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A New Car Following Model Considering the Multi-headway Variation Forecast Effect

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Green Transportation and Low Carbon Mobility Safety (GITSS 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 944))

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Abstract

An extended car following model is presented by considering the effect of Multi-Headway Variation Forecast (MHVF) effect in the real world. The model’s linear stability criterion was obtained by employing the linear stability theory. Theoretical analysis result shows that the new consideration leads to the stabilization of traffic systems. By means of nonlinear analysis method, the modified Korteweg-deVries (mKdV) equation near the critical point was derived, thus the propagation behavior of traffic jam can be characterized by the kink-antikink soliton solution for the mKdV equation. Numerical simulation is carried out and its results is in good agreement with the aforementioned theoretical analysis. Both of them show that the MHVF effect can suppress the emergence of traffic jamming and stabilize the vehicular system.

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References

  1. Tang TQ, Wang YP, Yang XB, Wu YH (2012) A new car-following model accounting for varying road condition. Nonlinear Dynam 70:1397–1405

    Article  MathSciNet  Google Scholar 

  2. Kaur R, Sharma S (2017) Analysis of driver’s characteristics on a curved road in a lattice model. Phys A 471:59–67

    Article  Google Scholar 

  3. Peng GH, Kuang H, Qing L (2018) Feedback control method in lattice hydrodynamic model under honk environment. Phys A 509:651–656

    Article  MATH  Google Scholar 

  4. Peng GH, Yang SH, Zhao HZ (2018) New feedback control model in the lattice hydrodynamic model considering the historic optimal velocity difference effect. Commun Theor Phys 70:803–807

    Article  MathSciNet  MATH  Google Scholar 

  5. Sun DH, Kang YR, Yang SH (2015) A novel car following model considering average speed of preceding vehicles group. Phys A 436:103–109

    Article  MathSciNet  MATH  Google Scholar 

  6. Tang TQ, Huang HJ, Shang HY (2010) A new macro model for traffic flow with the consideration of the driver’s forecast effect. Phys Lett A 374:1668–1672

    Article  MATH  Google Scholar 

  7. Jiang R, Wu QS, Zhu ZJ (2002) A new continuum model for traffic flow and numerical tests. Transp Res B 36:405–419

    Article  Google Scholar 

  8. Zhou J, Shi ZK (2016) Lattice hydrodynamic model for traffic flow on curved road. Nonlinear Dyn 83:1217–1236

    Article  MathSciNet  MATH  Google Scholar 

  9. Watanabe MS (2006) Dynamics of group motions controlled by signal processing: a cellular-automaton model and its applications. Commun Nonlinear Sci Numer Simul 11:624–634

    Article  MathSciNet  MATH  Google Scholar 

  10. Bando M, Hasebe K, Shibata A, Sugiyama Y (1995) Dynamical model of traffic congestion and numerical simulation. Phys Rev E 51:1035–1042

    Article  Google Scholar 

  11. Nagatani T (1996) Gas kinetic approach to two-dimensional traffic flow. J Phys Soc Jpn 65:3150–3152

    Article  Google Scholar 

  12. Felipe S, Omer V, Joshua A (2019) Mesoscopic traffic flow model for agent-based simulation. Procedia Comput Sci 151:858–863

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Jiang R, Wu QS, Zhu ZJ (2001) Full velocity difference model for a car-following theory. Phys Rev E 64:017101–017104

    Article  Google Scholar 

  15. Ge HX, Dai SQ, Xue Y, Dong LY (2005) Stabilization analysis and modified Korteweg–de Vries equation in a cooperative driving system. Phys Rev E 71:066119

    Article  MathSciNet  Google Scholar 

  16. Ge HX, Cheng RJ, Li ZP (2008) Two velocity difference model for a car following theory. Phys A 387:5239–5245

    Google Scholar 

  17. Ma GY, Ma MH, Liang SD, Wang SY, Zhang YZ (2020) An improved car-following model accounting for the time-delayed velocity difference and backward looking effect. Commun Nonlinear Sci Numer Simul 85:105221

    Article  MathSciNet  MATH  Google Scholar 

  18. Tang TQ, Li CY, Huang HJ (2010) A new car-following model with the consideration of the driver’s forecast effect. Phys Lett A 374:3951–3956

    Article  MATH  Google Scholar 

  19. Zhang LD, Jia L, Zhu WX (2012) Curved road traffic flow car-following model and stability analysis. Acta Phys Sin 61(7):074501

    Google Scholar 

  20. Tang TQ, Huang HJ, Wong SC, Jiang R (2009) A new car-following model with consideration of the traffic interruption probability. Chin Phys B 18(3):975–983

    Article  Google Scholar 

  21. Zheng LJ, Tian C, Sun DH, Liu WN (2012) A new car-following model with consideration of anticipation driving behavior. Nonlinear Dynam 70:1205–1211

    Article  MathSciNet  Google Scholar 

  22. Wang T, Li GY, Zhang J, Li SB, Sun T (2019) The effect of Headway Variation Tendency on traffic flow: modeling and stabilization. Phys A 525:566–575

    Article  MATH  Google Scholar 

  23. Zhang J, Wang B, Li SB, Sun T, Wang T (2020) Modeling and application analysis of car-following model with predictive headway variation. Phys A 540:123171

    Article  MathSciNet  MATH  Google Scholar 

  24. Wang T, Zang RD, Xu KY, Zhang J (2019) Analysis of predictive effect on lattice hydrodynamic traffic flow model. Phys A 526:120711

    Article  MATH  Google Scholar 

  25. Kaur D, Sharma S (2020) A new two-lane lattice model by considering predictive effect in traffic flow. Phys A 539:122913

    Article  MathSciNet  MATH  Google Scholar 

  26. Ge HX, Cheng RJ, Dai SQ (2005) KdV and kink-antikink solitons in car-following models. Phys A 357:466–476

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work is supported by Science and Technology Plan Projects of Guizhou Province: (No.[2018]1059) and Natural Science Foundation of Guangxi (No.2018GXNSFAA050020).

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Correspondence to Yi-rong Kang .

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Kang, Yr., Yang, Sh. (2023). A New Car Following Model Considering the Multi-headway Variation Forecast Effect. In: Wang, W., Wu, J., Jiang, X., Li, R., Zhang, H. (eds) Green Transportation and Low Carbon Mobility Safety. GITSS 2021. Lecture Notes in Electrical Engineering, vol 944. Springer, Singapore. https://doi.org/10.1007/978-981-19-5615-7_39

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  • DOI: https://doi.org/10.1007/978-981-19-5615-7_39

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5614-0

  • Online ISBN: 978-981-19-5615-7

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