On Efficient Passenger Assignment for Group Transportation

  • Jiajie XuEmail author
  • Guanfeng Liu
  • Kai Zheng
  • Chengfei Liu
  • Haoming Guo
  • Zhiming Ding
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9049)


With the increasing popularity of LBS services, spatial assignment has become an important problem nowadays. Nevertheless most existing works use Euclidean distance as the measurement of spatial proximity. In this paper, we investigate a variant of spatial assignment problem with road networks as the underlying space. Given a set of passengers and a set of vehicles, where each vehicle waits for the arrival of all passengers assigned to it, and then carries them to the same destination, our goal is to find an assignment from passengers to vehicles such that all passengers can arrive at earliest together. Such a passenger assignment problem has various applications in real life. However, finding the optimal assignment efficiently is challenging due to high computational cost in the fastest path search and combinatorial nature of capacity constrained assignment. In this paper, we first propose two exact solutions to find the optimal results, and then an approximate solution to achieve higher efficiency by trading off a little accuracy. Finally, performances of all proposed algorithms are evaluated on a real dataset.


Bipartite Graph Road Network Travel Cost Critical Pair Spatial Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Balinski, M.L., Gomory, R.E.: A primal method for the assignment and transportation problems. Management Sci. 10(3), 578–593 (1964)CrossRefGoogle Scholar
  2. 2.
    Brakatsoulas, S., Pfoser, D., Salas, R., Wenk, C.: On map-matching vehicle tracking data. In: Proceedings of VLDB, pp. 853–864 (2005)Google Scholar
  3. 3.
    Brunsch, T., Cornelissen, K., Manthey, B., Röglin, H.: Smoothed analysis of the successive shortest path algorithm. In: Proceedings of SODA, pp. 1180–1189 (2013)Google Scholar
  4. 4.
    Dantzig, G., Ramser, J.: The truck dispatching problem. Management Science 6, 80–91 (1959)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Desrochers, M., Jones, C., Lenstra, J.K., Savelsbergh, M., Stougie, L.: Towards a model and algorithm management system for vehicle routing and scheduling problems. Decision Support Systems 2(25), 109–133 (1999)CrossRefGoogle Scholar
  6. 6.
    Duan, R., Su, H.-H.: A scaling algorithm for maximum weight matching in bipartite graphs. In: Proceedings of SODA, pp. 1413–1424 (2012)Google Scholar
  7. 7.
    Geisberger, R., Sanders, P., Schultes, D., Delling, D.: Contraction hierarchies: faster and simpler hierarchical routing in road networks. In: McGeoch, C.C. (ed.) WEA 2008. LNCS, vol. 5038, pp. 319–333. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  8. 8.
    Matijevic, D., Sanders, P., Bast, H., Funke, S., Schultes, D.: In transit to constant time shortest-path queries in road networks. In: Proceedings of ALENEX (2007)Google Scholar
  9. 9.
    Kaul, M., Wong, R.C.-W., Yang, B., Jensen, C.S.: Finding shortest paths on terrains by killing two birds with one stone. PVLDB 7(1), 73–84 (2013)Google Scholar
  10. 10.
    Kuhn, H.W.: The hungarian method for the assignment problem. Naval Research Logistics Quarterly 2, 83–97 (1955)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Liu, K., Li, Y., He, F., Xu, J., Ding, Z.: Effective map-matching on the most simplified road network. In: Proceedings of ACM SIGSPATIAL GIS, pp. 609–612 (2012)Google Scholar
  12. 12.
    Ma, S., Zheng, Y., Wolfson, O.: T-share: A large-scale dynamic taxi ridesharing service. In: Proceedings of ICDE, pp. 410–421 (2013)Google Scholar
  13. 13.
    Munkres, J.: Algorithms for the assignment and transportation problems. J. Soc. Indust. Appl. Math. 5, 32–38 (1957)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Pournajaf, L., Xiong, L., Sunderam, V.S., Goryczka, S.: Spatial task assignment for crowd sensing with cloaked locations. In: Proceedings of MDM, pp. 73–82 (2014)Google Scholar
  15. 15.
    Sanders, P., Schultes, D.: Engineering highway hierarchies. In: Azar, Y., Erlebach, T. (eds.) ESA 2006. LNCS, vol. 4168, pp. 804–816. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  16. 16.
    Sankaranarayanan, J., Samet, H.: Query processing using distance oracles for spatial networks. IEEE TKDE 22(8), 1158–1175 (2010)Google Scholar
  17. 17.
    U, L.H., Mouratidis, K., Mamoulis, N.: Continuous spatial assignment of moving users. VLDBJ 19(2), 141–160 (2010)CrossRefGoogle Scholar
  18. 18.
    U, L.H., Yiu, M.L., Mouratidis, K., Mamoulis, N.: Capacity constrained assignment in spatial databases. In: Proceedings of SIGMOD, pp. 15–28 (2008)Google Scholar
  19. 19.
    U, L.H., Yiu, M.L., Mouratidis, K., Mamoulis, N.: Optimal matching between spatial datasets under capacity constraints. ACM TODS 35(2), 1–43 (2010)CrossRefGoogle Scholar
  20. 20.
    Wong, R.C.-W., Tao, Y., Fu, A.W.-C., Xiao, X.: On efficient spatial matching. In: Proceedings of VLDB, pp. 579–590 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jiajie Xu
    • 1
    • 5
    Email author
  • Guanfeng Liu
    • 1
    • 5
  • Kai Zheng
    • 2
  • Chengfei Liu
    • 3
  • Haoming Guo
    • 4
  • Zhiming Ding
    • 4
  1. 1.Department of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia
  3. 3.School of Software and Electrical EngineeringSwinburne University of TechnologyMelbourneAustralia
  4. 4.Institute of SoftwareChinese Academy of SciencesBeijingChina
  5. 5.Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjingChina

Personalised recommendations