Advertisement

Cooperative Lane-Change and Longitudinal Behaviour Model Extension for TraffSim

  • Christian BackfriederEmail author
  • Gerald Ostermayer
  • Manuel Lindorfer
  • Christoph F. Mecklenbräuker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9704)

Abstract

Behaviors of drivers have an important influence on the throughput, safety and traffic flow of vehicular transportation systems. Especially in simulation scenarios, a smooth, realistic and fully reliable lane-change model is a precondition to achieve reasonable results. An extraordinary challenge is provided by situations with multiple congested lanes, including vehicles intending to change to the adjacent lane even if the target lane is occupied by vehicles stuck in a traffic jam. This paper addresses this special use case by introducing Cooperative Lane-Change and Longitudinal Behaviour Model Extension (CLLxt), which can be applied as an extension to models from literature. The result is a simple but well-functioning cooperative model, which covers both participants, the vehicle intending to change the lane and others which need to react to this intention by providing space. The utilization of CLLxt is demonstrated with an example in TraffSim.

Keywords

Lane-change model Traffic simulation Cooperative lane-change 

Notes

Acknowledgments

This project has been co-financed by the European Union using financial means of the European Regional Development Fund (EFRE). Further information to IWB/EFRE is available at www.efre.gv.at.

References

  1. 1.
    Treiber, M., Kesting, A.: An open-source microscopic traffic simulator. IEEE Intell. Transp. Syst. Mag. 2(3), 6–13 (2010)CrossRefzbMATHGoogle Scholar
  2. 2.
    Miller, J., Horowitz, E.: FreeSim - a free real-time freeway traffic simulator. In: IEEE Intelligent Transportation Systems Conference, ITSC 2007, pp. 18–23, September 2007Google Scholar
  3. 3.
    Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: SUMO - simulation of urban mobility - an overview. In: SIMUL 2011, The Third International Conference on Advances in System Simulation, pp. 55–60, October 2011Google Scholar
  4. 4.
    Gora, P.: Traffic simulation framework. In: 2012 UKSim 14th International Conference on Computer Modelling and Simulation, pp. 345–349, March 2012Google Scholar
  5. 5.
    Backfrieder, C., Ostermayer, G., Mecklenbräuker, C.: Extended from EMS2013: TraffSim - a traffic simulator for investigations of congestion minimization through dynamic vehicle rerouting. Int. J. Simul. Syst. Sci. Technol. IJSSST V15 15, 8–13 (2015)Google Scholar
  6. 6.
    Helbing, D.: Traffic and related self-driven many-particle systems. Rev. Modern Phy. 73(4), 1067–1141 (2001)CrossRefGoogle Scholar
  7. 7.
    Rahman, M., Chowdhury, M., Xie, Y., He, Y.: Review of microscopic lane-changing models and future research opportunities. IEEE Trans. Intell. Transp. Syst. 14(4), 1942–1956 (2013)CrossRefGoogle Scholar
  8. 8.
    Kesting, A., Treiber, M., Helbing, D.: General lane-changing model MOBIL for car-following models. Transp. Res. Rec. J. Transp. Res. Board 1999, 86–94 (2007)CrossRefGoogle Scholar
  9. 9.
    Brackstone, M., McDonald, M., Wu, J.: Lane changing on the motorway: factors affecting its occurrence, and their implications. In: 9th International Conference on Road Transport Information and Control, 1998, (Conf. Publ. No. 454), pp. 160–164, April 1998Google Scholar
  10. 10.
    Rodemerk, C., Habenicht, S., Weitzel, A., Winner, H., Schmitt, T.: Development of a general criticality criterion for the risk estimation of driving situations and its application to a maneuver-based lane change assistance system. In: 2012 IEEE Intelligent Vehicles Symposium (IV), pp. 264–269, June 2012Google Scholar
  11. 11.
    Eidehall, A., Pohl, J., Gustafsson, F., Ekmark, J.: Toward autonomous collision avoidance by steering. IEEE Trans. Intell. Transp. Syst. 8(1), 84–94 (2007)CrossRefGoogle Scholar
  12. 12.
    Nagel, K., Wolf, D., Wagner, P., Simon, P.: Two-lane traffic rules for cellular automata: a systematic approach. Phy. Rev. E 58(2), 1425–1437 (1998). arXiv:cond-mat/9712196 CrossRefGoogle Scholar
  13. 13.
    Gipps, P.: A model for the structure of lane-changing decisions. Transp. Res. Part B Methodol. 20(5), 403–414 (1986)CrossRefGoogle Scholar
  14. 14.
    Hidas, P.: Modelling vehicle interactions in microscopic simulation of merging and weaving. Transp. Res. Part C Emerg. Technol. 13(1), 37–62 (2005)CrossRefGoogle Scholar
  15. 15.
    Toledo, T., Koutsopoulos, H., Ben-Akiva, M.: Integrated driving behavior modeling. Transp. Res. Part C Emerg. Technol. 15(2), 96–112 (2007)CrossRefGoogle Scholar
  16. 16.
    Li, K., Ioannou, P.: Modeling of traffic flow of automated vehicles. IEEE Trans. Intell. Transp. Syst. 5(2), 99–113 (2004)CrossRefGoogle Scholar
  17. 17.
    Kumar, P., Merzouki, R., Conrard, B., Coelen, V., Bouamama, B.O.: Multilevel modeling of the traffic dynamic. IEEE Trans. Intell. Transp. Syst. 15(3), 1066–1082 (2014)CrossRefGoogle Scholar
  18. 18.
    Zhang, F., Li, J., Zhao, Q.: Single-lane traffic simulation with multi-agent system. In: 2005 IEEE Intelligent Transportation Systems, Proceedings, pp. 56–60, September 2005Google Scholar
  19. 19.
    Kesting, A., Treiber, M., Helbing, D.: Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity. Philos. Trans. R. Soc. A Math. Phy. Eng. Sci. 368(1928), 4585–4605 (2010). arXiv:0912.3613 CrossRefzbMATHGoogle Scholar
  20. 20.
    Kesting, A., Treiber, M., Schönhof, M., Kranke, F., Helbing, D.: Jam-Avoiding Adaptive Cruise Control (ACC) and its Impact on Traffic Dynamics. In: Schadschneider, A., Pöschel, T., Kühne, R., Schreckenberg, M., Wolf, D.E. (eds.) Traffic and Granular FlowâĂŹ05, pp. 633–643. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-47641-2_62 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Christian Backfrieder
    • 1
    Email author
  • Gerald Ostermayer
    • 1
  • Manuel Lindorfer
    • 1
  • Christoph F. Mecklenbräuker
    • 2
  1. 1.Research Group Networks and MobilityUAS Upper AustriaHagenbergAustria
  2. 2.Christian Doppler Lab Wireless Technology for Sustainable MobilityVienna University of TechnologyViennaAustria

Personalised recommendations