Skip to main content

Advertisement

SpringerLink
Log in

Using Non-cooperative Game Theory for Taxi-Sharing Recommendation Systems

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

This paper presents a recommendation mechanism for taxi-sharing. The first aim of our model is to respectively recommend taxis and passengers for picking up passengers quickly and finding taxis easily. The second purpose is providing taxi-sharing service for passengers who want to save the payment. In our method, we analyze the historical global positioning system trajectories generated by 10,357 taxis during 110 days and present the service region with time-dependent R-Tree. We formulate the problem of choosing the paths among the taxis in the same region by using non-cooperative game theory, and find out the solution of this game which is known as Nash equilibrium. The simulation of SUMO, MOVE, and TraCI are adopted to fit our model to verify the proposed recommendation mechanism. The results show that our method can find taxis and passengers efficiently. In addition, applying our method can reduce the payment of passengers and increase the taxi revenue by taxi-sharing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Quality Carpool Service. http://www.qcar.org.tw/.

  2. Yan, S., Chen, C.-Y., & Chang, S.-C. (2003). A car pooling model and solution method with stochastic vehicle travel times. IEEE Transactions on Intelligent Transportation Systems, 15(1), 47–61.

    Article  Google Scholar 

  3. Dimitrakopoulos, G., Demestichas, P., & Koutra, V. (2012). Intelligent management functionality for improving transportation efficiency by means of the car pooling concept. IEEE Transactions on Intelligent Transportation Systems, 13(2), 424–436.

    Article  Google Scholar 

  4. Guttman, A. (1984). R-trees a dynamic index structure for spatial searching. In Proceedings of the ACM SIGMOD international conference on Management of data (pp. 47–57).

  5. Haapasalo, T., Jaluta, I., Sippu, S., & Soisalon-Soininen, E. (2013). On the recovery of R-trees. IEEE Transactions on Knowledge and Data Engineering, 25(1), 145–157.

    Article  Google Scholar 

  6. Kao, B., Lee, S. D., Lee, F. K. F., Cheung, D. W., & Ho, W.-S. (2010). Clustering uncertain data using voronoi diagrams and R-tree index. IEEE Transactions on Knowledge and Data Engineering, 22(9), 1219–1233.

    Article  Google Scholar 

  7. Myerson, R. B. (1991). Game theory: Analysis of conflict (pp. 568). Cambridge, MA: Harvard University Press.

    MATH  Google Scholar 

  8. Morgenstern, O., & von Neumann, J. (1947). The theory of games and economic behavior. Princeton: Princeton University Press.

    MATH  Google Scholar 

  9. Ma, Z. S., & Krings, A. W. (2011). Dynamic hybrid fault modeling and extended evolutionary game theory for reliability, survivability and fault tolerance analyses. IEEE Transactions on Reliability, 60(1), 180–196.

    Article  Google Scholar 

  10. Zhao, L., Zhang, J., & Zhang, H. (2008). Using incompletely cooperative game theory in wireless mesh networks. IEEE Network, 22(1), 39–44.

    Google Scholar 

  11. Umehara, E., & Ohta, T. (2009). Using game theory to investigate risk information disclosure by government agencies and satisfying the public—The role of the guardian agent. IEEE Transactions on Humans, 39(2), 321–330.

    Google Scholar 

  12. Liang, X., & Xiao, Y. (2013). Game theory for network security. IEEE Communications Surveys & Tutorials, 15(1), 472–486.

    Article  Google Scholar 

  13. Ahmad, I., & Luo, J. (2006). On using game theory to optimize the rate control in video coding. IEEE Circuits and Systems for Video Technology, 16(2), 209–219.

    Article  Google Scholar 

  14. Alavi, S. M., & Zhou, C. (2012). Resource allocation scheme for orthogonal frequency division multiple access networks based on cooperative game theory. International Journal of Communication Systems,. doi:10.1002/dac.2398.

    Google Scholar 

  15. Yuan, N. J., Zheng, Y., Zhang, L., & Xie, X. (2012). Optimal power control for wireless cooperative relay networks: A cooperative game theoretic approach. International Journal of Communication Systems,. doi:10.1002/dac.2316.

    Google Scholar 

  16. K. D. and R. C., SUMO (Simulation of Urban MObility). (2007). German Aerospace Centre. http://sumo.sourceforge.net/.

  17. TraCI (Traffic Control Interface). http://sourceforge.net/apps/mediawiki/sumo/?title=TraCI.

  18. MOVE (MObility model generator for VEhicular networks). (2007). Rapid generation of realistic simulation for VANET. http://lens1.csie.ncku.edu.tw/MOVE/index.htm.

  19. Yuan, N. J., Zheng, Y., Zhang, L., & Xie, X. (2013). T-finder: A recommender system for finding passengers and vacant taxis. IEEE Transactions on Knowledge and Data Engineering, 25(10), 2390–2403.

    Article  Google Scholar 

  20. d’Orey, P. M., Fernandes, R., & Ferreira, M. (2012). Empirical evaluation of dynamic and distribution taxi-sharing system. IEEE International Conference on Intelligent Transportation Systems, 1(1), 140–146.

    Google Scholar 

  21. Ma, S., Zheng, Y., & Wolfson, O. (2013). T-share: A large-scale dynamic taxi ridesharing service. IEEE International Conference on Data Engineering, 1(1), 410–421.

    Google Scholar 

  22. Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., & Huang, Y. (2010). 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).

  23. Yuan, J., Zheng, Y., Xie, X., & Sun, G. (2011). Driving with knowledge from the physical world. In The 17th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 316–324).

Download references

Acknowledgments

This research received funding from the Headquarters of University Advancement at the National Cheng Kung University, which is sponsored by the Ministry of Education, Taiwan, ROC.

Author information

Authors and Affiliations

  1. National Chung-Shan Institute of Science and Technology, Taoyuan, Taiwan

    Jian-Pan Li

  2. Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Tainan, Taiwan

    Gwo-Jiun Horng

  3. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan

    Yin-Jun Chen & Sheng-Tzong Cheng

Authors
  1. Jian-Pan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gwo-Jiun Horng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yin-Jun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sheng-Tzong Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gwo-Jiun Horng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, JP., Horng, GJ., Chen, YJ. et al. Using Non-cooperative Game Theory for Taxi-Sharing Recommendation Systems. Wireless Pers Commun 88, 761–786 (2016). https://doi.org/10.1007/s11277-016-3202-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-016-3202-3

Keywords

Search

Navigation