Skip to main content

Advertisement

SpringerLink
Log in

Interactive traffic simulation model with learned local parameters

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this paper, we present a parameter learning method to reflect the rapidly changing behaviors in the traffic flow simulation process, in which we insert virtual vehicles into the real trajectory data. We come up with a real-virtual interaction model and then we use genetic algorithm to learn some parameters in the model with the purpose to get some specific driving characteristics. Then we propose a real-virtual interaction system to vividly simulate the various interaction behaviors between the real vehicles and the virtual ones. Our results are compared to the existing methods to prove the effectiveness of our presented method.

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

Similar content being viewed by others

References

  1. Ngsim: Next generation simulation program, 2008. http://ngsim-community.org/. United States Department of Transportation (US DOT) Federal Highway Administration (FHWA)

  2. Aw A, Rascle M (2000) Resurrection of second order models of traffic flow. SIAM J Appl Math 60(3):916–938

    Article  MathSciNet  MATH  Google Scholar 

  3. Bando M, Hasebe K, Nakayama A, Shibata A, Sugiyama Y (1995) Dynamical model of traffic congestion and numerical simulation. Phys Rev E 51:1035–1042. [Online]. Available: doi:10.1103/PhysRevE.51.1035 10.1103/PhysRevE.51.1035

    Article  Google Scholar 

  4. Chao Q, Deng Z, Jin X (2015) Vehicle-pedestrian interaction for mixed traffic simulation. J Visual Comp Animat 26(3-4):405–412. [Online]. Available. doi:10.1002/cav.1654

    Google Scholar 

  5. Cheu RL, Jin X, Srinivasa D, Ng KC, Ng YL (1998) Calibration of fresim for Singapore expressway using genetic algorithm. J Transp Eng 124(124):526–535

    Article  Google Scholar 

  6. Gazis DC, Herman R, Rothery RW (1961) Nonlinear follow-the-leader models of traffic flow. Oper Res 9(4):545–567. [Online]. Available. doi:10.1287/opre.9.4.545

    Article  MathSciNet  MATH  Google Scholar 

  7. Gerlough D (1955) Simulation of freeway traffic on a general-purpose discrete variable computer. University of California, Los Angeles. [Online]. Available: https://books.google.com.hk/books?id=3KarNwAACAAJ

    Google Scholar 

  8. Hourdakis J, Michalopoulos PG, Kottommannil J (2003) Practical procedure for calibrating microscopic traffic simulation models. Transportation Research Record Journal of the Transportation Research Board 1852(1):130–139

    Article  Google Scholar 

  9. Kesting A, Treiber M (2008) Calibrating car-following models using trajectory data: Methodological study. Transportation Research Record Journal of the Transportation Research Board 2088(2088):148–156

    Article  Google Scholar 

  10. Kesting A, Treiber M (2008) Calibration of car-following models using floating car data. Traffic and Granular Flow 07:117–127

    MATH  Google Scholar 

  11. Kesting A, Treiber M, Helbing D (2009) Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity. arXiv:0912.3613

  12. Lighthill MJ, Whitham GB (1955) On kinematic waves. ii. A theory of traffic flow on long crowded roads

    MATH  Google Scholar 

  13. Nagel K, Schreckenberg M (1992) A cellular automaton model for freeway traffic. J Phys I France 2(12):2221–2229. [Online]. Available. doi:10.1051/jp1:1992277

    Article  Google Scholar 

  14. Paris S, Pettré J, Donikian S (2007) Pedestrian reactive navigation for crowd simulation: a predictive approach

    Google Scholar 

  15. Redmill KA, Ozguner U (1999) Vatsim: a vehicle and traffic simulator

    Google Scholar 

  16. Sewall J, van den Berg JP, Lin MC, Manocha D (2011) Virtualized traffic: Reconstructing traffic flows from discrete spatiotemporal data. IEEE Trans Vis Comput Graph 17(1):26–37. [Online]. Available: http://dblp.uni-trier.de/db/journals/tvcg/tvcg17.html#SewallBLM11

    Article  Google Scholar 

  17. Sewall J, Wilkie D, Merrell P, Lin MC (2010) Continuum traffic simulation. Comput. Graph. Forum 29(2):439–448. [Online]. Available: http://dblp.uni-trier.de/db/journals/cgf/cgf29.html#SewallWML10

    Article  Google Scholar 

  18. Shen J, Jin X (2012) Detailed traffic animation for urban road networks. Graph Model 74(5):265–282

    Article  Google Scholar 

  19. Treiber M, Helbing D (2001) Microsimulations of freeway traffic including control measures. at-Automatisierungstechnik Methoden und Anwendungen der Steuerungs-Regelungs-und Informationstechnik 49(11/2001):478

    Google Scholar 

  20. Wilkie D, Sewall J, Lin M (2013) Flow reconstruction for data-driven traffic animation. ACM Trans Graph 32:89:1–89:10. [Online]. Available. doi:10.1145/2461912.2462021

    Article  MATH  Google Scholar 

  21. Zhang HM (2002) A non-equilibrium traffic model devoid of gas-like behavior. Transp Res B Methodol 36(3):275–290

    Article  Google Scholar 

Download references

Acknowledgments

The authors wish to acknowledge the support of NSFC grant 61300084, 61370141, 61300015, 91546123, 11372067, and 61425002, National High-tech R&D Program of China (Grant No. 2015AA7046207), the Open Project Program of the State Key Lab of CAD&CG (Grant No. A1511), Zhejiang University, and the Fundamental Research Funds for the Central Universities (Grant No. DUT15QY41).

Author information

Authors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Xin Yang, Shuai Li, Yong Zhang, Wanchao Su, Mingyue Zhang, Guozhen Tan & Xiaopeng Wei

  2. Dalian University, Dalian, China

    Qiang Zhang & Dongsheng Zhou

Authors
  1. Xin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuai Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wanchao Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mingyue Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Guozhen Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Dongsheng Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xiaopeng Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Yang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, X., Li, S., Zhang, Y. et al. Interactive traffic simulation model with learned local parameters. Multimed Tools Appl 76, 9503–9516 (2017). https://doi.org/10.1007/s11042-016-3560-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3560-6

Keywords

Search

Navigation