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Analysis of connected vehicle networks using network-based perturbation techniques

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Abstract

In this paper we propose a novel technique to decompose networked systems and use this technique to investigate the dynamics of connected vehicle networks with wireless vehicle-to-vehicle (V2V) communication. We apply modal perturbation analysis to approximate the modes of the perturbed network about the modes of the corresponding cyclically symmetric network. By exploiting the cyclic symmetry, we approximate the dynamics of a given mode by solving a small number of linear algebraic equations. We apply this approach to decompose connected vehicle networks into traveling waves which allows us to assess the impacts of long-range V2V communication on the stability of traffic flow.

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References

  1. Alam, A., Mårtensson, J., Johansson, K.H.: Experimental evaluation of decentralized cooperative cruise control for heavy-duty vehicle platooning. Control Eng. Pract. 38, 11–25 (2015)

    Article  Google Scholar 

  2. Alam, A., Besselink, B., Turri, V., Martensson, J., Johansson, K.H.: Heavy-duty vehicle platooning for sustainable freight transportation: a cooperative method to enhance safety and efficiency. IEEE Control Syst. 35(6), 34–56 (2015)

    Article  MathSciNet  Google Scholar 

  3. Avedisov, S.S., Orosz, G.: Nonlinear network modes in cyclic systems with applications to connected vehicles. J. Nonlinear Sci. 25(4), 1015–1049 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  4. di Bernardo, M., Salvi, A., Santini, S.: Distributed consensus strategy for platooning of vehicles in the presence of time-varying heterogeneous communication delays. IEEE Trans. Intell. Transp. Syst. 16(1), 102–112 (2015)

    Article  Google Scholar 

  5. Gallagher, B., Akatsuka, H., Suzuki, H.: Wireless communications for vehicle safety: radio link performance and wireless connectivity methods. IEEE Veh. Technol. Mag. 1(4), 4–24 (2006)

    Article  Google Scholar 

  6. Ge, J.I., Avedisov, S.S., Orosz, G.: Stability of connected vehicle platoons with delayed acceleration feedback. In: Proceedings of the ASME Dynamical Systems and Control Conference. ASME (2013). Paper no. DSCC2013-4040

  7. Ge, J.I., Orosz, G.: Dynamics of connected vehicle systems with delayed acceleration feedback. Transp. Res. Part C Emerg. Technol. 46, 46–64 (2014)

    Article  Google Scholar 

  8. Ge, J.I., Orosz, G., Hajdu, D., Insperger, T., Moehlis, J.: To delay or not to delay—stability of connected cruise control time delay systems. Theory, numerics, applications, and experiments. In: Insperger, T., Ersal, T., Orosz, G. (eds.) Advances in Delays and Dynamics, vol. 7, pp. 263–282. Springer, Berlin (2017)

  9. Ge, J.I., Orosz, G.: Optimal control of connected vehicle systems with communication delay and driver reaction time. IEEE Trans. Intell. Transp. Syst. (2016). doi:10.1109/TITS.2016.2633164

    Google Scholar 

  10. Geiger, A., Lauer, M., Moosmann, F., Ranft, B., Rapp, H., Stiller, C., Ziegler, J.: Team Annieway’s entry to the 2011 grand cooperative driving challenge. IEEE Trans. Intell. Transp. Syst. 13(3), 1008–1017 (2012)

    Article  Google Scholar 

  11. Happawana, G.S., Nwokah, O.D.I., Bajaj, A.K., Azene, M.: Free and forced response of mistuned linear cyclic systems: a singular perturbation approach. J. Sound Vib. 211(5), 761–789 (1998)

    Article  Google Scholar 

  12. Liu, Z.H., Liu, W., Gao, W.C., Cheng, X.: Advances of research on mode localization in mistuned cyclically periodic structures. Appl. Mech. Mater. 405–408, 3198–3203 (2013)

    Article  Google Scholar 

  13. Lu, X.Y., Hedrick, J.K., Drew, M.: ACC/CACC-control design, stability and robust performance. In: Proceedings of the American Control Conference, vol. 6, pp. 4327–4332. IEEE (2002)

  14. Liao, Y., Li, S.E., Wang, W., Wang, Y., Li, G., Cheng, B.: Detection of driver cognitive distraction: a comparison study of stop-controlled intersection and speed-limited highway. IEEE Trans. Intell. Transp. Syst. 17(6), 1628–1637 (2016)

    Article  Google Scholar 

  15. Mangel, T., Michl, M., Klemp, O., Hartenstein, H.: Real-world measurements of non-line-of-sight reception quality for 5.9 GHz IEEE 802.11p at intersections. In: Strang, T., Festag, A., Vinel, A., Mehmood, R., Rico Garcia, C., Röckl, M. (eds.) Communication Technologies for Vehicles, pp. 189–202. Springer, Berlin (2011)

    Chapter  Google Scholar 

  16. Milanés, V., Shladover, S.E.: Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data. Transp. Res. Part C Emerg. Technol. 48, 285–300 (2014)

    Article  Google Scholar 

  17. Milanés, V., Shladover, S.E., Spring, J., Nowakowski, C., Kawazoe, H., Nakamura, M.: Cooperative adaptive cruise control in real traffic situations. IEEE Trans. Intell. Transp. Syst. 15(1), 296–305 (2014)

    Article  Google Scholar 

  18. Olson, B.J., Shaw, S.W., Shi, C., Pierre, C., Parker, R.G.: Circulant matrices and their application to vibration analysis. Appl. Mech. Rev. 66(4), 040803 (2014)

  19. Öncü, S., Ploeg, J., van de Wouw, N., Nijmeijer, H.: Cooperative adaptive cruise control: network-aware analysis of string stability. IEEE Trans. Intell. Transp. Syst. 15(4), 1527–1537 (2014)

    Article  Google Scholar 

  20. Orosz, G., Stépán, G.: Subcritical Hopf bifurcations in a car-following model with reaction-time delay. Proc. R. Soc. 462(2073), 2643–2670 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  21. Orosz, G.: Connected cruise control: modelling, delay effects, and nonlinear behaviour. Veh. Syst. Dyn. 54(8), 1147–1176 (2016)

    Article  Google Scholar 

  22. Pierre, C., Dowell, E.H.: Localization of vibrations by structural irregularity. J. Sound Vib. 114(3), 549–564 (1987)

    Article  Google Scholar 

  23. Ploeg, J., van de Wouw, N., Nijmeijer, H.: Lp string stability of cascaded systems: application to vehicle platooning. IEEE Trans. Control Syst. Technol. 22(2), 1527–1537 (2014)

    Article  Google Scholar 

  24. Qin, W.B., Gomez, M.M., Orosz, G.: Stability and frequency response under stochastic communication delays with applications to connected cruise design. IEEE Trans. Intell. Transp. Syst. 18(2), 388–403 (2017)

  25. Rajamani, R.: Vehicle Dynamics and Control. Springer, Berlin (2011)

    MATH  Google Scholar 

  26. Roose, D., Szalai, R.: Continuation and bifurcation analysis of delay differential equations. In: Krauskopf, B., Osinga, H.M., Galan-Vioque, J. (eds.) Numerical Continuation Methods for Dynamical Systems, Understanding Complex Systems, pp. 359–399. Springer, Berlin (2007)

    Chapter  Google Scholar 

  27. Shladover, S.E., Nowakowski, C., Lu, X.Y., Ferlis, R.: Cooperative adaptive cruise control (CACC) definitions and operating concepts. In: Proceedings of the 94th Annual TRB Meeting, 15-3265 (2015)

  28. Shladover, S.E., Su, D., Lu, X.Y.: Impacts of cooperative adaptive cruise control on freeway traffic flow. Transp. Res. Rec. J. Transp. Res. Board 2324, 63–70 (2012)

    Article  Google Scholar 

  29. Szalai, R., Orosz, G.: Decomposing the dynamics of heterogeneous delayed networks with applications to connected vehicle systems. Phys. Rev. E 88(4), 040902 (2013)

    Article  Google Scholar 

  30. Wei, S.T., Pierre, C.: Localization phenomena in mistuned assemblies with cyclic symmetry part i: free vibrations. J. Vib. Acoust. Stress Reliab. Des. 110(4), 429–438 (1988)

    Article  Google Scholar 

  31. Wang, M., Daamen, W., Hoogendoorn, S.P., van Arem, B.: Cooperative car-following control: distributed algorithm and impact on moving jam features. IEEE Trans. Intell. Transp. Syst. 17(5), 1459–1471 (2016)

    Article  Google Scholar 

  32. Zhang, L., Orosz, G.: Motif-based design for connected vehicle systems in presence of heterogeneous connectivity structures and time delays. IEEE Trans. Intell. Transp. Syst. 17(6), 1638–1651 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

Funding was provided by the National Science Foundation (Award No. 1351456).

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Correspondence to Sergei S. Avedisov.

Appendices

Appendix 1: Third-order approximation of modal dynamics

To obtain the third-order perturbation of the dynamics of the k-th mode \([\hat{\mathbf {D}}^{(1,2,3)}]^{k}_{k}\) for an arbitrary \(i_{1},\sigma _{1},i_{2},\sigma _{2},i_{3},\sigma _{3}\) sextuple we take the derivative of (49) with respect to \(\varepsilon _{i_{3}\sigma _{3}}\) (denoted by \(\varepsilon _{3}\)) which yields

$$\begin{aligned}&\mathrm {\partial }_{\varepsilon _{1}}\mathrm {\partial }_{\varepsilon _{2}} \mathrm {\partial }_{\varepsilon _{3}}\Big (\hat{\mathbf {J}}-\mathbf {I}_{N} \otimes [\hat{\mathbf {D}}]^{k}_{k}\Big )[\hat{\mathbf {T}}]_{k}\nonumber \\&\quad +\,\mathrm {\partial }_{\varepsilon _{1}}\mathrm {\partial }_{\varepsilon _{2}} \Big (\hat{\mathbf {J}}-\mathbf {I}_{N}\otimes [\hat{\mathbf {D}}]^{k}_{k}\Big ) \mathrm {\partial }_{\varepsilon _{3}}[\hat{\mathbf {T}}]_{k}\nonumber \\&\quad +\,\mathrm {\partial }_{\varepsilon _{1}}\mathrm {\partial }_{\varepsilon _{3}}\Big (\hat{ \mathbf {J}}-\mathbf {I}_{N}\otimes [\hat{\mathbf {D}}]^{k}_{k}\Big ) \mathrm {\partial }_{\varepsilon _{2}}[\hat{\mathbf {T}}]_{k}\nonumber \\&\quad +\,\mathrm {\partial }_{\varepsilon _{2}}\mathrm {\partial }_{\varepsilon _{3}} \Big (\hat{\mathbf {J}}-\mathbf {I}_{N}\otimes [\hat{\mathbf {D}}]^{k}_{k}\Big ) \mathrm {\partial }_{\varepsilon _{1}}[\hat{\mathbf {T}}]_{k}\nonumber \\&\quad +\,\mathrm {\partial }_{\varepsilon _{1}}\Big (\hat{\mathbf {J}}-\mathbf {I}_{N} \otimes [\hat{\mathbf {D}}]^{k}_{k}\Big )\mathrm {\partial }_{\varepsilon _{2}} \mathrm {\partial }_{\varepsilon _{3}}[\hat{\mathbf {T}}]_{k}\nonumber \\&\quad +\,\mathrm {\partial }_{\varepsilon _{2}}\Big (\hat{\mathbf {J}}-\mathbf {I}_{N} \otimes [\hat{\mathbf {D}}]^{k}_{k}\Big )\mathrm {\partial }_{\varepsilon _{1}} \mathrm {\partial }_{\varepsilon _{3}}[\hat{\mathbf {T}}]_{k}\nonumber \\&\quad +\,\mathrm {\partial }_{\varepsilon _{3}}\Big (\hat{\mathbf {J}}-\mathbf {I}_{N} \otimes [\hat{\mathbf {D}}]^{k}_{k}\Big )\mathrm {\partial }_{\varepsilon _{1}} \mathrm {\partial }_{\varepsilon _{2}}[\hat{\mathbf {T}}]_{k}\nonumber \\&\quad +\,\Big (\hat{\mathbf {J}}- \mathbf {I}_{N}\otimes [\hat{\mathbf {D}}]^{k}_{k}\Big )\mathrm {\partial }_{ \varepsilon _{1}}\mathrm {\partial }_{\varepsilon _{2}}\mathrm {\partial }_{\varepsilon _{3}}[ \hat{\mathbf {T}}]_{k}=0. \end{aligned}$$
(89)

At \(\varepsilon _{i_{1}\sigma _{1}}=\varepsilon _{i_{2}\sigma _{2}}=\varepsilon _{i_{3}\sigma _{3}}=0\) we obtain

$$\begin{aligned}&\frac{1}{6}\mathbf {I}_{N}\otimes \Big ([\hat{\mathbf {D}}^{(1,2,3)}]^{k}_{k}+ [\hat{\mathbf {D}}^{(1,3,2)}]^{k}_{k}+[\hat{\mathbf {D}}^{(2,1,3)}]^{k}_{k}\nonumber \\&\quad +[\hat{\mathbf {D}}^{(2,3,1)}]^{k}_{k}+[\hat{\mathbf {D}}^{(3,1,2)}]^{k}_{k}+ [\hat{\mathbf {D}}^{(3,2,1)}]^{k}_{k}\Big )\nonumber \\&\quad =-\frac{1}{2}\mathbf {I}_{N}\otimes \Big ([\hat{\mathbf {D}}^{(1,2)}]^{k}_{k}+ [\hat{\mathbf {D}}^{(2,1)}]^{k}_{k}\Big )[\hat{\mathbf {T}}^{(3)}]_{k}\nonumber \\&\qquad -\frac{1}{2} \mathbf {I}_{N}\otimes \Big ([\hat{\mathbf {D}}^{(1,3)}]^{k}_{k}+[\hat{ \mathbf {D}}^{(3,1)}]^{k}_{k}\Big )[\hat{\mathbf {T}}^{(2)}]_{k}\nonumber \\&\qquad -\frac{1}{2}\mathbf {I}_{N}\otimes \Big ([\hat{\mathbf {D}}^{(2,3)}]^{k}_{k}+ [\hat{\mathbf {D}}^{(3,2)}]^{k}_{k}\Big )[\hat{\mathbf {T}}^{(1)}]_{k}\nonumber \\&\qquad +\frac{1}{2}(\hat{\mathbf {P}}^{(1)}-\mathbf {I}_{N}\otimes [\hat{\mathbf {D}}^{(1)}] ^{k}_{k})([\hat{\mathbf {T}}^{(2,3)}]_{k}+[\hat{\mathbf {T}}^{(3,2)}]_{k})\nonumber \\&\qquad +\frac{1}{2}(\hat{\mathbf {P}}^{(2)}-\mathbf {I}_{N}\otimes [\hat{\mathbf {D}}^{(2)} ]^{k}_{k})([\hat{\mathbf {T}}^{(1,3)}]_{k}+[\hat{\mathbf {T}}^{(3,1)}]_{k})\nonumber \\&\qquad + \frac{1}{2}(\hat{\mathbf {P}}^{(3)}-\mathbf {I}_{N}\otimes [ \hat{\mathbf {D}}^{(3)}]^{k}_{k})([\hat{\mathbf {T}}^{(1,2)}]_{k}+[\hat{ \mathbf {T}}^{(2,1)}]_{k})\nonumber \\&\qquad +\frac{1}{6}(\hat{\mathbf {J}}_{0}-\mathbf {I}_{N}\otimes [ \hat{\mathbf {D}}_{0}]^{k}_{k})\Big ([\hat{\mathbf {T}}^{(1,2,3)}]_{k}+[ \hat{\mathbf {T}}^{(1,3,2)}]_{k}\nonumber \\&\qquad +[\hat{\mathbf {T}}^{(2,1,3)}]_{k}+[ \hat{\mathbf {T}}^{(2,3,1)}]_{k}+[\hat{\mathbf {T}}^{(3,1,2)}]_{k}\nonumber \\&\qquad +[ \hat{\mathbf {T}}^{(3,2,1)}]_{k}\Big ). \end{aligned}$$
(90)

We can eliminate the last term in the expression above by multiplying by \([\hat{\mathbf {T}}_{0}^{-1}]^{k}\) from the left and using (26). Also because the above expression has six unknowns \(\Big (\) the \([\hat{\mathbf {D}}^{(\cdot ,\cdot ,\cdot )}]^{k}_{k}\)’s \(\Big )\) for each \(i_{1},\,\sigma _{1},\,i_{2},\,\sigma _{2},\,i_{3},\,\sigma _{3}\) sextuple we have the freedom to set

$$\begin{aligned} \begin{aligned} \hat{[\mathbf {D}}^{(1,2,3)}]^{k}_{k}=\,&[\hat{\mathbf {D}}_{3}(i_{1},\sigma _{1},i_{2},\sigma _{2},i_{3},\sigma _{3})]^{k}_{k}\\ =\,&-3[\hat{\mathbf {T}}^{-1}_{0}]^{k}(\mathbf {I}_{N}\otimes [\hat{\mathbf {D}}^{(1,2)} ]^{k}_{k})\hat{\mathbf {T}}_{0}[\hat{\mathbf {U}}^{(3)}]_{k}\\&+3[\hat{ \mathbf {T}}^{-1}_{0}]^{k}(\hat{\mathbf {P}}^{(1)}-\mathbf {I}_{N}\otimes [ \hat{\mathbf {D}}^{(1)}]^{k}_{k})\hat{\mathbf {T}}_{0}[\hat{\mathbf {U}}^{( 2,3)}]_{k}, \end{aligned} \end{aligned}$$
(91)

while the equations for the other third-order terms can be obtained by permuting on the indices corresponding to \(i_{1},\,\sigma _{1},\,i_{2},\,\sigma _{2},\,i_{3},\,\sigma _{3}\) on the left and right hand side of (91). By algebraic manipulation one can show \([\hat{\mathbf {T}}^{-1}_{0}]^{k}(\mathbf {I}_{N}\otimes [\hat{\mathbf {D}}^{(1,2)}]^{k}_{k})\hat{\mathbf {T}}_{0}[\hat{\mathbf {U}}^{(3)}]_{k}=[\hat{\mathbf {T}}^{-1}_{0}]^{k}(\mathbf {I}_{N}\otimes [\hat{\mathbf {D}}^{(1)}]^{k}_{k})\hat{\mathbf {T}}_{0}[\hat{\mathbf {U}}^{(2,3)}]_{k}=0\). This means we can simplify (91) to

$$\begin{aligned}{}[\hat{\mathbf {D}}^{(1,2,3)}]^{k}_{k}=3[\hat{\mathbf {T}}^{-1}_{0}]^{k}\hat{\mathbf {P}}^{(1)}\hat{\mathbf {T}}_{0}[\hat{\mathbf {U}}^{(2,3)}]_{k}, \end{aligned}$$
(92)

and by using (17) we can obtain (62).

Appendix 2: Second-order approximation of the modal block eigenvector

Solving (59) for the connected vehicle network , we obtain the \((k,\ell )\)-th block of \(\hat{\mathbf {U}}^{(1,2)}\) whose elements are contained in

$$\begin{aligned} \mathbf {b}_{k\ell }^{(1,2)}=\begin{bmatrix} u^{(1,2)}_{k\ell ,11} \\ u^{(1,2)}_{k\ell ,21} \\ u^{(1,2)}_{k\ell ,12} \\ u^{(1,2)}_{k\ell ,22} \end{bmatrix}. \end{aligned}$$
(93)

For \(k\ne \ell \) using (80,81) we obtain

$$\begin{aligned} \begin{aligned}&\mathbf {b}_{k\ell }^{(1,2)}=\frac{1}{p(p-\alpha \beta _{1})\eta _{k\,\ell }}\\&\times \begin{bmatrix} (p+\beta _{1}(\beta _{1}\eta _{\ell 1}-\alpha ))R(i_{1},\sigma _{1},i_{2},\sigma _{2})_{k\ell }+p\eta _{\ell 1}(p+\beta _{1}(\beta _{1}\eta _{k1}-\alpha ))Q(i_{1},\sigma _{1},i_{2},\sigma _{2})_{k\ell }-p\beta _{1}\eta _{\ell 1}S(i_{1},\sigma _{1},i_{2},\sigma _{2})_{k\ell }\\ p\eta _{\ell 1}(-\beta _{1}R(i_{1},\sigma _{1},i_{2},\sigma _{2})_{k\ell }-p\beta _{1}\eta _{k1}Q(i_{1},\sigma _{1},i_{2},\sigma _{2})_{k\ell }+pS(i_{1},\sigma _{1},i_{2},\sigma _{2})_{k\ell })\\ (-p\beta _{1}\eta _{k1}Q(i_{1},\sigma _{1},i_{2},\sigma _{2})_{k\ell }-\beta _{1}R(i_{1},\sigma _{1},i_{2},\sigma _{2})_{k\ell }+pS(i_{1},\sigma _{1},i_{2},\sigma _{2})_{k\ell })\\ p(R(i_{1},\sigma _{1},i_{2},\sigma _{2})_{k\ell }+p\eta _{k1}Q(i_{1},\sigma _{1}, i_{2},\sigma _{2})_{k\ell }-\alpha ~S(i_{1},\sigma _{1}, i_{2},\sigma _{2})_{k\;\ell }) \end{bmatrix}, \end{aligned} \end{aligned}$$
(94)

where

$$\begin{aligned} \begin{aligned} Q(i_{1},\sigma _{1},i_{2},\sigma _{2})_{k\;\ell }&=\frac{2}{N}u^{(1)}_{k\ell ,12}\Big (\mathrm {e}^{i\frac{2\pi }{N}\sigma _{2}(\ell -1)}-1\Big ),\\ R(i_{1},\sigma _{1},i_{2},\sigma _{2})_{k\;\ell }&=\frac{2}{N}\bigg (-\sum ^{N}_{j=1} \mathrm {e}^{i\frac{2\pi }{N}(i_{1}-1)(j-k)}\\&\qquad \times \Big (\mathrm {e}^{i\frac{2\pi }{N}\sigma _{1}(j-1)}-1\Big )u^{(2)}_{j\ell ,21}\bigg ),\\ S(i_{1},\sigma _{1},i_{2},\sigma _{2})_{k\;\ell }&=\frac{2}{N}\bigg (u^{(1)}_{k \ell ,22}\Big (\mathrm {e}^{i\frac{2\pi }{N}\sigma _{2}(\ell -1)}-1\Big )\\&\quad -\sum ^{N}_{j=1}\mathrm {e}^{i\frac{2\pi }{N}(i_{1}-1)(j-k)}\\&\qquad \times \Big (\mathrm {e}^{i\frac{2\pi }{N}\sigma _{1}(j-1)}-1\Big )u^{(2)}_{j\ell ,22}\bigg ). \end{aligned} \end{aligned}$$
(95)

and \(u^{(1)}_{k\ell ,12},\,u^{(2)}_{j\ell ,21},\,u^{(1)}_{k\ell ,22},\,u^{(2)}_{j\ell ,22}\) are given in (77). For the case \(k=\ell \) (59) has multiple possible solutions due to a nonzero nullity. In this case, we set

$$\begin{aligned} \mathbf {b}^{(1,2)}_{k\ell }=\begin{bmatrix} 0 \\ 0 \\ 0 \\ 0 \end{bmatrix}. \end{aligned}$$
(96)

Appendix 3: Cubic terms of the modal approximation

The coefficients in (82) and (83) are given by

$$\begin{aligned}&L_{0}(i_{1},\sigma _{1},i_{2},\sigma _{2},i_{3},\sigma _{3}) = \frac{1}{N^3} \frac{p\eta _{k1}}{(p-\alpha \beta _{1})^{2}}\nonumber \\&\quad \sum ^{N}_{u=1,u\ne k}\frac{(\mathrm {e}^{i\frac{2\pi }{N} \sigma _{1}(u-1)}-1)\mathrm {e}^{i\frac{2\pi }{N}(i_{1}-1)(u-k)}}{\eta _{uk}}\nonumber \\&\quad \times \bigg (-\frac{(\mathrm {e}^{i\frac{2\pi }{N}\sigma _{2}(k-1)}-1)(\mathrm {e}^{ i\frac{2\pi }{N}\sigma _{3}(k-1)}-1)\mathrm {e}^{i\frac{2\pi }{N} (i_{2}-1)(k-u)}}{\eta _{uk}}\nonumber \\&\qquad (\beta _{1}\eta _{u1}+\alpha )+\sum ^{N}_{j=1,j\ne k}\nonumber \\&\quad \frac{(\mathrm {e}^{i\frac{2\pi }{N}\sigma _{2}(j-1)}-1) (\mathrm {e}^{i\frac{2\pi }{N}\sigma _{3}(k-1)}-1)\mathrm {e}^{i \frac{2\pi }{N}(i_{2}-1)(j-u)}\mathrm {e}^{i\frac{2\pi }{N}(i_{3}-1)(k-j)}}{ \eta _{jk}}\nonumber \\&\quad (\beta _{1}\eta _{k1}+\alpha )\bigg ),\nonumber \\&\quad \mathrm {and}\nonumber \\&\quad L_{1}(i_{1},\sigma _{1},i_{2},\sigma _{2},i_{3},\sigma _{3}) = \frac{1}{N^3} \frac{1}{(p-\alpha \beta _{1})^{2}}\nonumber \\&\quad \sum ^{N}_{u=1,u\ne k}\frac{(\mathrm {e}^{ i\frac{2\pi }{N}\sigma _{1}(u-1)}-1)\mathrm {e}^{i\frac{2\pi }{N}(i_{1}-1) (u-k)}}{\eta _{uk}}\nonumber \\&\quad \times \bigg (\frac{(\mathrm {e}^{i\frac{2\pi }{N}\sigma _{2}(k-1)}-1) (\mathrm {e}^{i\frac{2\pi }{N}\sigma _{3}(k-1)}-1)\mathrm {e}^{i \frac{2\pi }{N}(i_{2}-1)(k-u)}}{\eta _{uk}}\nonumber \\&\qquad (p\eta _{u1}+\alpha ^2)-\sum ^{N}_{j=1,j\ne k}\nonumber \\&\quad \frac{(\mathrm {e}^{i\frac{2\pi }{N}\sigma _{2}(j-1)}-1)(\mathrm {e}^{ i\frac{2\pi }{N}\sigma _{3}(k-1)}-1)\mathrm {e}^{i\frac{2\pi }{N} (i_{2}-1)(j-u)}\mathrm {e}^{i\frac{2\pi }{N}(i_{3}-1)(k-j)}}{\eta _{jk}}\nonumber \\&\qquad (p\eta _{k1}+\alpha ^2)\bigg ). \end{aligned}$$
(97)

Appendix 4: Coefficients for modal stability boundaries and modal frequencies

The coefficients for \(p_{k}\) and \(\omega _{k}\) in (85) and (86) are obtained by plugging in (85) and (86) into (84) with \(p=p_{k}\) and \(\lambda =i\omega _{k}\). The zeroth-order terms in (85) and (86) are then determined by setting all \(\beta _{i\sigma }=0\), taking the real and imaginary parts of (84), and solving the resulting two equations for \(p_{k0}\) and \(\omega _{k0}\) we obtain

$$\begin{aligned} \begin{aligned}&p_{k0}\,=\,{\textstyle \frac{1}{2}}(2\beta _{1}+\alpha )\Big ((2\beta _{1}+\alpha )\tan ^2\Big (\textstyle {\frac{\theta _{k}}{2}}\Big )+\alpha \Big ),\,\\&\omega _{k0}=(2\beta _{1}+\alpha )\tan \Big (\textstyle {\frac{\theta _{k}}{2}}\Big ), \end{aligned} \end{aligned}$$
(98)

where \(\theta _{k}=\frac{2\pi }{N}(k-1)\). These expressions indeed correspond to (72) and (73).

To obtain the first-order terms for indices \(i_{1},\sigma _{1}\), we take the partial derivative of (84) with respect to \(\beta _{i_{1}\sigma _{1}}\) and evaluate the expression at \(\beta _{i_{1}\sigma _{1}}=0\). Then taking the real and imaginary parts, and performing some algebraic manipulation we get

$$\begin{aligned} \begin{aligned} p_{k1}(i_{1},\sigma _{1})&=\frac{1}{2N}\Bigg (\omega _{k0}\bigg (\sin (\sigma _{1} \theta _{k})+\frac{2\big (1+\sin \big (\frac{\theta _{k}}{2}\big )\big )}{\sin (\theta _{k})}\\&\quad \times \big (1-\cos (\sigma _{1}\theta _{k})\big )\bigg )\\&\quad +\alpha \bigg (\frac{\sin (\sigma _{1}\theta _{k})}{\tan \big (\frac{\theta _{k}}{2}\big )}+ \big (1-\cos (\sigma _{1}\theta _{k})\big )\bigg )\Bigg )\\ \omega _{k1}(i_{1},\sigma _{1})&=\frac{1}{N}\bigg (\sin (\sigma _{1}\theta _{k}) +\tan ( \textstyle {\frac{\theta _{k}}{2}})\big (1-\cos (\sigma _{1}\theta _{k})\big )\bigg ). \end{aligned} \end{aligned}$$
(99)

Similarly, the second-order terms for the indices \(i_{1},\sigma _{1},i_{2},\sigma _{2}\) can be obtained by taking the second partial derivative of (84) with respect to \(\beta _{i_{1}\sigma _{1}}\) and \(\beta _{i_{2}\sigma _{2}}\) and evaluating the results at \(\beta _{i_{1}\sigma _{1}}=\beta _{i_{2}\sigma _{2}}=0\). Splitting the real and imaginary parts we obtain

$$\begin{aligned}&p_{k2}(i_{1},\sigma _{1},i_{2},\sigma _{2})=\frac{2}{N}\omega _{k1}(i_{1}, \sigma _{1})_{k}\frac{1-\cos (\sigma _{2}\theta _{k})}{\sin (\theta _{k})}\nonumber \\&\quad +\,\bigg (2\omega _{k0}\frac{1+\sin ^{2}\big (\frac{\theta _{k}}{2}\big )}{\sin (\theta _{k})}+ \alpha \bigg )\bigg (\mathrm {Re}\,K_{1}(i_{1},\sigma _{1},i_{2},\sigma _{2})|_{\mathrm {c}}\nonumber \\&\quad +\, \frac{1}{\omega _{k0}}\mathrm {Im}\,K_{0}(i_{1},\sigma _{1},i_{2},\sigma _{2})|_{ \mathrm {c}}\bigg )\nonumber \\&\quad +\,\bigg (\omega _{k0}+\alpha \frac{2\cos ^{2}\big (\frac{\theta _{k}}{2}\big )}{\sin (\theta _{k})}\bigg ) \bigg (-\mathrm {Im}K_{1}\,(i_{1},\sigma _{1},i_{2},\sigma _{2})|_{\mathrm {c}}\nonumber \\&\quad +\, \frac{1}{\omega _{k0}}\mathrm {Re}\,K_{0}(i_{1},\sigma _{1},i_{2},\sigma _{2})|_{ \mathrm {c}}\bigg )\nonumber \\&\omega _{k2}(i_{1},\sigma _{1},i_{2},\sigma _{2})=2\bigg (\mathrm {Re}\,K_{1}(i_{1}, \sigma _{1},i_{2},\sigma _{2})|_{\mathrm {c}}\tan \left( \textstyle {\frac{\theta _{k}}{2}}\right) \nonumber \\&\quad -\, \mathrm {Im}\,K_{1}(i_{1},\sigma _{1},i_{2},\sigma _{2})|_{\mathrm {c}}\bigg )\nonumber \\&\quad +\,\frac{2}{\omega _{k0}}\bigg (\mathrm {Re}\,K_{0}(i_{1},\sigma _{1},i_{2}, \sigma _{2})|_{\mathrm {c}}\nonumber \\&\quad +\,\mathrm {Im}\,K_{0}(i_{1},\sigma _{1},i_{2}, \sigma _{2})|_{\mathrm {c}}\tan \left( \textstyle {\frac{\theta _{k}}{2}}\right) \bigg ), \end{aligned}$$
(100)

where “\(|_{\mathrm {c}}\)” indicates that the quantity is evaluated with all \(\beta _{i\sigma }=0\).

Finally, to obtain the third-order terms for the indices \(i_{1},\sigma _{1},i_{2},\sigma _{2},i_{3},\sigma _{3}\) we take the third partial derivative of (84) with respect to \(\beta _{i_{1}\sigma _{1}}\), \(\beta _{i_{2}\sigma _{2}}\), and \(\beta _{i_{3}\sigma _{3}}\) and evaluate the result at \(\beta _{i_{1}\sigma _{1}}=\beta _{i_{2}\sigma _{2}}=\beta _{i_{3}\sigma _{3}}=0\). Taking the real and imaginary parts yields

$$\begin{aligned}&p_{k3}(i_{1},\sigma _{1},i_{2},\sigma _{2},i_{3},\sigma _{3})=3\omega _{k1}(i_{1}, \sigma _{1})\omega _{k2}(i_{1},\sigma _{1},i_{2},\sigma _{2})\nonumber \\&\quad +\,\frac{3}{N}\frac{1-\cos ( \sigma _{1}\theta _{})}{\sin (\theta _{k})}\omega _{k2}(i_{2},\sigma _{2},i_{3},\sigma _{3})\nonumber \\&\quad +\,\bigg (\frac{3}{2}\omega _{k0}+\frac{\alpha }{2}\frac{1}{\tan \big (\frac{\theta _{k}}{2}\big )} \bigg )\omega _{k3}(i_{1},\sigma _{1},i_{2},\sigma _{2},i_{3},\sigma _{3})\nonumber \\&\quad +\,6\omega _{k1}(i_{1},\sigma _{1})\bigg (\mathrm {Re}\,K_{1}(i_{2},\sigma _{2},i_{3}, \sigma _{3})|_{\mathrm {c}}\frac{1}{\tan (\theta _{k})}\nonumber \\&\quad +\,\mathrm {Im}\,K_{1}(i_{2}, \sigma _{2},i_{3},\sigma _{3})|_{\mathrm {c}}\bigg )\nonumber \\&\quad +\,6\omega _{k0}\bigg (\mathrm {\partial }_{\varepsilon _{1}}\mathrm {Re}\,K_{1}(i_{2}, \sigma _{2},i_{3},\sigma _{3})|_{\mathrm {c}}\frac{1}{\tan (\theta _{k})}\nonumber \\&\quad +\, \mathrm {\partial }_{\varepsilon _{1}}\mathrm {Im}\,K_{1}(i_{2},\sigma _{2},i_{3}, \sigma _{3})|_{\mathrm {c}}\bigg )\nonumber \\&\quad +\,6\omega _{k0}\bigg (\mathrm {Re}\,L_{1}(i_{1},\sigma _{1},i_{2},\sigma _{2},i_{3}, \sigma _{3})|_{\mathrm {c}}\frac{1}{\tan (\theta _{k})}\nonumber \\&\quad +\,\mathrm {Im}\,L_{1}(i_{1}, \sigma _{1},i_{2},\sigma _{2},i_{3},\sigma _{3})|_{\mathrm {c}}\bigg )\nonumber \\&\quad +\,6\bigg (\mathrm {\partial }_{\varepsilon _{1}}\mathrm {Im}\,K_{0}(i_{2},\sigma _{2},i_{3}, \sigma _{3})|_{\mathrm {c}}\frac{1}{\tan (\theta _{k})}\nonumber \\&\quad -\,\mathrm {\partial }_{\varepsilon _{1}} \mathrm {Re}\,K_{0}(i_{2},\sigma _{2},i_{3},\sigma _{3})|_{\mathrm {c}}\bigg )\nonumber \\&\quad +\,6\bigg (\mathrm {Im}\,L_{0}(i_{1},\sigma _{1},i_{2},\sigma _{2},i_{3},\sigma _{3})|_{ \mathrm {c}}\frac{1}{\tan (\theta _{k})}\nonumber \\&\quad -\mathrm {Re}\,L_{0}(i_{1},\sigma _{1},i_{2}, \sigma _{2},i_{3},\sigma _{3})|_{\mathrm {c}}\bigg ),\nonumber \\&\omega _{k3}(i_{1},\sigma _{1},i_{2},\sigma _{2},i_{3},\sigma _{3})=-3\frac{ \omega _{k1}(i_{1},\sigma _{1})\omega _{k2}(i_{2},\sigma _{2},i_{3},\sigma _{3})}{ \omega _{k0}}\nonumber \\&\quad +\,6\frac{\omega _{k1}(i_{1},\sigma _{1})}{\omega _{k0}}\bigg (\mathrm {Re}\,K_{1}(i_{2}, \sigma _{2},i_{3},\sigma _{3})|_{\mathrm {c}}\tan \big (\textstyle {\frac{\theta _{k}}{2}}\big )\nonumber \\&\quad -\, \mathrm {Im}\,K_{1}(i_{2},\sigma _{2},i_{3},\sigma _{3})|_{\mathrm {c}}\bigg )\nonumber \\&\quad +\,6\bigg (\mathrm {\partial }_{\varepsilon _{1}}\mathrm {Re}\,K_{1}(i_{2},\sigma _{2},i_{3}, \sigma _{3})|_{\mathrm {c}}\tan \big (\textstyle {\frac{\theta _{k}}{2}}\big )\nonumber \\&\quad -\,\mathrm {\partial }_{ \varepsilon _{1}}\mathrm {Im}\,K_{1}(i_{2},\sigma _{2},i_{3},\sigma _{3})|_{\mathrm {c}}\bigg )\nonumber \\&\quad +\,6\bigg (\mathrm {Re}\,L_{1}(i_{1},\sigma _{1},i_{2},\sigma _{2},i_{3},\sigma _{3})|_{ \mathrm {c}}\tan \big (\textstyle {\frac{\theta _{k}}{2}}\big )\nonumber \\&\quad -\,\mathrm {Im}\,L_{1}(i_{1}, \sigma _{1},i_{2},\sigma _{2},i_{3},\sigma _{3})|_{\mathrm {c}}\bigg )\nonumber \\&\quad +\,\frac{6}{\omega _{k0}}\bigg (\mathrm {\partial }_{\varepsilon _{1}}\mathrm {Re}\,K_{0}(i_{2}, \sigma _{2},i_{3},\sigma _{3})|_{\mathrm {c}}\nonumber \\&\quad +\,\mathrm {\partial }_{\varepsilon _{1}}\mathrm {Im} \,K_{0}(i_{2},\sigma _{2},i_{3},\sigma _{3})|_{\mathrm {c}}\tan (\textstyle {\frac{ \theta _{k}}{2}})\bigg )\nonumber \\&\quad +\,\frac{6}{\omega _{k0}}\bigg (\mathrm {Re}\,L_{0}(i_{1},\sigma _{1},i_{2},\sigma _{2},i_{3}, \sigma _{3})|_{\mathrm {c}}\nonumber \\&\quad +\,\mathrm {Im}\,L_{0}(i_{1},\sigma _{1},i_{2},\sigma _{2},i_{3}, \sigma _{3})|_{\mathrm {c}}\tan \big (\textstyle {\frac{\theta _{k}}{2}}\big )\bigg ). \end{aligned}$$
(101)

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Avedisov, S.S., Orosz, G. Analysis of connected vehicle networks using network-based perturbation techniques. Nonlinear Dyn 89, 1651–1672 (2017). https://doi.org/10.1007/s11071-017-3541-y

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