Skip to main content

Microscopic Cycling Behavior Model Using Differential Game Theory

  • Conference paper
  • First Online:
Traffic and Granular Flow '17 (TGF 2017)

Included in the following conference series:

  • 757 Accesses

Abstract

In order to develop design guidelines and assess the implications on traffic flow operations and safety, microscopic behavioral models are used. The increasing interest in cycling in cities necessitates the development of a model that captures the movement of cyclists. Given the fact that cyclists exert effort for their motion, the theory of effort minimization can be adopted from the micro-economic theory of subjective utility maximization. Also, due to their size and flexibility, close interactions between cyclists are possible, which can be resolved by solving a differential game. This solution determines the optimal control strategy of a cyclist and is, hence, a microscopic cycling model. In this paper we explain the derivation of such a model. Moreover, we demonstrate its plausibility by interpreting the derived equations and face validating the model. The results indicate the need to consider traffic rules and to collect bicycle trajectory data.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Antonini, G., Bierlaire, M., Weber, M.: Discrete choice models of pedestrian walking behavior. Transp. Res. B Methodol. 40(8), 667–687 (2006)

    Article  Google Scholar 

  2. Anvari, B., Bell, M.G., Sivakumar, A., Ochieng, W.Y.: Modelling shared space users via rule-based social force model. Transp. Res. C Emerg. Technol. 51, 83–103 (2015)

    Article  Google Scholar 

  3. Falkenberg, G., Blase, A., Bonfranchi, T., Cossé, L., Draeger, W., Vortisch, P., Kautzsch, L., Stapf, H., Zimmermann, A.: Bemessung von radverkehrsanlagen unter verkehrstechnischen gesichtspunkten. Berichte der Bundesanstalt fuer Strassenwesen. Unterreihe Verkehrstechnik (103) (2003)

    Google Scholar 

  4. Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator VISSIM. In: Fundamentals of Traffic Simulation, pp. 63–93. Springer, New York (2010)

    Chapter  Google Scholar 

  5. Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282 (1995)

    Article  Google Scholar 

  6. Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407(6803), 487–490 (2000)

    Article  Google Scholar 

  7. Helbing, D., Farkas, I.J., Molnar, P., Vicsek, T.: Simulation of pedestrian crowds in normal and evacuation situations. In: Pedestrian and Evacuation Dynamics, vol. 21(2), pp. 21–58. Springer, New York (2002)

    Google Scholar 

  8. Hoogendoorn, S., Daamen, W.: Bicycle headway modeling and its applications. Transp. Res. Rec. 2587, 34–40 (2016)

    Article  Google Scholar 

  9. Hoogendoorn, S., HL Bovy, P.: Simulation of pedestrian flows by optimal control and differential games. Optimal Control Appl. Methods 24(3), 153–172 (2003)

    Article  MathSciNet  Google Scholar 

  10. Isaacs, R.: Differential Games: A Mathematical Theory with Applications to Warfare and Pursuit, Control and Optimization. Courier Corporation, New York (1999)

    MATH  Google Scholar 

  11. Kong, J., Pfeiffer, M., Schildbach, G., Borrelli, F.: Kinematic and dynamic vehicle models for autonomous driving control design. In: Intelligent Vehicles Symposium (IV), 2015 IEEE, pp. 1094–1099. IEEE, Piscataway (2015)

    Google Scholar 

  12. Luo, Y., Jia, B., Liu, J., Lam, W.H., Li, X., Gao, Z.: Modeling the interactions between car and bicycle in heterogeneous traffic. J. Adv. Transp. 49(1), 29–47 (2015)

    Article  Google Scholar 

  13. Nagel, K., Schreckenberg, M.: A cellular automaton model for freeway traffic. J. Phys. I 2(12), 2221–2229 (1992)

    Google Scholar 

  14. Pontryagin, L.S.: Mathematical Theory of Optimal Processes. CRC Press, New York (1987)

    Google Scholar 

Download references

Acknowledgements

This research was supported by the ALLEGRO project (no. 669792), which is financed by the European Research Council and Amsterdam Institute for Advanced Metropolitan Solutions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alexandra Gavriilidou .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gavriilidou, A., Yuan, Y., Farah, H., Hoogendoorn, S.P. (2019). Microscopic Cycling Behavior Model Using Differential Game Theory. In: Hamdar, S. (eds) Traffic and Granular Flow '17. TGF 2017. Springer, Cham. https://doi.org/10.1007/978-3-030-11440-4_54

Download citation

Publish with us

Policies and ethics