Skip to main content

Advertisement

Springer Nature Link
Log in

Multiple ACO-based method for solving dynamic MSMD traffic routing problem in connected vehicles

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In this study, we focus on dynamic traffic routing of connected vehicles with various origins and destinations; this is referred to as a multi-source multi-destination traffic routing problem. Ant colony optimization (ACO)-based routing method, together with the idea of coloring ants, is proposed to solve the defined problem in a distributed manner. Using the concept of coloring ants, traffic flows of connected vehicles to different destinations can be distinguished. To evaluate the performance of the proposed method, we perform simulations on the multi-agent NetLogo platform. The simulation results indicate that the ACO-based routing method outperforms the shortest path-based routing method (i.e., given the same simulation period, the average travel time decreases by 8% on average and by 11% in the best case, whereas the total number of arrived vehicles increases by 13% on average and by 23% in the best case).

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. https://ccl.northwestern.edu/netlogo/.

References

  1. Balbo F, Bhouri N, Pinson S (2016) Bimodal traffic regulation system: a multi-agent approach. Web Intell 14(2):139–151. https://doi.org/10.3233/WEB-160336

    Article  Google Scholar 

  2. Blum C, Dorigo M (2004) The hyper-cube framework for ant colony optimization. IEEE Trans Syst Man Cybern Part B 34(2):1161–1172. https://doi.org/10.1109/TSMCB.2003.821450

    Article  Google Scholar 

  3. Bui KHN, Jung JJ (2018) Cooperative game-theoretic approach to traffic flow optimization for multiple intersections. Comput Electr Eng 71:1012–1024. https://doi.org/10.1016/j.compeleceng.2017.10.016

    Article  Google Scholar 

  4. Bui KHN, Jung JJ (2018) Internet of agents framework for connected vehicles: a case study on distributed traffic control system. J Parallel Distrib Comput 116:89–95. https://doi.org/10.1016/j.jpdc.2017.10.019

    Article  Google Scholar 

  5. Bui KHN, Jung JJ (2019) ACO-based dynamic decision making for connected vehicles in IOT system. IEEE Trans Industr Inf 15(10):5648–5655. https://doi.org/10.1109/TII.2019.2906886

    Article  Google Scholar 

  6. Bui KHN, Jung JJ (2019) Computational negotiation-based edge analytics for smart objects. Inf Sci 480:222–236. https://doi.org/10.1016/j.ins.2018.12.046

    Article  MathSciNet  Google Scholar 

  7. Chakraborty A, Kar AK (2017) Swarm intelligence: a review of algorithms. Springer, Cham, pp 475–494. https://doi.org/10.1007/978-3-319-50920-4_19

    Book  Google Scholar 

  8. Cong Z, De Schutter B, Babuška R (2013) Ant colony routing algorithm for freeway networks. Transp Res Part C Emerg Technol 37:1–19. https://doi.org/10.1016/j.trc.2013.09.008

    Article  Google Scholar 

  9. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39. https://doi.org/10.1109/ci-m.2006.248054

    Article  Google Scholar 

  10. Jabbarpour MR, Jalooli A, Shaghaghi E, Noor RM, Rothkrantz LJM, Khokhar RH, Anuar NB (2014) Ant-based vehicle congestion avoidance system using vehicular networks. Eng Appl Artif Intell 36:303–319. https://doi.org/10.1016/j.engappai.2014.08.001

    Article  Google Scholar 

  11. Jerry K, Yujun K, Kwasi O, Enzhan Z, Parfait T (2015) Netlogo implementation of an ant colony optimisation solution to the traffic problem. IET Intel Transp Syst 9(9):862–869. https://doi.org/10.1049/iet-its.2014.0285

    Article  Google Scholar 

  12. Jovanović A, Nikolić M, Teodorović D (2017) Area-wide urban traffic control: a bee colony optimization approach. Transp Res Part C Emerg Technol 77:329–350. https://doi.org/10.1016/j.trc.2017.02.006

    Article  Google Scholar 

  13. Kumar PM, Gandhi UD, Manogaran G, Sundarasekar R, Chilamkurti N, Varatharajan R (2018) Ant colony optimization algorithm with internet of vehicles for intelligent traffic control system. Comput Netw 144:154–162. https://doi.org/10.1016/j.comnet.2018.07.001

    Article  Google Scholar 

  14. Mirheli A, Tajalli M, Hajibabai L, Hajbabaie A (2019) A consensus-based distributed trajectory control in a signal-free intersection. Transp Res Part C Emerg Technol 100:161–176. https://doi.org/10.1016/j.trc.2019.01.004

    Article  Google Scholar 

  15. Rehman A, Rathore MM, Paul A, Saeed F, Ahmad RW (2018) Vehicular traffic optimisation and even distribution using ant colony in smart city environment. IET Intel Transp Syst 12(7):594–601. https://doi.org/10.1049/iet-its.2017.0308

    Article  Google Scholar 

  16. Ser JD, Osaba E, Sanchez-Medina JJ, Fister I, Fister I (2020) Bioinspired computational intelligence and transportation systems: a long road ahead. IEEE Trans Intell Transp Syst 21:466–495. https://doi.org/10.1109/TITS.2019.2897377

    Article  Google Scholar 

  17. Storck CR, de Duarte-Figueiredo F (2019) A 5g V2X ecosystem providing internet of vehicles. Sensors 19(3):550. https://doi.org/10.3390/s19030550

    Article  Google Scholar 

  18. Tsai C, Lai C, Vasilakos AV (2014) Future internet of things: open issues and challenges. Wirel Netw 20(8):2201–2217. https://doi.org/10.1007/s11276-014-0731-0

    Article  Google Scholar 

  19. Wang H, Rudy K, Li J, Ni D (2010) Calculation of traffic flow breakdown probability to optimize link throughput. Appl Math Model 34(11):3376–3389. https://doi.org/10.1016/j.apm.2010.02.027

    Article  MathSciNet  MATH  Google Scholar 

  20. Yeow K, Gani A, Ahmad RW, Rodrigues JJPC, Ko K (2018) Decentralized consensus for edge-centric internet of things: a review, taxonomy, and research issues. IEEE Access 6:1513–1524. https://doi.org/10.1109/ACCESS.2017.2779263

    Article  Google Scholar 

  21. Zedadra O, Guerrieri A, Jouandeau N, Spezzano G, Seridi H, Fortino G (2018) Swarm intelligence-based algorithms within IOT-based systems: a review. J Parallel Distrib Comput 122:173–187. https://doi.org/10.1016/j.jpdc.2018.08.007

    Article  Google Scholar 

  22. Zhang Y, Ni Q (2017) A coordinated traffic control on urban expressways with modified particle swarm optimization. KSCE J Civ Eng 21(2):501–511. https://doi.org/10.1007/s12205-017-1505-x

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIP) (NRF-2019K1A3A1A80113259).

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Chung-Ang University, Seoul, 156-756, Korea

    Tri-Hai Nguyen & Jason J. Jung

Authors
  1. Tri-Hai Nguyen
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jason J. Jung
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Jason J. Jung.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nguyen, TH., Jung, J.J. Multiple ACO-based method for solving dynamic MSMD traffic routing problem in connected vehicles. Neural Comput & Applic 33, 6405–6414 (2021). https://doi.org/10.1007/s00521-020-05402-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05402-8

Keywords