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Self-delayed feedback car-following control with the velocity uncertainty of preceding vehicles on gradient roads

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Abstract

Uphill and downhill roads are prevalent in mountainous areas and freeways. Despite the advancements of vehicle-to-vehicle (V2V) communication technology, the driving field of vision could be still largely limited under such a complex road environment, which hinders the sensors from accurately perceiving the speed of front vehicles. As such, a fundamental question for autonomous traffic management is how to control traffic flow associated with the velocity uncertainty of preceding vehicles? This paper aims to answer this question by devising a cooperative control method for autonomous traffic to stabilize the traffic flow under such a complex road environment. To this end, this paper first develops a traffic flow model accounting for the uncertainty of preceding vehicle’s velocity on gradient roads and further devises a new self-delayed feedback controller based on the velocity and headway differences between the current time step and historical time step. The sufficient condition where traffic jams do not occur is derived from the perspective of the frequency domain via Hurwitz criteria and \(H_{\infty }\) norm of transfer functions. The Bode diagram reveals that the robustness of the closed-loop traffic flow model can be significantly enhanced. Simulation results show that the key parameters (control gain coefficient and delay time) of the designed controller contribute to the stability of traffic flow, which is consistent with the theoretical analysis conclusion.

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References

  1. Wu, W., Liu, R., Jin, W., et al.: Stochastic bus schedule coordination considering demand assignment and rerouting of passengers. Transp. Res. B Methodol. 121, 275–303 (2019)

    Google Scholar 

  2. Wu, W., Li, P., Liu, R., et al.: Predicting peak load of bus routes with supply optimization and scaled Shepard interpolation: a newsvendor model. Transp. Res. E Log. Transp. Rev. 142, 102041 (2020)

    Google Scholar 

  3. Wu, W., Lin, Y., Liu, R., et al.: Online EV charge scheduling based on time-of-use pricing and peak load minimization: properties and efficient algorithms. IEEE Trans. Intell. Transp. Syst. (2020). https://doi.org/10.1109/TITS.2020.3014088

    Article  Google Scholar 

  4. Yang, S., Li, M., Tang, T.: Electronic vehicle’s electricity consumption on a road with different slope. Phys. A Stat. Mech. Appl. 402, 41–48 (2014)

    Google Scholar 

  5. Kaur, R., Sharma, S.: Modeling and simulation of driver’s anticipation effect on a two lanes system on curved road with slope. Phys. A Stat. Mech. Appl. 499, 110–120 (2018)

    MathSciNet  Google Scholar 

  6. Zhai, C., Wu, W.: A continuum model with traffic interruption probability and electronic throttle opening angle effect under connected vehicle environment. Eur. Phys. J. B 93, 52 (2020)

    MathSciNet  Google Scholar 

  7. Zhai, C., Wu, W.: A new continuum model with driver’s continuum sensory memory and preceding vehicle’s taillight. Commun. Theor. Phys. 72(10), 105004 (2020)

    MathSciNet  Google Scholar 

  8. Zhai, C., Wu, W.: An extended multi-phase lattice model with consideration of optimal current changes with memory. Clust. Comput. 22, 7447–7457 (2019)

    Google Scholar 

  9. Zhai, C., Wu, W.: Designing continuous delay feedback control for lattice hydrodynamic model under cyber-attacks and connected vehicle environment. Commun. Nonlinear Sci. Numer. Simul. 95, 105667 (2021)

    MathSciNet  MATH  Google Scholar 

  10. Pipes, L.: An operational analysis of traffic dynamics. J. Appl. Phys. 24(3), 274–281 (1953)

    MathSciNet  Google Scholar 

  11. Newell, G.: Nonlinear effects in the dynamics of car following. Oper. Res. 9(2), 209–229 (1961)

    MATH  Google Scholar 

  12. Zeng, J., Qian, Y., Mi, P., et al.: Freeway traffic flow cellular automata model based on mean velocity feedback. Phys. A Stat. Mech. Appl. 562, 125387 (2021)

    MathSciNet  Google Scholar 

  13. Xue, Y., Wang, X., Cen, B., et al.: Study on fuel consumption in the Kerner–Klenov–Wolf three phase cellular automation traffic flow model. Nonlinear Dyn. 102, 393–402 (2020)

    Google Scholar 

  14. Lighthill, M., Whitham, G.: On kinematic waves I Flood movement in long rivers. Proc. R. Soc. Lond. A Math. Phys. Eng. Sci. 229(1178), 281–316 (1955)

    MathSciNet  MATH  Google Scholar 

  15. Richards, P.: Shockwaves on the highway. Oper. Res. 4(1), 42–51 (1956)

    MATH  Google Scholar 

  16. Papageoriou, M.: A hierarchical control system for freeway traffic. Transp. Res. B Methodol. 17(3), 251–261 (1983)

    Google Scholar 

  17. Bando, M., Hasebe, K., Nakayama, A., et al.: Dynamical model of traffic congestion and numerical simulation. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top. 51(2): 1035–1042 (1995)

  18. Helbing, D., Tilch, B.: Generalized force model of traffic dynamics. Phys. Rev. E 58(1), 133–138 (1998)

    Google Scholar 

  19. Jiang, R., Wu, Q., Zhu, Z.: Full velocity difference model for a car following theory. Phys. Rev. E 64, 017101 (2001)

    Google Scholar 

  20. Nagatani, T.: Stabilization and enhancement of traffic flow by the next-nearest-neighbor interaction. Phys. Rev. E 60(6), 6395–6401 (1999)

    Google Scholar 

  21. 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 65, 016112 (2002)

    Google Scholar 

  22. Hasebe, K., Nakayama, A., Sugiyama, Y.: Dynamical model of a cooperative driving system for freeway traffic. Phys. Rev. E 68, 026102 (2003)

    Google Scholar 

  23. Jiao, S., Zhang, S., Zhou, B., et al.: Dynamic performance and safety analysis of car following models considering collision sensitivity. Phys. A Stat. Mech. Appl. 564, 125504 (2021)

    MathSciNet  Google Scholar 

  24. Yu, S., Liu, Q., Li, X.: Full velocity difference and acceleration model for a car following theory. Commun. Nonlinear Sci. Numer. Simul. 18, 1229–1234 (2013)

    MathSciNet  MATH  Google Scholar 

  25. Zhao, X., Gao, Z.: A new car following model: full velocity and acceleration difference model. Eur. Phys. J. B 47, 145–150 (2005)

    Google Scholar 

  26. Xu, X., Pang, J., Monterola, C.: Asymmetric optimal velocity car following model. Phys. A Stat. Mech. Appl. 436, 565–571 (2015)

    MathSciNet  MATH  Google Scholar 

  27. Sun, Y., Ge, H., Cheng, R.: An extended car following model considering driver’s memory and average speed of preceding vehicles with control strategy. Phys. A Stat. Mech. Appl. 521, 752–761 (2019)

    MathSciNet  Google Scholar 

  28. Jin, Z., Yang, Z., Ge, H.: Energy consumption investigation for a new car following model considering driver’s memory and average speed of the vehicles. Phys. Stat. Mech. Appl. 506, 1038–1049 (2018)

    Google Scholar 

  29. Sun, Y., Ge, H., Cheng, R.: A car following model considering the effect of electronic throttle opening angle over the curved road. Phys. A Stat. Mech. Appl. 534, 122377 (2019)

    MathSciNet  Google Scholar 

  30. Li, Y., Li, Z., Peeta, S., et al.: A car following model considering the effect of electronic throttle opening angle under connected environment. Nonlinear Dyn. 85, 2115–2125 (2016)

    Google Scholar 

  31. Yan, C., Ge, H., Cheng, R.: An extended car following model by considering the optimal velocity difference and electronic throttle angle. Phys. A Stat. Mech. Appl. 535, 122216 (2019)

    MathSciNet  Google Scholar 

  32. Zhou, J.: An extended visual angle model for car following theory. Nonlinear Dyn. 81, 549–560 (2015)

    Google Scholar 

  33. Zheng, L., Zhou, S., Jin, P., et al.: Influence of lateral discomfort on the stability of traffic flow based on visual angle car following model. Phys. A Stat. Mech. Appl. 391, 5948–5959 (2012)

    Google Scholar 

  34. Li, Y., Zhang, L., Peeta, S., et al.: Non-lane-discipline- based car following model considering the effects of two-sided lateral gaps. Nonlinear Dyn. 80, 227–238 (2015)

    MATH  Google Scholar 

  35. Jin, S., Wang, D., Tao, P., et al.: Non-lane-based full velocity difference car following model. Phys. A Stat. Mech. Appl. 389, 4654–4662 (2010)

    Google Scholar 

  36. Tang, T., Huang, H., Shang, H.: Influences of the driver’s bounded rationality on micro driving behavior, fuel consumption and emissions. Transp. Res. D Transp. Environ. 41, 423–432 (2015)

    Google Scholar 

  37. Zhai, C., Wu, W.: A new car-following model considering driver’s characteristics and traffic jerk. Nonlinear Dyn. 93, 2185–2199 (2018)

    Google Scholar 

  38. Song, H., Ge, H., Chen, F., et al.: TDGL and mKdV equations for car following model considering traffic jerk and velocity difference. Nonlinear Dyn. 87, 1809–1817 (2017)

    Google Scholar 

  39. Liu, F., Cheng, R., Zheng, P., et al.: TDGL and mKdV equations for car following model considering traffic jerk. Nonlinear Dyn. 83, 793–800 (2016)

    Google Scholar 

  40. Tang, T., Huang, H., Wong, S., et al.: A new car following model with considering the traffic interruption probability. Chin. Phys. B 18(3), 975 (2009)

    Google Scholar 

  41. Zhang, G., Liu, H.: Effect of current vehicle’s interruption on traffic stability in cooperative car following model. Mod. Phys. Lett. B 31(34), 1750317 (2017)

    Google Scholar 

  42. Li, Y., Sun, D., Liu, W., et al.: Modeling and simulation for microscopic traffic flow based on multiple headway, velocity and acceleration difference. Nonlinear Dyn. 66, 15–28 (2011)

    MathSciNet  MATH  Google Scholar 

  43. Kuang, H., Wang, M., Liu, F., et al.: An extended car following model considering multi-anticipative average velocity effect under V2V environment. Phys. A Stat. Mech. Appl. 527, 121268 (2019)

    Google Scholar 

  44. Tang, T., Li, C., Wu, Y., et al.: Impact of the honk effect on the stability of traffic flow. Phys. A Stat. Mech. Appl. 390, 3362–3368 (2011)

    Google Scholar 

  45. Kuang, H., Xu, Z., Li, X., et al.: An extended car following model accounting for the honk effect and numerical tests. Nonlinear Dyn. 87, 149–157 (2017)

    Google Scholar 

  46. Zhai, C., Wu, W.: Car-following model based delay feedback control method with the gyroidal road. Int. J. Mod. Phys. C 30(9), 1950073 (2019)

    MathSciNet  Google Scholar 

  47. Sun, Y., Ge, H., Cheng, R.: An extended car following model considering driver’s desire for smooth driving on the curved road. Phys. A Stat. Mech. Appl. 527, 121426 (2019)

    MathSciNet  Google Scholar 

  48. Cao, B.: A new car following model considering driver’s sensory memory. Phys. A Stat. Mech. Appl. 427, 218–225 (2015)

    MathSciNet  Google Scholar 

  49. Yu, S., Shi, Z.: An improved car following model considering headway changes with memory. Phys. A Stat. Mech. Appl. 421, 1–14 (2015)

    Google Scholar 

  50. Wang, Y., Song, H., Cheng, R.: TDGL and mKdV equations for an extended car following model with the consideration of driver’s memory. Phys. A Stat. Mech. Appl. 515, 440–449 (2019)

    MathSciNet  Google Scholar 

  51. Cheng, J., Liu, R., Ngoduy, D., et al.: A new multi-anticipative car following model with consideration of the desired following distance. Nonlinear Dyn. 85, 2705–2717 (2016)

    Google Scholar 

  52. Wang, T., Li, G., Zhang, J., et al.: The effect of headway variation tendency on traffic flow: modeling and stabilization. Phys. A Stat. Mech. Appl. 525, 566–575 (2019)

    Google Scholar 

  53. Zhou, T., Sun, D., Kang, Y., et al.: A new car following model with consideration of the prevision driving behavior. Commun. Nonlinear Sci. Numer. Simul. 19, 3820–3826 (2014)

    MathSciNet  MATH  Google Scholar 

  54. Yu, L., Shi, Z., Li, T.: A new car following model with two delays. Phys. Lett. A 378, 348–357 (2014)

    MathSciNet  MATH  Google Scholar 

  55. Li, S., Yang, L., Gao, Z., et al.: Stabilization strategies of a general nonlinear car following model with carrying reaction time delay of the drivers. ISA Trans. 53, 1739–1745 (2014)

    Google Scholar 

  56. Zhai, C., Wu, W., Luo, S.: Heterogeneous traffic flow modeling with drivers’ timid and aggressive characteristics. Chin. Phys. B 30(10), 100507 (2021)

    Google Scholar 

  57. Tang, T., He, J., Yang, S., et al.: A car following model accounting for the driver’s attribution. Phys. A Stat. Mech. Appl. 413, 583–591 (2014)

    Google Scholar 

  58. Wang, J., Sun, F., Ge, H.: Effect of the driver’s desire for smooth driving on the car following model. Phys. A Stat. Mech. Appl. 512, 96–108 (2018)

    Google Scholar 

  59. Zhu, W., Zhang, L.: A speed feedback control strategy for car following model. Phys. A Stat. Mech. Appl. 413, 343–351 (2014)

    MathSciNet  MATH  Google Scholar 

  60. Yu, J., Cheng, R., Ge, H.: A control method considering two velocity difference effect in the car following model. Appl. Mech. Mater. 198–199, 962–965 (2012)

    Google Scholar 

  61. Jin, Y., Hu, H.: Stabilization of traffic flow in optimal velocity model via delayed-feedback control. Commun. Nonlinear Sci. Numer. Simul. 18, 1027–1034 (2013)

    MathSciNet  MATH  Google Scholar 

  62. Li, Y., Kang, Y., Yang, B., et al.: A sliding mode controller for vehicular traffic flow. Phys. A Stat. Mech. Appl. 462, 38–47 (2016)

    MathSciNet  MATH  Google Scholar 

  63. Li, Z., Li, W., Xu, S., et al.: Analyses of vehicle’s self-stabilizing effect in an extended optimal velocity model by utilizing historical velocity in an environment of intelligent transportation system. Nonlinear Dyn. 80, 529–540 (2015)

    Google Scholar 

  64. Rong, Y., Wen, H.: An extended delayed feedback control method for the two-lane traffic flow. Nonlinear Dyn. 94, 2479–2490 (2018)

    Google Scholar 

  65. Peng, G., Yang, S., Xia, D., et al.: Delayed-feedback control in a car following model with the combination of V2V communication. Phys. A Stat. Mech. Appl. 526, 120912 (2019)

    MathSciNet  Google Scholar 

  66. Wang, T., Zhang, Y., Zhang, J., et al.: New feedback control strategy for optimal velocity traffic model. Phys. A Stat. Mech. Appl. 559, 125053 (2020)

    MathSciNet  Google Scholar 

  67. Ge, H., Meng, X., Zhu, H., et al.: Feedback control for car following model based on two-lane traffic flow. Phys. A Stat. Mech. Appl. 408, 28–39 (2014)

    MathSciNet  MATH  Google Scholar 

  68. Zhang, G., Sun, D., Zhao, M., et al.: An extended car following model accounting for cooperation driving system with velocity uncertainty. Phys. A Stat. Mech. Appl. 505, 1008–1017 (2018)

    MathSciNet  Google Scholar 

  69. Li, S., Wang, T., Cheng, R., et al.: An extended car following model considering the driver’s desire for smooth driving and self-stabilizing control with velocity uncertainty. Math. Prob. Eng. 2020, 9546012 (2021)

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work is jointly supported by the Regional Joint Fund for foundation and Applied Research Fund of Guangdong Province (Project No. 2019A1515111200), the National Science Foundation of China (Project No. 72071079), and the Science and Technology Program of Guangzhou, China (Project No. 201904010202).

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Correspondence to Weitiao Wu.

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Zhai, C., Wu, W. Self-delayed feedback car-following control with the velocity uncertainty of preceding vehicles on gradient roads. Nonlinear Dyn 106, 3379–3400 (2021). https://doi.org/10.1007/s11071-021-06970-7

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