Skip to main content
Log in

The “backward-looking” effect in the continuum model considering a new backward equilibrium velocity function

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

In this paper, a new continuum traffic model is developed considering the backward-looking effect through a new positive backward equilibrium speed function. As compared with the conventional full velocity difference model, the backward equilibrium velocity function, which is largely acceptably grounded from mathematical and physical perspectives, plays an important role in significantly enhancing the stability of the traffic flow field. A linear stability condition is derived to demonstrate the flow neutralization capacity of the model, whereas the Korteweg–de Vries–Burgers equation and the attendant analytical solution are deduced using nonlinear analysis to observe the traffic flow behavior near the neutral stability condition. A numerical simulation, used to determine the flow stability enhancement efficiency of the model, is also conducted to verify the theoretical results.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Ma, G., Ma, M., Liang, S., Wang, Y., Zhang, Y.: An improved car-following model accounting for the time-delayed velocity difference and backward looking effect. Commun. Nonlinear Sci. Numer. Simul. 85, 105221 (2020). https://doi.org/10.1016/J.CNSNS.2020.105221

    Article  MathSciNet  MATH  Google Scholar 

  2. Tanimoto, J., Nakamura, K.: Social dilemma structure hidden behind traffic flow with route selection. Phys. A Stat. Mech. its Appl. 459, 92–99 (2016). https://doi.org/10.1016/J.PHYSA.2016.04.023

    Article  MathSciNet  MATH  Google Scholar 

  3. Tanimoto, J.: Traffic Flow Analysis Dovetailed with Evolutionary Game Theory. In: Springer 6. pp. 159–182 (2015)

  4. Ge, H., Meng, X., Ma, J., Lo, S., Ge, H., Meng, X., Ma, J., Lo, S.: An improved car-following model considering influence of other factors on traffic jam. PhLA. 377, 9–12 (2012). https://doi.org/10.1016/J.PHYSLETA.2012.10.005

    Article  MATH  Google Scholar 

  5. Ou, H., Tang, T.Q., Zhang, J., Zhou, J.M.: A car-following model accounting for probability distribution. Phys. A Stat. Mech. Appl. 505, 105–113 (2018). https://doi.org/10.1016/j.physa.2018.03.072

    Article  Google Scholar 

  6. Orosz, G., Krauskopf, B., Wilson, R.E.: Bifurcations and multiple traffic jams in a car-following model with reaction-time delay. Phys. D Nonlinear Phenom. 211, 277–293 (2005). https://doi.org/10.1016/j.physd.2005.09.004

    Article  MathSciNet  MATH  Google Scholar 

  7. Guo, L., Zhao, X., Yu, S., Li, X., Shi, Z.: An improved car-following model with multiple preceding cars’ velocity fluctuation feedback. Phys. A Stat. Mech. Appl. 471, 436–444 (2017). https://doi.org/10.1016/j.physa.2016.12.071

    Article  Google Scholar 

  8. An, S., Xu, L., Qian, L., Chen, G., Luo, H., Li, F.: Car-following model for autonomous vehicles and mixed traffic flow analysis based on discrete following interval. Phys. A Stat. Mech. its Appl. 560, 125246 (2020). https://doi.org/10.1016/j.physa.2020.125246

    Article  MathSciNet  Google Scholar 

  9. Jiang, R., Wang, R., Wu, Q.S.: Two-lane totally asymmetric exclusion processes with particle creation and annihilation. Phys. A Stat. Mech. its Appl. 375, 247–256 (2007). https://doi.org/10.1016/j.physa.2006.08.025

    Article  Google Scholar 

  10. Peng, G., Lu, W., He, H., Gu, Z.: Nonlinear analysis of a new car-following model accounting for the optimal velocity changes with memory. Commun. Nonlinear Sci. Numer. Simul. 40, 197–205 (2016). https://doi.org/10.1016/j.cnsns.2016.04.024

    Article  MathSciNet  MATH  Google Scholar 

  11. Jiang, R., Wu, Q.S., Zhu, Z.J.: A new continuum model for traffic flow and numerical tests. Transp. Res. Part B Methodol. 36, 405–419 (2002). https://doi.org/10.1016/S0191-2615(01)00010-8

    Article  Google Scholar 

  12. Piccoli, B., Tosin, A.: Vehicular Traffic: A review of continuum mathematical models. In: Encyclopedia of complexity and systems science. pp. 9727–9749. Springer New York (2009)

  13. Khoshyaran, M.M., Lebacque, J.P.: Continuum traffic flow modelling: network approximation, flow approximation. In: Springer proceedings in physics. pp. 505–513. Springer Science and Business Media Deutschland GmbH (2020)

  14. Nagatani, T.: Thermodynamic theory for the jamming transition in traffic flow. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top. 58, 4271–4276 (1998). https://doi.org/10.1103/PhysRevE.58.4271

    Article  Google Scholar 

  15. Gupta, A.K., Katiyar, V.K.: A new anisotropic continuum model for traffic flow. Phys. A Stat. Mech. Appl. 368, 551–559 (2006). https://doi.org/10.1016/j.physa.2005.12.036

    Article  Google Scholar 

  16. Ngoduy, D., Hoogendoorn, S.P., Liu, R.: Continuum modeling of cooperative traffic flow dynamics. Phys. A Stat. Mech. Appl. 388, 2705–2716 (2009). https://doi.org/10.1016/j.physa.2009.02.040

    Article  Google Scholar 

  17. Chen, T., Shi, X., Wong, Y.D.: A lane-changing risk profile analysis method based on time-series clustering. Phys. A Stat. Mech. Appl. 565, 125567 (2021). https://doi.org/10.1016/j.physa.2020.125567

    Article  Google Scholar 

  18. Benjamin, S.C., Johnson, N.F., Hui, P.M.: Cellular automata models of traffic flow along a highway containing a junction. J. Phys. A. Math. Gen. 29, 3119–3127 (1996). https://doi.org/10.1088/0305-4470/29/12/018

    Article  MATH  Google Scholar 

  19. Tian, J., Jia, B., Ma, S., Zhu, C., Jiang, R., Ding, Y.X.: Cellular automaton model with dynamical 2D speed-gap relation. Transp. Sci. 51, 807–822 (2017). https://doi.org/10.1287/trsc.2015.0667

    Article  Google Scholar 

  20. Zhu, W.X., Zhang, H.M.: Analysis of mixed traffic flow with human-driving and autonomous cars based on car-following model. Phys. A Stat. Mech. Appl. 496, 274–285 (2018). https://doi.org/10.1016/j.physa.2017.12.103

    Article  MathSciNet  Google Scholar 

  21. Kala, R., Warwick, K.: Motion planning of autonomous vehicles in a non-autonomous vehicle environment without speed lanes. Eng. Appl. Artif. Intell. 26, 1588–1601 (2013). https://doi.org/10.1016/j.engappai.2013.02.001

    Article  Google Scholar 

  22. Li, Z., Li, W., Xu, S., Qian, Y., Sun, J.: Traffic behavior of mixed traffic flow with two kinds of different self-stabilizing control vehicles. Phys. A Stat. Mech. Appl. 436, 729–738 (2015). https://doi.org/10.1016/j.physa.2015.05.090

    Article  MathSciNet  MATH  Google Scholar 

  23. Peng, G.: A new lattice model of traffic flow with the consideration of individual difference of anticipation driving behavior. Commun. Nonlinear Sci. Numer. Simul. 18, 2801–2806 (2013). https://doi.org/10.1016/j.cnsns.2013.03.007

    Article  MathSciNet  MATH  Google Scholar 

  24. Li, X., Li, Z., Han, X., Dai, S.: Effect of the optimal velocity function on traffic phase transitions in lattice hydrodynamic models. Commun. Nonlinear Sci. Numer. Simul. 14, 2171–2177 (2009). https://doi.org/10.1016/j.cnsns.2008.06.017

    Article  MathSciNet  MATH  Google Scholar 

  25. Li, Z., Zhang, R., Xu, S., Qian, Y.: Study on the effects of driver’s lane-changing aggressiveness on traffic stability from an extended two-lane lattice model. Commun. Nonlinear Sci. Numer. Simul. 24, 52–63 (2015). https://doi.org/10.1016/j.cnsns.2014.12.007

    Article  MathSciNet  MATH  Google Scholar 

  26. Zhang, G., Sun, D.H., Zhao, M.: Phase transition of a new lattice hydrodynamic model with consideration of on-ramp and off-ramp. Commun. Nonlinear Sci. Numer. Simul. 54, 347–355 (2018). https://doi.org/10.1016/j.cnsns.2017.06.011

    Article  MathSciNet  MATH  Google Scholar 

  27. Zhu, C., Zhong, S., Ma, S.: Two-lane lattice hydrodynamic model considering the empirical lane-changing rate. Commun. Nonlinear Sci. Numer. Simul. 73, 229–243 (2019). https://doi.org/10.1016/j.cnsns.2019.02.010

    Article  MathSciNet  MATH  Google Scholar 

  28. Gupta, A.K., Redhu, P.: Analysis of a modified two-lane lattice model by considering the density difference effect. Commun. Nonlinear Sci. Numer. Simul. 19, 1600–1610 (2014). https://doi.org/10.1016/j.cnsns.2013.09.027

    Article  MathSciNet  MATH  Google Scholar 

  29. Lighthill M.J., W.G.B.: On kinematic waves II. A theory of traffic flow on long crowded roads. Proc. R. Soc. London. Ser. A. Math. Phys. Sci. 229, 317–345 (1955). https://doi.org/10.1098/rspa.1955.0089

  30. Lighthill M.J., W.G.B.: On kinematic waves I. Flood movement in long rivers. Proc. R. Soc. London. Ser. A. Math. Phys. Sci. 229, 281–316 (1955). https://doi.org/10.1098/rspa.1955.0088

  31. Richards, P.I.: Shock waves on the highway. Oper. Res. 4, 42–51 (1956). https://doi.org/10.1287/opre.4.1.42

    Article  MathSciNet  MATH  Google Scholar 

  32. Payne, H.J.: Mathematical models of public systems. Simul. Counc. 1, 51–61 (1971). https://doi.org/10.1098/rspa.1955.0089

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  34. Helbing, D., Tilch, B.: Generalized force model of traffic dynamics. Phys. Rev. E. Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top. 58, 133–138 (1998). https://doi.org/10.1103/PhysRevE.58.133

    Article  Google Scholar 

  35. Jiang, R., Wu, Q., Zhu, Z.: Full velocity difference model for a car-following theory. Phys. Rev. E. Stat. Phys. Plasmas. Fluids. Relat. Interdiscip. Top. 64, 4 (2001). https://doi.org/10.1103/PhysRevE.64.017101

    Article  Google Scholar 

  36. Nakayama, A., Sugiyama, Y., Hasebe, K.: Effect of looking at the car that follows in an optimal velocity model of traffic flow. Phys. Rev. E. Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top. 65, 016112 (2002). https://doi.org/10.1103/PhysRevE.65.016112

    Article  Google Scholar 

  37. Anowar Hossain, M., Kabir, K.M.A., Tanimoto, J.: Improved car-following model considering modified backward optimal velocity and velocity difference with backward-looking effect. (2021)

  38. Hossain, M.A., Tanimoto, J.: The “backward looking” effect in the continuum model considering a new backward equilibrium velocity function. Nonlinear Dyn. 87, 149–157 (2021). https://doi.org/10.1007/s11071-016-3032-6

    Article  Google Scholar 

  39. Liu, Z., Wang, J., Ge, H., Cheng, R.: KdV-Burgers equation in the modified continuum model considering the “backward looking” effect. Nonlinear Dyn. 91, 2007–2017 (2018). https://doi.org/10.1007/s11071-017-3999-7

    Article  Google Scholar 

  40. Chen, C., Cheng, R., Ge, H.: An extended car-following model considering driver’s sensory memory and the backward looking effect. Phys. A Stat. Mech. Appl. 525, 278–289 (2019). https://doi.org/10.1016/j.physa.2019.03.099

    Article  Google Scholar 

  41. Wang, Q., Ge, H.: An improved lattice hydrodynamic model accounting for the effect of “backward looking” and flow integral. Phys. A Stat. Mech. Appl. 513, 438–446 (2019). https://doi.org/10.1016/j.physa.2018.09.025

    Article  Google Scholar 

  42. Wang, Z., Ge, H., Cheng, R.: Nonlinear analysis for a modified continuum model considering driver’s memory and backward looking effect. Phys. A Stat. Mech. Appl. 508, 18–27 (2018). https://doi.org/10.1016/j.physa.2018.05.072

    Article  MathSciNet  Google Scholar 

  43. Jiao, Y., Ge, H., Cheng, R.: Nonlinear analysis for a modified continuum model considering electronic throttle (ET) and backward looking effect. Phys. A Stat. Mech. Appl. 535, 122362 (2019). https://doi.org/10.1016/j.physa.2019.122362

    Article  MathSciNet  Google Scholar 

  44. Herrmann, M., Kerner, B.S.: Local cluster effect in different traffic flow models. Phys. A Stat. Mech. Appl. 255, 163–188 (1998). https://doi.org/10.1016/S0378-4371(98)00102-2

    Article  Google Scholar 

Download references

Acknowledgements

This study was partially supported by the Grant-in-Aid for Scientific Research (KAKENHI) from JSPS (Grant No. JP 19KK0262, JP 20H02314, JP 20K21062) awarded to Professor Tanimoto.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Md. Anowar Hossain.

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

Hossain, M.A., Tanimoto, J. The “backward-looking” effect in the continuum model considering a new backward equilibrium velocity function. Nonlinear Dyn 106, 2061–2072 (2021). https://doi.org/10.1007/s11071-021-06894-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-021-06894-2

Keywords

Navigation