Skip to main content

Advertisement

SpringerLink
Log in

A quantum particle swarm optimization driven urban traffic light scheduling model

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

Abstract

Urban traffic congestion becomes a severe problem for many cities all around the world. How to alleviate traffic congestions in real cities is a challenging problem. Benefited from concise and efficient evolution rules, the Biham, Middleton and Levine (BML) model has a great potential to provide favorable results in the dynamic and uncertain traffic flows within an urban network. In this paper, an enhanced BML model (EBML) is proposed to effectively simulate the urban traffic where the timing scheduling optimization algorithm (TSO) based on the quantum particle swarm optimization is creatively introduced to optimize the timing scheduling of traffic light. The main contributions include that: (1) The actual urban road network with different two-way multi-lane roads is firstly mapped into the theoretical lattice space of BML. And the corresponding updating rules of each lattice site are proposed to control vehicle dynamics; (2) compared with BML, a much deeper insight into the phase transition and traffic congestions is provided in EBML. And the interference among different road capacities on forming traffic congestions is elaborated; (3) based on the scheduling simulation of EBML, TSO optimizes the timing scheduling of traffic lights to alleviate traffic congestions. Extensive comparative experiments reveal that TSO can achieve excellent optimization performances in real cases.

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

Similar content being viewed by others

References

  1. Al-Deek H, Lochrane TWP, Chandra CSR, Khattak A (2012) Diversion during unexpected congestion on toll roads: the role of traffic information displayed on dynamic message signs. IET Intell Transp Syst 6(2):97–106

    Article  Google Scholar 

  2. Sun R, Ochieng WY, Feng S (2015) An integrated solution for lane level irregular driving detection on highways. Transp Res Part C Emerg Technol 56:61–79

    Article  Google Scholar 

  3. Sun R, Han K, Hu J, Wang Y, Hu M, Ochieng WY (2016) Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements. Transp Res Part C Emerg Technol 69:193–207

    Article  Google Scholar 

  4. Finotti C, Gaio E (2014) Continuous model in dq frame of thyristor controlled reactors for stability analysis of high power electrical systems. Int J Electr Power Energy Syst 63:836–845

    Article  Google Scholar 

  5. Sazhin SS, Xie JF, Shishkova IN, Elwardany AE, Heikal MR (2013) A kinetic model of droplet heating and evaporation: effects of inelastic collisions and a non-unity evaporation coefficient. Int J Heat Mass Transf 56(1):525–537

    Article  Google Scholar 

  6. Peng GH, Cheng RJ (2013) A new car-following model with the consideration of anticipation optimal velocity. Phys A 392(17):3563–3569

    Article  MathSciNet  Google Scholar 

  7. Ding ZJ, Jiang R, Gao ZY, Wang BH, Long J (2013) Effect of overpasses in the Biham–Middleton–Levine traffic flow model with random and parallel update rule. Phys Rev E 88(2):022809

    Article  Google Scholar 

  8. Biham O, Middleton AA, Levine D (1992) Self-organization and a dynamical transition in traffic-flow models. arXiv preprint cond-mat/9206001

  9. Hu W, Wang H, Min Z (2014) A storage allocation algorithm for outbound containers based on the outer-inner cellular automaton. Inf Sci 281:147–171

    Article  MathSciNet  Google Scholar 

  10. Chowdhury D, Schadschneider A (1999) Self-organization of traffic jams in cities: effects of stochastic dynamics and signal periods. Phys Rev E 59(2):R1311

    Article  Google Scholar 

  11. Hu J, Song J, Zhang Y, Guo D, Xu Y (2005) Modelling and analysis for self-organization of urban traffic flow. In: Proceedings of intelligent transportation systems, vol 111. IEEE, pp 237–242

  12. D’Souza R (2005) Coexisting phases and lattice dependence of a cellular automaton model for traffic flow. Phys Rev E Stat Nonlin Soft Matter Phys 71(2):531–536

    Google Scholar 

  13. Chung et al (1995) Two-dimensional traffic flow problems with faulty traffic lights. Phys Rev E 51:772–774

    Article  Google Scholar 

  14. Xie et al (2013) Dynamical traffic light strategy in the Biham–Middleton–Levine model. Phys Rev E 87(2):022812

    Article  Google Scholar 

  15. Sun D, Jiang R, Wang B (2010) Timing of traffic lights and phase separation in two-dimensional traffic flow. Comput Phys Commun 181(2):301–304

    Article  Google Scholar 

  16. Fukui et al (1996) Flow of cars crossing with unequal velocities in a two-dimensional cellular automaton model. J Phys Soc Jpn 65(8):2514–2517

    Article  Google Scholar 

  17. Török and Kertész (1996) The green wave model of two-dimensional traffic: transitions in the flow properties and in the geometry of the traffic jam. Phys A 231(4):515–533

    Article  Google Scholar 

  18. Horiguchi T, Sakakibara T (1998) Numerical simulations for traffic-flow models on a decorated square lattice. Phys A 252(3):388–404

    Article  Google Scholar 

  19. Bouwmeester D, Ekert A, Zeilinger A (2010) The physics of quantum information: quantum cryptography, quantum teleportation, quantum computation, 2000 edn. Springer

  20. Li Y, Jiao L, Shang R, Stolkin R (2015) Dynamic-context cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Inf Sci 294:408–422

    Article  MathSciNet  Google Scholar 

  21. Maerivoet S, Moor BD (2005) Cellular automata models of road traffic. Phys Rep 419(1):1–64

    Article  MathSciNet  Google Scholar 

  22. Li P, Lam J, Cheung KC (2014) Velocity-dependent multi-objective control of vehicle suspension with preview measurements. Mechatronics 24:464–475

    Article  Google Scholar 

  23. Cardillo A, Scellato S, Latora V, Porta S (2006) Structural properties of planar graphs of urban street patterns. Phys Rev E 73(6):066107

    Article  Google Scholar 

  24. Barthélemy M, Flammini A (2008) Modeling urban street patterns. Phys Rev Lett 100(13):138702

    Article  Google Scholar 

  25. Leung JY (2004) Handbook of scheduling: algorithms, models, and performance analysis. CRC Press, Boca Raton

    MATH  Google Scholar 

  26. Tang D, Cai Y, Zhao J, Xue Y (2014) A quantum-behaved particle swarm optimization with memetic algorithm and memory for continuous non-linear large scale problems. Inf Sci 289(24):162–189

    Article  Google Scholar 

  27. Kachroudi S, Bhouri N (2009) A multimodal traffic responsive strategy using particle swarm optimization. Control Transport Sys 42:531–537

    Google Scholar 

  28. Liu J, Teo KL, Wang X, Wu C (2015) An exact penalty function-based differential search algorithm for constrained global optimization. Soft Comput 20(4):1305–1313

  29. Sánchez-Medina JJ, Galán-Moreno MJ, Rubio-Royo E (2010) Traffic signal optimization in “La Almozara” district in Saragossa under congestion conditions, using genetic algorithms, traffic microsimulation, and cluster computing. IEEE Trans Intell Transp Syst 11:132–141

    Article  Google Scholar 

  30. Hu Z, Bao Y, Xiong T (2014) Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression. Appl Soft Comput 25:15–25

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation, China (No. 61572369), the Hubei Province Natural Science Foundation (No. 2015CFB423), and Wenbin Hu is the grant recipient of these two foundations.

Author information

Authors and Affiliations

  1. School of Computer, Wuhan University, Wuhan City, 430072, Hubei Province, China

    Wenbin Hu, Huan Wang, Zhenyu Qiu, Cong Nie & Liping Yan

Authors
  1. Wenbin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhenyu Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cong Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Liping Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenbin Hu.

Ethics declarations

Conflict of interest

Wenbin Hu declares that he has no conflict of interest in this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, W., Wang, H., Qiu, Z. et al. A quantum particle swarm optimization driven urban traffic light scheduling model. Neural Comput & Applic 29, 901–911 (2018). https://doi.org/10.1007/s00521-016-2508-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2508-0

Keywords

Search

Navigation