Skip to main content
SpringerLink
Log in

Using Intelligent Vehicle Infrastructure Integration for Reducing Congestion in Smart City

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

This paper proposes a novel cognitive cellular automata (CA) approach to traffic management that can adapt to immediate requirements, be applied for use in cross-area car societies, enhance system performance, and decrease traffic congestion problems. We propose a mechanism that operates in a cognitive radio mode to increase the channel-reuse rate and decrease the allocation of redundant channels. This approach provides the advantage of a heterogeneous communication interface based on cognitive mechanisms that recognize different transmission modulation modes. The receiver gets messages through different transmission modulation modes. In this work, we postulate vehicles connecting to traffic congestion computing centers by vehicle-to-roadside communications within a car society. Roadside units serve each road segment, and we suppose that every car has a navigation device. We propose an innovative congestion-reduction mechanism that provides directions to a vehicle’s navigation device after the driver sets the origin location and the destination. This mechanism calculates the congestion status of the upcoming road segment. By tracking the status of road segments from a point of origin to a destination, our proposed mechanism can handle cross-area car societies. The current study evaluates this approach’s performance by conducting computer simulations. Simulation results reveal the strengths of the proposed CA mechanism in terms of increased lifetime and increased congestion-avoidance for urban vehicular networks.

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

Similar content being viewed by others

References

  1. Milanes, V., Perez, J., Onieva, E., & Gonzalez, C. (2010). Controller for urban intersections based on wireless communications and fuzzy logic. IEEE Transactions on Intelligent Transportation Systems, 11(1), 243–248.

    Article  Google Scholar 

  2. Zhang, Xi, Hang, Su, & Chen, Hsiao-Hwa. (2006). Cluster-based multi-channel communications protocols in vehicle ad hoc networks. IEEE Wireless Communications, 13(5), 44–51.

    Article  Google Scholar 

  3. Thomas, T., Weijermars, W., & van Berkum, E. (2010). Predictions of urban volumes in single time series. IEEE Transactions on Intelligent Transportation Systems, 11(1), 71–80.

    Article  Google Scholar 

  4. Gokulan, B. P., & Srinivasan, D. (2010). Distributed geometric fuzzy multiagent urban traffic signal control. IEEE Transactions on Intelligent Transportation Systems, 11(3), 714–727.

    Article  Google Scholar 

  5. Wang, X. Y., & Ho, P.-H. (2010). A novel sensing coordination framework for CR-VANETs. IEEE Transactions on Vehicular Technology, 59(4), 1936–1948.

    Article  Google Scholar 

  6. Dashtinezhad, S., et al. (2004). Traffic view: A driver assistant device for traffic monitoring based on car-to-car communication. In Proceedings of IEEE VTC-Spring, Milan, Italy, May 17–19, 2004.

  7. ASTM E2213-03. (2003). Standard specification for telecommunications and information exchange between roadside and vehicle systems: 5 GHz band dedicated short range communications (DSRC) medium access control (MAC) and physical layer (PHY) Specifications. ASTM Int’l, July 2003.

  8. Ding, L., Melodia, T., Batalama, S. N., Matyjas, J. D., & Medley, M. J. (2010). Cross-layer routing and dynamic spectrum allocation in cognitive radio ad hoc networks. IEEE Transaction on Vehicular Technology, 59(4), 1969–1979.

    Article  Google Scholar 

  9. Saad, W., Han, Z., Hjorungnes, A., Niyato, D., & Hossain, E. (2011). Coalition formation games for distributed cooperation among roadside units in vehicular networks. IEEE Journal on Selected Areas in Communications, 29(1), 48–60.

    Article  Google Scholar 

  10. Olariu, S., & Weigle, M. C. (2009). Vehicular networks: From theory to practice. London: Chapman & Hall/CRC, Computer and Information Sciences Series.

    Book  Google Scholar 

  11. Zhang, Y., Zhao, J., Cao, G. (2007). On scheduling vehicle-roadside data access. In Proceedings of ACM international workshop on vehicular ad hoc networks (VANET) (pp. 9–18), Montreal, Canada, September 2007.

  12. Yang, K., Ou, S., Chen, H.-H., & He, J. (2007). A multihop peer-communication protocol with fairness guarantee for IEEE 802.16-based vehicular networks. IEEE Transactions on Vehicular Technology, 56(6), 3358–3370.

    Article  Google Scholar 

  13. Shrestha, B., Niyato, D., Han, Z., Hossain, E. (2008). Wireless access in vehicular environments using bittorrent and bargaining. In Proceedings of IEEE global communication conference, New Orleans, USA, November 2008.

  14. Niyato, D., Hossain, E., & Wang, P. (2011). Optimal channel access management with QoS support for cognitive vehicular networks. IEEE Transactions on Mobile Computing, 10(4), 573–591.

    Article  Google Scholar 

  15. Missoum, S., Gürdal, Z., & Setoodeh, S. (2005). Study of a new local update scheme for cellular automata in structural design. Structural and Multidisciplinary Optimization. doi:10.1007/s00158-004-0464-2.

    Google Scholar 

  16. Mamei, M., Roli, A., & Zambonelli, F. (2005). Emergence and control of macro-spatial structures in perturbed cellular automata, and implications for pervasive computing systems. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 35(3), 337–348.

    Article  Google Scholar 

  17. Niyato, D., & Hossain, E. (2008). Competitive spectrum sharing in cognitive radio networks: A dynamic game approach. IEEE Transactions on Wireless Communications, 7(7), 2651–2660.

    Article  Google Scholar 

  18. Mitola, J. (1999). Cognitive radio for flexible multimedia communications. In Proceedings of MoMuC’99 (pp. 3–10).

  19. Choy, M. C. (2005). Cooperative, hybrid multi-agent systems for distributed, real-time traffic signal control. Ph.D. dissertation, Dept. Elect. Comput. Eng., Nat. Univ. Singapore, Singapore, 2005.

  20. Choy, M. C. (2005). Cooperative, hybrid multi-agent systems for distributed, real-time traffic signal control. Ph.D. dissertation, Dept. Elect. Comput. Eng., Nat. Univ. Singapore, Singapore, 2005.

  21. Bi, Y., Liu, K.-H., Shen, X., Zhao, H. (2008). A multi-channel token ring protocol for inter-vehicle communications. In IEEE global telecommunications conference (pp. 1–5), 2008.

  22. Task Group. (2006). IEEE P802.11p: Wireless access in vehicular environments (WAVE), draft standard ed., IEEE Computer Society, 2006.

  23. Zang, Y. P., Stibor, L., Walke, B., Reumerman, H. J., Barroso, A. (2007). Towards broadband vehicular ad hoc networks: The vehicular mesh network (VMESH) MAC Protocol. In Proceedings of IEEE WCNC’07 (pp. 417–422), March 11–15, 2007.

  24. Committee SCC32. (2006). IEEE P1609.4 standard for wireless access in vehicular environments (WAVE): Multi-channel operation, draft standard. IEEE Intelligent Transportation Systems Council, 2006.

  25. Ma, J., Li, G. Y., & Juang, B. H. (2009). Signal processing in cognitive radio. Proceedings of the IEEE, 97(5), 805–823.

    Article  Google Scholar 

  26. Poor, H. V. (1994). An introduction to signal detection and estimation (2nd ed.). Berlin: Springer.

    Book  MATH  Google Scholar 

  27. Ross, S. (1996). Stochastic processes (2nd ed.). New York: Wiley.

    MATH  Google Scholar 

  28. Kelly, F. P. (1979). Reversibility and stochastic networks. New York: Wiley.

    MATH  Google Scholar 

  29. Wang, X. Y., Wong, A., & Ho, P.-H. (2009). Prioritized spectrum sensing in cognitive radio based on spatiotemporal statistical fusion. In Proceedings of IEEE WCNC (pp. 1–6), April 2009.

Download references

Acknowledgments

We thank the National Science Council of Taiwan for funding this research (Project No.: MOST 104-2221-E-218-006-).

Author information

Authors and Affiliations

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

    Gwo-Jiun Horng

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

    Sheng-Tzong Cheng

Authors
  1. Gwo-Jiun Horng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. 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

Horng, GJ., Cheng, ST. Using Intelligent Vehicle Infrastructure Integration for Reducing Congestion in Smart City. Wireless Pers Commun 91, 861–883 (2016). https://doi.org/10.1007/s11277-016-3501-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-016-3501-8

Keywords

Search

Navigation