruising route for taxi drivers. In: Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems, pp. 2003–2008 (2014)Google Scholar
  16. 16.
    Wang, Y., Zheng, Y., Xue, Y.: Travel time estimation of a path using sparse trajectories. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 25–34 (2014)Google Scholar
  17. 17.
    Qu, M., Zhu, H., Liu, J., et al.: A cost-effective recommender system for taxi drivers. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 45–54 (2014)Google Scholar
  18. 18.
    Zhang, D., Sun, L., Li, B., et al.: Understanding taxi service strategies from taxi GPS traces. IEEE Trans. Intell. Transp. Syst. 16(1), 123–135 (2015)CrossRefGoogle Scholar
  19. 19.
    Yang, W., Wang, X., Rahimi, S.M., Luo, J.: Recommending profitable taxi travel routes based on big taxi trajectories data. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9078, pp. 370–382. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-18032-8_29 CrossRefGoogle Scholar
  20. 20.
    Ma, S., Zheng, Y., Wolfson, O.: Real-time city-scale taxi ridesharing. IEEE Trans. Knowl. Data Eng. 27(7), 1782–1795 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Yizhi Liu
    • 1
    • 2
    Email author
  • Jianxun Liu
    • 1
    • 2
  • Jianjun Wang
    • 1
    • 2
  • Zhuhua Liao
    • 1
    • 2
  • Mingdong Tang
    • 1
    • 2
  1. 1.School of Computer Science and EngineeringHunan University of Science and TechnologyXiangtanChina
  2. 2.Key Laboratory of Knowledge Processing and Networked ManufacturingXiangtanChina

Personalised recommendations