Loading [MathJax]/jax/output/SVG/jax.js
Research article Special Issues

Analysis of bus travel characteristics and predictions of elderly passenger flow based on smart card data

  • Preferential public transport policies provide an important social welfare support for travel by the elderly. However, the travel problems faced by the elderly, such as traffic congestion during peak hours, have not attracted enough attention from transportation-related departments. This study proposes a passenger flow prediction model for the elderly taking public transport and validates it using bus smart card data. The study incorporates short time series clustering (STSC) to integrate the elements of the heterogeneity of bus trips taken by the elderly, and accurately identifies the needs of elderly passengers by analysing passenger flow spatiotemporal characteristics. According to the needs and characteristics of passenger flow, a short time series clustering Seasonal Autoregressive Integrated Moving Average (STSC-SARIMA) model was constructed to predict passenger flow. The analysis of spatiotemporal travel characteristics identified three peak periods for the elderly to travel every day. The number of people traveling in the morning peak was significantly larger compared to other periods. At the same time, compared with bus lines running through central urban areas, multi-community, and densely populated areas, the passenger flow of bus lines in other areas dropped significantly. The study model was applied to Lhasa, China. The prediction results verify that the model has high prediction accuracy and applicability. In addition to the initial application, this predictive model provides new directions for bus passenger flow forecasting to support better public transport policy-making and improve elderly mobility.

    Citation: Gang Cheng, Changliang He. Analysis of bus travel characteristics and predictions of elderly passenger flow based on smart card data[J]. Electronic Research Archive, 2022, 30(12): 4256-4276. doi: 10.3934/era.2022217

    Related Papers:

    [1] Boyu Wang . A splitting lattice Boltzmann scheme for (2+1)-dimensional soliton solutions of the Kadomtsev-Petviashvili equation. AIMS Mathematics, 2023, 8(11): 28071-28089. doi: 10.3934/math.20231436
    [2] Amna Mumtaz, Muhammad Shakeel, Abdul Manan, Marouan Kouki, Nehad Ali Shah . Bifurcation and chaos analysis of the Kadomtsev Petviashvili-modified equal width equation using a novel analytical method: describing ocean waves. AIMS Mathematics, 2025, 10(4): 9516-9538. doi: 10.3934/math.2025439
    [3] Jalil Manafian, Onur Alp Ilhan, Sizar Abid Mohammed . Forming localized waves of the nonlinearity of the DNA dynamics arising in oscillator-chain of Peyrard-Bishop model. AIMS Mathematics, 2020, 5(3): 2461-2483. doi: 10.3934/math.2020163
    [4] Cheng Chen . Hyperbolic function solutions of time-fractional Kadomtsev-Petviashvili equation with variable-coefficients. AIMS Mathematics, 2022, 7(6): 10378-10386. doi: 10.3934/math.2022578
    [5] Abeer S. Khalifa, Hamdy M. Ahmed, Niveen M. Badra, Jalil Manafian, Khaled H. Mahmoud, Kottakkaran Sooppy Nisar, Wafaa B. Rabie . Derivation of some solitary wave solutions for the (3+1)- dimensional pKP-BKP equation via the IME tanh function method. AIMS Mathematics, 2024, 9(10): 27704-27720. doi: 10.3934/math.20241345
    [6] Wafaa B. Rabie, Hamdy M. Ahmed, Taher A. Nofal, Soliman Alkhatib . Wave solutions for the (3+1)-dimensional fractional Boussinesq-KP-type equation using the modified extended direct algebraic method. AIMS Mathematics, 2024, 9(11): 31882-31897. doi: 10.3934/math.20241532
    [7] Gulnur Yel, Haci Mehmet Baskonus, Wei Gao . New dark-bright soliton in the shallow water wave model. AIMS Mathematics, 2020, 5(4): 4027-4044. doi: 10.3934/math.2020259
    [8] Jamilu Sabi'u, Sekson Sirisubtawee, Surattana Sungnul, Mustafa Inc . Wave dynamics for the new generalized (3+1)-D Painlevé-type nonlinear evolution equation using efficient techniques. AIMS Mathematics, 2024, 9(11): 32366-32398. doi: 10.3934/math.20241552
    [9] Xiaoli Wang, Lizhen Wang . Traveling wave solutions of conformable time fractional Burgers type equations. AIMS Mathematics, 2021, 6(7): 7266-7284. doi: 10.3934/math.2021426
    [10] Zhe Ji, Yifan Nie, Lingfei Li, Yingying Xie, Mancang Wang . Rational solutions of an extended (2+1)-dimensional Camassa-Holm- Kadomtsev-Petviashvili equation in liquid drop. AIMS Mathematics, 2023, 8(2): 3163-3184. doi: 10.3934/math.2023162
  • Preferential public transport policies provide an important social welfare support for travel by the elderly. However, the travel problems faced by the elderly, such as traffic congestion during peak hours, have not attracted enough attention from transportation-related departments. This study proposes a passenger flow prediction model for the elderly taking public transport and validates it using bus smart card data. The study incorporates short time series clustering (STSC) to integrate the elements of the heterogeneity of bus trips taken by the elderly, and accurately identifies the needs of elderly passengers by analysing passenger flow spatiotemporal characteristics. According to the needs and characteristics of passenger flow, a short time series clustering Seasonal Autoregressive Integrated Moving Average (STSC-SARIMA) model was constructed to predict passenger flow. The analysis of spatiotemporal travel characteristics identified three peak periods for the elderly to travel every day. The number of people traveling in the morning peak was significantly larger compared to other periods. At the same time, compared with bus lines running through central urban areas, multi-community, and densely populated areas, the passenger flow of bus lines in other areas dropped significantly. The study model was applied to Lhasa, China. The prediction results verify that the model has high prediction accuracy and applicability. In addition to the initial application, this predictive model provides new directions for bus passenger flow forecasting to support better public transport policy-making and improve elderly mobility.



    Nonlinear evolution equations (NLEEs) play an important part in the study of nonlinear science, particular in plasma physics, quantum field theory, nonlinear wave propagation and nonlinear optical fibers so that it attracted the attention of a large number of scholars. The extended auxiliary equation technique [1], the Bernoulli's equation approach [2], the Exp-function technique [3], the homotopy analysis technique [4], the homotopy perturbation technique [5], the improved tan(ϕ/2)-expansion technique ([6,7]), the Hirota's bilinear technique [8,9,10,11,12,13,14,15], the He's variational principle [16,17], the binary Darboux transformation [18], the Lie group analysis [19,20], the Bäcklund transformation method [21], optimal galerkin-homotopy asymptotic method applied [22], and the multiple rogue waves method ([23,24]) have been proposed to solve NLEEs. By using these approaches, various exact solutions including soliton solution, lump solution, rogue wave solution, periodic solution, interaction solution, rational solution and high-order rational solution were obtained ([25,26]).

    In this paper, we mainly consider the following dynamical model, which can be used to describe some interesting (3+1)-dimensional waves of physics, namely, the generalized Kadomtsev-Petviashvili (gKP) equation [27]. That is

    (Ψt+6ΨΨx+Ψxxx)x+aΦyy=0, (1.1)

    and also above equation is integrable. Author of [28] introduced the modification of KP (mKP) equation [29] given

    4Ψt6Ψ2Ψx+Ψxxx+6Ψx1xΨy+Ψ1xΦyy=0. (1.2)

    The generalized KP (gKP) equation has been researched by some scholars [30,31,32] in which is given as

    (Ψt+αΨx+βΨΨx+γΨΨxxt)x+Ψyy=0. (1.3)

    The another type of gKP equation is given in [33] as below

    Ψxxxy+3(ΨxΨy)x+Ψtx+Ψty+ΨtzΨzz=0. (1.4)

    We first present the bilinear form for Eq (1.4), by taking the following first-order logarithmic transformation

    Ψ=2(lnf)x, (1.5)

    then, Eq (1.4) is turned into the bilinear form

    (D3xDy+DxDt+DyDt+DzDtD2z)f.f=0, (1.6)

    in which Dt,Dx,Dy and Dz are Hirota's bilinear frames. Cao [34] investigated the generalized B-type KP equation as follows

    Ψxxxy+3(ΨxΨy)x3ΨxzΨty=0. (1.7)

    Guan et al. [35] derived a (3+1)-dimensional gKP equation in below form

    Ψxxxy+3(ΨxΨy)x+αΨxxxz+3α(ΨxΨz)x+λ1Ψxt+λ2Φyt+λ3Φzt+ω1Φxz+ω2Φyz+ω3Φzz=0, (1.8)

    and some lump soliton solutions have been constructed using the Hirota bilinear method in [36]. Via transformation Ψ=2(lnf)x, the bilinear form of equation (1.8) reads:

    (D3xDy+αD3xDz+λ1DxDt+λ2DyDt+λ3DzDt+ω1DxDz+ω2DyDz+ω3D2z)f.f=0. (1.9)

    Most classical test functions for solving NLPDEs by using the several particular functions can be constructed via Hirota bilinear technique. In other words, Hirota operator covers most of the classical hypothesis function method. For example, the fractional generalized CBS-BK equation [37], the generalized Bogoyavlensky-Konopelchenko equation [38], the (2+1)-dimensional asymmetrical Nizhnik-Novikov-Veselov equation [39], and the (2+1)-dimensional generalized variable-coefficient KP-Burgers-type equation [40]. Diverse kinds of studies on solve NLPDEs were perused via mighty authors in which some of them can be stated, for instance, multivariate rogue wave to some PDEs [41], interaction lump solutions the gKP equation [42], the (1+1)-dimensional coupled integrable dispersionless equations [43]. Therefore, we embark on the new research topic of constructing the analytic solutions of nonlinear PDEs by exploring the bilinear method. According to recent studies, we can obtain some of the new exact analytic solutions of nonlinear PDEs by way of constructing their corresponding bilinear differential equations in [44,45,46,47]. Here, we will study the multiple Exp-function method (MEFM) for determining the multiple soliton solutions (MSSs). The MEFM employed by some of powerful authors for various nonlinear equations have been surveyed in more studies in [31,48,49,50]. Authors of [51] utilized the reduced differential transform method for solving partial differential equations. Also, Yu and Sun [52] studied the dimensionally reduced generalized KP equations by help of Hirita bilinear method and obtained of lump solutions.

    The outline of our paper is as follows: the multiple Exp-function scheme has been summarized in section 2. In sections 3, the KP equation, will investigate to finding 1-wave, 2-wave, and three-wave solutions. The Sections 4-6 devote to determined the periodic, cross-kink, and solitary wave solutions. Moreover, in Section 7, the modulation instability analysis is investigated. Finally in Section 8, the SIVP technique is considered with four cases for finding the solitary, bright, dark and singular wave solutions. A few of conclusions and outlook will be given in the final section.

    This method was summarized and improved for achieving the analytic solutions of NLPDEs:

    Step 1. Assume a nonlinear PDE is given in general frame as follows

    N(x,y,t,Ψ,Ψx,Ψy,Ψz,Ψt,Ψxx,Ψtt,...)=0. (2.1)

    Take the novel variables ξi=ξi(x,y,z,t),1in, by differentiable frames:

    ξi,x=αiξi,  ξi,y=βiξi,  ξi,z=γiξi,  ξi,t=δiξi,  1in, (2.2)

    where αi,βi,γi,1in, are unfound amounts. It noted that one can get as the following function

    ξi=ϖieθi,  θi=αix+βiy+γizδit,  1in, (2.3)

    where ϖi,1in, unspecified amounts.

    Step 2. Assuming the solution of the Eq (2.1) is function of variables ξi,1in:

    Ψ=Δ(ξ1,ξ2,...,ξn)Ω(ξ1,ξ2,...,ξn),  Δ=nr,s=1Mi,j=0Δrs,ijξirξjs,  Ω=nr,s=1Ni,j=0Ωrs,ijξirξjs,   (2.4)

    in which Δrs,ij and Ωrs,ij are amounts to be remained. Replacing Eq (2.4) into Eq (2.1) can be achieved the below form as:

    Ψ=Δ(ϖ1eα1x+β1y+γ1zδ1t,...,ϖneαnx+βny+γnzδnt)Ω(ϖ1eα1x+β1y+γ1zδ1t,...,ϖneαnx+βny+γnzδnt), (2.5)

    and also we have

    Δt=ni=1Δξiξi,t,  Ωt=ni=1Ωξiξi,t,  Δx=ni=1Δξiξi,x,  Ωx=ni=1Ωξiξi,x,  Δy=ni=1Δξiξi,y,  Δz=ni=1Δξiξi,z, Ωy=ni=1Ωξiξi,y,  Ωz=ni=1Ωξiξi,z,  (2.6)
       Ψt=Ωni=1Δξiξi,tΔni=1Ωξiξi,tΩ2,  Ψx=Ωni=1Δξiξi,xΔni=1Ωξiξi,xΩ2,
    Ψy=Ωni=1Δξiξi,yΔni=1Ωξiξi,yΩ2,  Ψz=Ωni=1Δξiξi,zΔni=1Ωξiξi,zΩ2.

    The one-wave function of the solution will be reduced as below form

    Ψ=2Δ1Ω1,  Ω1=1+ρ1+ρ2 eα1x+β1y+γ1zδ1t,  Δ1=σ1+σ2 eα1x+β1y+γ1zδ1t, (3.1)

    in which ρ1,ρ2,σ1 and σ2 are unspecified amounts. Substituting (3.1) into Eq (1.8), the below cases will be concluded as:

    Case I:

    α1=α1,  β1=β1,  ρ1=ρ1,  ρ2=  σ2(ρ1+1)σ1  ,  σ1=σ1,  σ2=σ2,  δ1=δ1,  γ1=γ1,  γ2=γ2. (3.2)

    Case II:

    α1=α1,  β1=  αα1  3γ1α1δ1λ1+α1γ1ω1δ1γ1λ3+γ12ω3  α13δ1λ2+γ1ω2,  ρ1=1,  ρ2=ρ2,   (3.3)
    σ1=σ1,  σ2=σ2,  δ1=δ1,  γ1=γ1,  γ2=γ2.

    Case III:

    α1=  γ1(αδ1λ2αγ1ω2δ1λ3+γ1ω3)δ1λ1γ1ω1,  β1=αγ1,  ρ1=ρ1,  ρ2=ρ2,   (3.4)
    σ1=σ1,  σ2=σ2,  δ1=δ1,    γ1=γ1,  γ2=γ2.

    Case IV:

    α1=ϑ,  β1=β1, or  β1=αγ1,   ρ1=1,  ρ2=ρ2,  σ1=σ1,  σ2=σ2, δ1=ϑ3+γ1ω2λ2,  γ1=γ1,  γ2=γ2,  (3.5)

    in which ϑ, solves the equation λ1ϑ4+(γ1λ3αγ1λ2)ϑ3+(γ1λ1ω2γ1λ2ω1)ϑγ21ω3λ2+γ21λ3ω2=0.

    For example, the 1-wave solution for Case III will be considered as

    Ψ=2σ1+σ2e  γ1(αδ1λ2αγ1ω2δ1λ3+γ1ω3)δ1λ1γ1ω1  xαγ1y+γ1zδ1t1+ρ1+ρ2e  γ1(αδ1λ2αγ1ω2δ1λ3+γ1ω3)δ1λ1γ1ω1    xαγ1y+γ1zδ1t. (3.6)

    The two-wave function of the solution will be reduced as below form

    Ψ=2Δ2Ω2, (3.7)
    Ω2=1+σ1eα1x+β1y+γ1zδ1t+σ2eα2x+β2y+γ2zδ2t+σ1σ2σ12e(α1+α2)x+(β1+β2)y+(γ1+γ2)z(δ1+δ2)t,   (3.8)
    Δ2=ρ1eα1x+β1y+γ1zδ1t+ρ2eα2x+β2y+γ2zδ2t+ρ1ρ2ρ12e(α1+α2)x+(β1+β2)y+(γ1+γ2)z(δ1+δ2)t.

    Substituting (3.7) in terms of (3.8) into Eq (1.8), the below cases will be resulted as:

    Case I:

    α1=0,  α2=α2,  β1=β1,  β2=αγ2,  δ1=  γ1(β1ω2+ω3γ1)β1λ2+γ1λ3,  δ2=  γ2(αγ2ω 2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3,γ1=γ1,  γ2=γ2,  ρ1=0,  ρ2=ρ2, (3.9)
      ρ12=ρ12,  σ1=σ1,  σ2=σ2,  σ12=1.

    Case II:

    α1=α1,  α2=0,  β1=αγ1,  β2=β2,  δ1=  γ1(αγ1ω 2α1ω1ω3γ1)αγ1λ2α1λ1γ1λ3,  δ2=  γ2(β2ω2+γ2ω3)β2λ2+γ2λ3,γ1=γ1,  γ2=γ2,  ρ1=0,  (3.10)
     ρ2=ρ2,  ρ12=ρ12,  σ1=σ1,  σ2=σ2,  σ12=1.

    Case III:

    α1=α1,  α2=α2,  β1=αγ1,  β2=αγ2,  δ1=  γ1(αγ1ω 2α1ω1ω3γ1)αγ1λ2α1λ1γ1λ3,  δ2=  γ2(αγ2ω 2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3,γ1=γ1,  γ2=γ2,  ρ1=0, (3.11)
      ρ2=ρ2,  ρ12=ρ12,  σ1=σ1,  σ2=σ2,  σ12=1.

    Case IV:

    α1=0,  α2=α2,  β1=β1,  β2=αγ2,  δ1=  γ1(β1ω2+ω3γ1)β1λ2+γ1λ3,  δ2=  γ2(αγ2ω 2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3,γ1=γ1,  γ2=γ2,  ρ1=ρ1,  ρ2=0, (3.12)
      ρ12=ρ12,  σ1=σ1,  σ2=σ2,  σ12=1.

    Case V:

    α1=α1,  α2=α2,  β1=αγ1,  β2=αγ2,  δ1=  γ1(αγ1ω 2α1ω1ω3γ1)αγ1λ2α1λ1γ1λ3,  δ2=  γ2(αγ2ω 2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3,γ1=γ1,  γ2=γ2,  ρ1=ρ1,  ρ2=0, (3.13)
      ρ12=ρ12,  σ1=σ1,  σ2=σ2,  σ12=1.

    Case VI:

    α1=α1,  α2=α2,  β1=αα1γ2  α2,  β2=αγ2,  δ1=  α1γ2(αγ 2ω2α2ω1γ2ω3)α2(αγ2λ2α2λ1γ2λ3),  δ2=  γ2(αγ2ω 2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3,γ1=γ1,  γ2=γ2,  ρ1=ρ1,  ρ2=0, (3.14)
      ρ12=ρ12,  σ1=σ1,  σ2=σ2,  σ12=σ12.

    Case VII:

    α1=α1,  α2=α2,  β1=αα1γ2  α2,  β2=αγ2,  δ1=  α1γ2(αγ 2ω2α2ω1γ2ω3)α2(αγ2λ2α2λ1γ2λ3),  δ2=  γ2(αγ2ω 2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3,γ1=  α1γ2  α2  ,  γ2=γ2,  ρ1=ρ1,  ρ2=ρ2, (3.15)
      ρ12=ρ12,  σ1=0,  σ2=σ2,  σ12=σ12.

    Case VIII:

    α1=12α2,  α2=α2,  β1=12αγ2,  β2=αγ2,  δ1=12  γ2(αγ2ω2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3,  δ2=  γ2(αγ2ω 2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3,γ1=12γ2,  γ2=γ2,  ρ1=ρ1,  (3.16)
     ρ2=ρ2,  ρ12=ρ12,  σ1=σ1,  σ2=σ2,  σ12=0.

    For instance, the 2-wave solution for Case I will be taken as

    Ψ1=2ρ2e  tγ2(αγ 2ω2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3  +xα2yαγ2+zγ2  /(1+σ1etγ1(β1ω2+ω3γ1)β1λ2+γ1λ3  +yβ1+zγ1  + (3.17)
    σ2e  tγ2(αγ2ω2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3  +xα2yαγ2+zγ2  +σ1σ2e  tγ1(β1ω2+ω3γ1)β1λ2+γ1λ3    tγ2(αγ2ω2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3  +xα2+yβ1yαγ2+zγ1+zγ2).

    Also, the 2-wave solution for Case III will be considered as

    Ψ2=2ρ2e  tγ2(αγ 2ω2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3  +xα2yαγ2+zγ2  /(1+σ1etγ1(αγ1ω2α1ω1ω3γ1)αγ1λ2α1λ1γ1λ3  +xα1yαγ1+zγ1  + (3.18)
    σ2e  tγ2(αγ2ω2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3  +xα2yαγ2+zγ2  +σ1σ2e  tγ1(αγ1ω2α1ω1ω3γ1)αγ1λ2α1λ1γ1λ3    tγ2(αγ2ω2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3  +xα1+xα2yαγ1yαγ2+zγ1+zγ2).

    And finally, the resulting two-wave solution for Case VIII will be read as

    Ψ3(x,y,z,t)=2(ρ1e12  tγ2(αγ2ω2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3  +12xα212yαγ2+12zγ2  +ρ2e  tγ2(αγ2ω2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3  +xα2yαγ2+zγ2  + (3.19)
    ρ1ρ2ρ12 e32tγ2(αγ2ω2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3  +32xα232yαγ2+32zγ2  )/(1+σ1e12  tγ2(αγ2ω2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3  +12xα212yαγ2+12zγ2  
    +σ2e  tγ2(αγ2ω2α2ω1γ2ω3)αγ2λ2α2λ1γ2λ3  +xα2yαγ2+zγ2  ).

    The triple-wave function of the solution will be reduced as below form

    Ψ=Δ3Ω3, (3.20)
    Ω3=1+ρ1eΛ1+ρ2eΛ2+ρ3eΛ3+ρ1ρ2ρ12eΛ1+Λ2+ρ1ρ3ρ13eΛ1+Λ3+ρ2ρ3ρ23eΛ2+Λ3+ρ1ρ2ρ3ρ12ρ13ρ23eΛ1+Λ2+Λ3,   (3.21)
    Δ3=2σ1eΛ1+2σ2eΛ2+2σ1σ2σ12eΛ1+Λ2+2σ1σ3σ13eΛ1+Λ3+2σ2σ3σ23eΛ2+Λ3+
    2σ1σ2σ3σ12σ13σ23eΛ1+Λ2+Λ3,

    in which Λi=αix+βiy+γixδit,i=1,2,3. Inserting (3.20) in terms of (3.21) into Eq (1.8), the below case will be reached:

    αi=αi,  γi=γi,  σi=σi,  ρ1=0,  ρ2=ρ2,  ρ3=ρ3,  βi=αγi,  δi=  γi(αγiω 2αiω1ω3γi)αγiλ2αiλ1γiλ3,  i=1,2,3,  ρij=ρij,  σij=1,  i,j=1,2,3, ij. (3.22)

    Then, the solution is

    Ψ1=2σ1eΛ1+2σ2eΛ2+2σ1σ2eΛ1+Λ2+2σ1σ3eΛ1+Λ3+2σ2σ3eΛ2+Λ3+2σ1σ2σ3eΛ1+Λ2+Λ3  1+ρ2eΛ2+ρ3eΛ3+ρ2ρ3ρ23eΛ2+Λ3  , (3.23)

    in which Λi=αixαγiy+γixγi(αγiω 2αiω1ω3γi)αγiλ2αiλ1γiλ3  t,i=1,2,3 and Ψ=Ψ(x,y,z,t).

    The triangular periodic waves for Eq (1.8) can be assumed as below:

    f=exp(τ1)+a16exp(τ1)+cosh(τ2)+cos(τ3)+a17,   τ1=4i=1aixi+a5,   τ2=9i=6aixi5+a10,    (4.1)
    τ3=14i=11aixi10+a15,  (x1,x2,x3,x4)=(x,t)=(x,y,z,t),   Ψ(x,t)=v0+2ln(f)x, (4.2)

    in which ai,i=1,...,17 are unfound values. Substituting (4.1) and (4.2) into Eq (1.8) the below consequences will be gained:

    Case I:

    f=ea4t+a1x  y(a3a14ω3a4a12ω2a4a13ω3)a14ω2  +a3z+a5  +cosh(a9ty(a8a14ω3a9a12ω2a 9a13ω3)a14ω2  +a8z+a 10)+cos(ta14+ya12+za13+a15). (4.3)

    Appending (4.3) into (4.1) and (4.2), the soliton-periodic wave solution of Eq (1.8) as below will be achieved:

    Ψ1=v0+2a1ea4t+a1x  y(a3a14ω3a4a12ω2a4a13ω3)a14ω2  +a3z+a5  f. (4.4)

    By selecting the suitable values of parameters including

    a1=1,a3=1.5,a4=2,a5=1.5,a8=2,a9=1.5,a10=1,a12=2,a13=2.5,a14=1,a15=3.2,ω2=1.5,ω3=1.2,

    the graphical display of soliton-periodic wave solution is offered in Figure 1 such as 3D plot and density plot.

    Figure 1.  The soliton-periodic solution (4.4) at (f1, f2) x=2,y=2, (f3, f4) x=0,y=0, and (f5, f6) x=2,y=2.

    Case II:

    f=ea4t+a1x+ya2  (a1  3a 14a4a13ω2)za14ω2  +a5  +cosh(a9t+ya7+  a9a13za14  +a10)+cos(ta14+ya12+za13+a 15). (4.5)

    Plugging (4.5) into (4.1) and (4.2), obtain a soliton-periodic wave solution of Eq (1.8) as below case:

    Ψ2=v0+2a1ea4t+a1x+ya2  (a13a14a4a13ω2)za14ω2  +a5  f. (4.6)

    Case III:

    f=ea4t+a1x  y(a3ω3+a4λ3)ω2  +a3z+a5  +cosh(a9t  y(a8ω3+a9λ3)ω2  +a8z+a10)+cos(  a13ω3yω2  +za13+a15). (4.7)

    Incorporating (4.7) into (4.1) and (4.2), the soliton-periodic wave solution of Eq (1.8) will be gained as below:

    Ψ3=v0+2a1ea4t+a1x  y(a3ω3+a4λ3)ω2  +a3z+a5  f. (4.8)

    Case IV:

    f=ea4t  y(a3a9ω3a4a7ω2a4a8ω3)a9ω2  +a3z+a5  +cosh(a9t+ya7+a8z+a10)+cos(xa11+a15). (4.9)

    Plugging (4.9) into (4.1) and (4.2), the soliton-periodic wave solution of Eq (1.8) will be achieved as below:

    Ψ4=v02sin(xa11+a15)a11f. (4.10)

    Case V:

    f=ea4t+ya2+  a4a8za9  +a5+cosh(a9t+ya7+a8z+a10)+cos(xa11  (a112ω3+ω 1ω2)a11yω22  +  a 11  3zω2  +a15). (4.11)

    Incorporating (4.11) into (4.1) and (4.2), we capture a soliton-periodic wave solution of Eq (1.8) as below:

    Ψ5=v02sin(xa11  (a11  2ω3+ω1ω2)a11yω2  2  + a113zω2  +a15)a11f. (4.12)

    Case VI:

    f=ea4t+  ω2(a3a9a4a8)xa9a112  13  y(2αa9a116ω2+2a9a11  6ω3+3αa32a9ω2  33αa3a4a8ω23+2a9a114ω1ω2)ω23(a3a9a4a8)  +a3z+a5+cosh(a9t13  yΩa4ω2  3(a3a9a4a8)(a92a116+ω22(a3a9a4a8)2)+a8z+a10)+cos(xa11  αa113yω2  +a113zω2  +a15), (4.13)
    Ω=3αa4a8ω25(a3a9a4a8)3+αa92a116ω23(a3a9+2a4a8)(a3a9a4a8)a92a114ω22(a3a9a4a8)2(a112ω3+ω1ω2)a92a1112(3a42a9  2)(αω2ω3).

    Appending (4.13) into (4.1) and (4.2), the soliton-periodic wave solution of Eq (1.8) will be obtained as below:

    Ψ5=v0+2f[  ω2(a3a9a4a8)a9a112ea4t+  ω2(a3a9a4a8)xa9a112  13  y(2αa9a116ω2+2a9a116ω3+3αa32a9ω233αa3a4a8ω23+2a9a114ω1ω2)ω23(a3a9a4a8)+a3z+a5  +sin(xa11+  αa113yω2    a113zω2  a15)a11]. (4.14)

    By selecting suitable values of parameters including

    α=0.5,a3=1,a4=1.5,a5=2,a8=2,a9=1.5,a10=1,a11=2,a13=2.5,a14=1,a15=3.2,ω1=1.5,ω2=1.2,ω3=1.5,

    the graphical display of soliton-periodic wave solution is offered in Figure 2 such as 3D chart and density chart.

    Figure 2.  The soliton-periodic solution (4.14) at (f1, f2) x=2,y=2, (f3, f4) x=0,y=0, and (f5, f6) x=2,y=2.

    Case VII:

    f=e  Ωa9ta113  +a1x16  y(6a14a115ω33a14a113ω1ω2+2a117ω1ω2+6Ωa1a8a112ω2ω3+3Ωa1a8ω1ω2  2)a115ω22a1  +  (a13a113+Ωa8ω2)za11  3ω2  +a5  + (4.15)
    cosh(a9t+ya7+a8z+a10)+cos(xa1112  (2a112ω3+ω1ω2)a11yω22  +  a113zω2  +a15),
    Ω=a162a14a112+a116.

    Incorporating (4.15) into (4.1) and (4.2), the soliton-periodic wave solution of Eq (1.8) will be received as below:

    Ψ5=v0+2f[a1e  Ωa9ta113  +a1x16  y(6a14a115ω33a14a11  3ω1ω2+2a117ω1ω2+6Ωa1a8a112ω2ω3+3Ωa1a8ω1ω22)a115ω22a1  +  (a13a11  3+Ωa8ω2)za113ω2  +a5  sin(xa1112  (2a11  2ω3+ω1ω2)a11yω22  +  a113zω2  +a15)a11]. (4.16)

    By selecting the specific amounts of parameters including

    α=0.5,a1=1,a4=a9=ω1=ω3=1.5,,a5=2,a7=1.5,a8=2,a10=1,a11=2,a13=2.5,a14=1,a15=3.2,ω2=1.2,

    the graphical display of soliton-periodic wave solution is offered in Figure 3 such as 3D chart, density chart, and 2D chart and below cases:

    (f3) y=1,2,3,  (f6) y=1,2,3, and  (f9) y=1,2,3.
    Figure 3.  The soliton-periodic solution (4.16) at (f1, f2) z=2,t=2, (f4, f5) z=0,t=0, and (f7, f8) z=2,t=2.

    Three function containing exponential, hyperbolic, and triangular periodic waves for Eq (1.8) can be assumed as the following:

    f=exp(τ1)+a16exp(τ1)+sinh(τ2)+sin(τ3)+a17,   τ1=4i=1aixi+a5,   τ2=9i=6aixi5+a10, (5.1)
    τ3=14i=11aixi10+a15,  (x1,x2,x3,x4)=(x,t)=(x,y,z,t),   Ψ(x,t)=v0+2ln(f)x, (5.2)

    in which ai,i=1,...,17 are unfound values. Substituting (5.2) into Eq (1.8) the below consequences will be gained:

    Case I:

    f=ea4t+a1x  (a3a14ω3a4a12ω2a4a13ω3)ya14ω2  +a3z+a5  +sinh(a9t  (a8a14ω3a9a12ω2a9a13ω3)ya14ω2  +a8z+a10) (5.3)
    +sin(ta14+ya12+za13+a15).

    Substituting (5.3) into (5.1) and (5.2), the cross-kink solution of Eq (1.8) will be gained as the following:

    Ψ1=v0+2a1ea4t+a1x  (a3a14ω3a4a12ω2a4a13ω3)ya14ω2  +a3z+a5  f. (5.4)

    By selecting the suitable values of parameters including

    a1=1,a3=3,a4=2,a5=1.5,a8=1.7,a9=1.5,a10=1.5,a12=2.5,a13=1.1,a14=2.1,a15=3.2,ω2=1,ω3=1.5,

    the graphical representation of cross-kink wave solution is offered in Figure 4 such 3D plot and density plot.

    Figure 4.  The cross-kink wave solution (5.4) at (f1, f2) x=3,y=2, (f3, f4) x=0,y=2, and (f5, f6) x=3,y=2.

    Case II:

    f=ea4t+a1x+a2y  (a1  3a14a4a13ω2)za14ω2  +a5  +sinh(a9t+a7y+  a9a13za14  +a10)+sin(ta14+ya12+za13+a15). (5.5)

    Putting (5.5) into (5.1) and (5.2), the cross-kink solution of Eq (1.8) will be received as the following:

    Ψ2=v0+2a1ea4t+a1x+a2y(a13a14a4a13ω2)za14ω2  +a5  f. (5.6)

    By selecting the suitable values of parameters including

    a1=1,a3=3,a4=2,a5=1.5,a8=1.7,a9=1.5,a10=1.5,a12=2.5,a13=1.1,a14=2.1,a15=3.2,ω2=1,ω3=1.5,

    the graphical exhibition of cross-kink solution is offered in Figure 5 such as 3D chart and density chart.

    Figure 5.  The cross-kink wave solution (5.6) at (f1, f2) x=3,y=2, (f3, f4) x=0,y=2, and (f5, f6) x=3,y=2.

    Case III:

    f=ea4t+a1x  (a3ω3+a4λ3)yω2  +a3z+a5  +sinh(a9t  (a8ω3+a9λ 3)yω2  +a8z+a10)+sin(  a13ω3yω2  +a13z+a15). (5.7)

    Plugging (5.7) into (5.1) and (5.2), the cross-kink solution of Eq (1.8) will be obtained as the following:

    Ψ3=v0+2a1ea4t+a1x  (a3ω3+a4λ3)yω2  +a3z+a5  f. (5.8)

    Case IV:

    f=e  a9(a112(ω3a12ω1ω2)(a1  3+a3ω2)a15ω1ω2)ta12a7a112ω22  +a1x  a3(ω3a12ω1ω2)ya12ω2  +a3z+a5  +sin(xa11+a15)+sinh(a9t+a7y  (a13+a3ω2)a12a7a112ω2za11  2(ω3a12ω1ω2)(a13+a3ω2)a15ω1ω2  +a10). (5.9)

    Inserting (5.9) into (5.1) and (5.2), the cross-kink solution of Eq (1.8) will be gained as the following:

    Ψ4=v0+2a1e  a9(a11  2(ω3a12ω1ω2)(a13+a3ω2)a15ω1ω2)ta12a7a112ω2  2  +a1x  a3(ω3a12ω1ω2)ya12ω2  +a3z+a5  +cos(xa11+a15)a11  f. (5.10)

    Case V:

    f=ea4t+a2y+  a4a8za9  +a5  +sinh(a9t+a7y+a8z+a10)+sin(xa11  a11(a112ω3+ω1ω2)yω22  +  a113zω2  +a15). (5.11)

    Substituting (5.11) into (5.1) and (5.2), the cross-kink solution of Eq (1.8) will be received as the following:

    Ψ5=v0+2cos(xa11  a11(a112ω3+ω1ω2)yω22+  a113zω2  +a15)a11  f. (5.12)

    By selecting the suitable values of parameters including

    a1=1,a3=3,a4=2,a5=1.5,a8=1.7,a9=1.5,a10=1.5,a12=2.5,a13=1.1,a14=2.1,a15=3.2,ω2=1,ω3=1.5,

    the graphical exhibition of cross-kink solution is offered in Figure 6 such as 3D chart and density chart.

    Figure 6.  The cross-kink wave solution (5.6) at (f1, f2) x=3,y=2, (f3, f4) x=0,y=2, and (f5, f6) x=3,y=2.

    Case VI:

    f=e  Ωa9ta113  +a1x112  (3Ωa1a8ω2(4a112ω3+3ω1ω2)12a14a115ω3a113ω1ω2(9a142a112a122a11  4))ya115ω22a1  +  (a13a113+Ωa8ω2)zω2a113  +a5  sinh(a9t+a7y+a8z+a10)+sin(xa11112  a11(12a112ω3+7ω1ω2)yω22  +  a311zω2+a15), (5.13)
    Ω=3a164a14a112+a116.

    Putting (5.13) into (5.1) and (5.2), the cross-kink solution of Eq (1.8) will be concluded as the following:

    Ψ5=v0+2f[a1e  Ωa9ta113  +a1x112  (3Ωa1a8ω2(4a112ω3+3ω1ω2)12a14a11  5ω3a113ω1ω2(9a1  42a112a122a114))ya115ω22a1  +  (a13a11  3+Ωa8ω2)zω2a113  +a5  +cos(xa11112  a11(12a112ω3+7ω1ω2)yω2  2  +  a113zω2  +a15)a11]. (5.14)

    By selecting suitable values of parameters including

    α=0.5,a3=1,a4=a9=ω1=ω3=1.5,a5=2,a8=2,a10=1,a11=2,a13=2.5,a14=1,a15=3.2,ω2=1.2,

    the graphical representation of cross-kink solution is offered in Figure 7 such as 3D chart and density chart.

    Figure 7.  The cross-kink wave solution (5.14) at (f1, f2) x=2,y=2, (f3, f4) x=0,y=0, and (f5, f6) x=2,y=2.

    Case VII:

    f=e  Ωa9ta113  +a1x16 y(6a14a115ω33a14a113ω1ω2+2a11  7ω1ω2+6Ωa1a8a11  2ω2ω3+3Ωa1a8ω1 ω22)a115ω22a1  + (a13a113+Ωa8ω2)za113ω2  +a5  +cosh(a9t+ya7+a8z+a10)+cos(xa 1112  (2a112ω3+ω1ω2)a11yω22  +  a113zω2  +a15), (5.15)
    Ω=a162a14a112+a116.

    Inserting (5.15) into (5.1) and (5.2), the cross-kink solution of Eq (1.8) will be gained as the following:

    Ψ5=v0+2f[a1e  Ωa9ta113  +a1x16  y(6a14a115ω33a14a11  3ω1ω2+2a117ω1ω2+6Ωa1a8a112ω2ω3+3Ωa1a8ω1ω22)a115ω22a1  +  (a13a11  3+Ωa8ω2)za113ω2  +a5  sin(xa1112  (2a11  2ω3+ω1ω2)a11yω22  +  a113zω2  +a15)a11]. (5.16)

    By selecting suitable values of parameters including

    α=0.5,a1=1,a5=1.5,a8=2,a9=0.5,a10=1.5,a15=3.2,ω1=1.5,ω2=1.2,ω3=1.5,

    the graphical representation of cross-kink solution is offered in Figure 8 such as 3D chart and density chart.

    Figure 8.  The lump-periodic solution (5.16) at (f1, f2) z=2, (f4, f5) z=0, and (f7, f8) z=2.

    Three function containing exponential, hyperbolic, and triangular periodic waves for Eq (1.8) can be assumed as the following:

    f=exp(τ1)+a16exp(τ1)+tanh(τ2)+tan(τ3)+a17,   τ1=4i=1aixi+a5,   τ2=9i=6aixi5+a10,    (6.1)
    τ3=14i=11aixi10+a15,  (x1,x2,x3,x4)=(x,t)=(x,y,z,t),   Ψ(x,t)=v0+2ln(f)x, (6.2)

    in which ai,i=1,...,17 are unfound values. Substituting (6.2) into Eq (1.8) the below consequences will be gained:

    Case I:

    f=ea4t+a1x+a2y  (a1  3+a4λ2)zω2  +a5  +tanh(ya7+a10)+tan(ya12+a15)+a17. (6.3)

    Substituting (6.3) into (6.1) and (6.2), we can capture a solitary wave solution of Eq (1.8) as the following:

    Ψ1=v0+2a1ea4t+a1x+a2y(a13+a4λ2)zω2  +a5  f. (6.4)

    Case II:

    f=ea1x+a2y  a13zω2  +a5  +tanh(ya7+a10)+tan(ya12+a15)+a17. (6.5)

    Inserting (6.5) into (6.1) and (6.2), we can capture a solitary wave solution of Eq (1.8) as below:

    Ψ2=v0+2a1ea1x+a2y  a13zω2  +a5  f. (6.6)

    Case III:

    f=ea4t+a1x+a2y  (a1  3ω3+a4λ3ω2)zω2ω3  +a5  +tanh(ta9+ya7  za9λ3  ω3  +a10)+tan(ya12+a15)+a17. (6.7)

    Putting (6.7) into (6.1) and (6.2), the solitary wave solution of Eq (1.8) can be indicated as below:

    Ψ3=v0+2a1ea4t+a1x+a2y(a13ω3+a4λ3ω2)zω2ω3  +a5  f. (6.8)

    Case IV:

    f=ea4t+a1x+a2y+za3+a5  tanh(  ya8ω3  ω2  za8a10)tan(  ya13ω3  ω2  a13za15)+a17. (6.9)

    Putting (6.9) into (6.1) and (6.2), the solitary wave of Eq (1.8) can be stated as the following:

    Ψ4=v0+2a1ea4t+a1x+a2y+za3+a5  f. (6.10)

    By selecting the suitable values of parameters including

    a1=1,a2=1.5,a3=2,a4=2,a5=1.5,a8=2,a9=1.5,a10=1.5,a13=2,a13=1.1,a15=3.2,a17=2,ω2=1.5,
    ω3=1.2,λ2=1,λ3=1.5,x=2,t=2,

    with the following components

    α=  a13ω3a1ω1ω2a2ω22a3ω2ω3+a4λ2ω3a4λ3ω2  ω2a13  ,  λ1=  a13a2ω2+a13a3ω3+a2a4λ2ω2+a3a4λ2ω3  a1a4ω2,

    the graphical representation of rational solitary solution is offered in Figure 9 such as 3D chart and density chart.

    Figure 9.  The rational solitary wave solution (6.10) at (f1) z=0, (f2) z=1, and (f3) z=3.

    Case V:

     f=ea1x  a3ω3yω2  +za3+a5  tanh(  ya8ω3  ω2za8a10)tan(  ya13ω3  ω2  a13za15)+a17. (6.11)

    Incorporating (6.11) into (6.1) and (6.2), the solitary solution of Eq (1.8) will be gained as below:

    Ψ5=v0+2a1ea1x  a3ω3yω2  +za3+a5  f,  α=  a12ω3ω1ω2  a12ω2  . (6.12)

    Case VI:

    f=ea4t+a1x  (a3ω3+a4λ3)yω2  +za3+a5  +tanh(ta9  y(a8ω3+a9λ3)ω2  +za8+a10)tan( ya13ω3  ω2  a13za15)+a17. (6.13)

    Appending (6.13) into (6.1) and (6.2), the solitary solution of Eq (1.8) can be written as the following:

    Ψ6=v0+2a1ea4t+a1x  (a3ω 3+a4λ3)yω2  +za3+a5  f,  α=  a12ω3ω1ω2  a12ω2  . (6.14)

    Case VII:

    f=ea4t+a1x  a3ω3yω2  +za3+a5  +tanh(ta9  ya8ω3  ω2  +za8+a10)tan(  ya13ω3  ω2  a13za15)+a17. (6.15)

    Appending (6.15) into (6.1) and (6.2), the solitary solution of Eq (1.8) will be obtained as below:

    Ψ7=v0+2a1ea4t+a1x  a3ω3yω2  +za3+a5  f,  α=  a12ω3ω1ω2  a1  2ω2,  λ2=  λ3ω2  ω3  . (6.16)

    By choosing the specific amounts of parameters including

    a1=1,a2=1.5,a3=2,a4=2,a5=1.5,a8=2,a9=1.3,a10=1.5,a13=2,a13=2,a15=3.2,a17=2,ω2=1.5,ω3=1.2,λ2=1,λ3=1.5,x=2,t=2,

    the graphical representation of rational solitary solution is offered in Figure 10 such as 3D chart and density chart.

    Figure 10.  The rational solitary wave solution (6.16) at (f1) z=0, (f2) z=1, and (f3) z=3.

    Case VIII:

    f=ea4t+a1x+a2y  (a1  3a 14a4a13ω2)za14ω2  +a5  +tanh(ta9+ya7+  za9a13  a14  +a10)+tan(ta14+ya12+a13z+a 15)+a17. (6.17)

    Incorporating (6.17) into (6.1) and (6.2), the solitary solution of Eq (1.8) will be received as below:

    Ψ8=v0+2a1ea4t+a1x+a2y(a13a14a4a13ω2)za14ω2  +a5  f,  α=  a1  2ω3ω1ω2  a12ω2,  λ2=  a13ω2  a14,  λ3=a13ω3  a14  . (6.18)

    Case IX:

    f=ea4t+a1x  (a3a14ω3a4a12ω2a4a13ω3)ya14ω2  +a3z+a5   (6.19)
    +tanh(ta9  y(a8a14ω3a9a12ω2a 9a13ω3)a14ω2  +za8+a 10) (6.20)
    +tan(ta14+ya12+za13+a15)+a17. (6.21)

    Appending (6.21) into (6.1) and (6.2), the solitary solution of Eq (1.8) can be reached as below:

    Ψ9=v0+2a1ea4t+a1x  (a3a14ω3a4a12ω2a4a13ω3)ya14ω2  +a3z+a5  f, α=  a12ω3ω1ω2  a12ω2,  (6.22)
    λ1=  a12(a12ω2+a 13ω3)a14ω2, λ3=  a12ω2+a13ω3  a 14  .

    Case X:

    f=ea4t+a1x  a3ω3yω2+a3z+a5  +tanh(  ya8ω3ω2  +za8+a10)+tan(ta14 ya13ω3  ω2  +za13+a15)+a17. (6.23)

    Inserting (6.21) into (6.1) and (6.2), the solitary solution of Eq (1.8) will be gained as below form:

    Ψ10=v0+2a1ea4t+a1x  a3ω3yω2  +a3z+a5  f, α=a12ω3ω1ω2  a1  2ω2  , λ2=  λ3ω2  ω3  . (6.24)

    By choosing the specific amounts of parameters including

    a1=1,a3=1.5,a4=2,a5=1.5,a7=2,a8=2,a9=2.1,a10=1.5,a13=2,a13=2,a14=2.5,a15=3.2,a17=2,ω2=1.5,ω3=1.2,λ3=1.5,x=2,t=2,

    the graphical representation of rational solitary solution is offered in Figure 11 such as 3D chart and density chart.

    Figure 11.  The rational solitary wave solution (6.24) at (f1) z=2, (f2) z=0, and (f3) z=2.

    Case XI:

    f=ea4t+a1x+a2y  (a1  3a 14a4a13ω2)za14ω2  +a5  +tanh(  ta14a8  a13  +ya7+za8+a10)+tan(ta14+za13+a15)+a17. (6.25)

    Inserting (6.25) into (6.1) and (6.2), the solitary solution of Eq (1.8) can be stated as below case:

    Ψ11=v0+2a1ea4t+a1x+a2y(a13a14a4a13ω2)za14ω2  +a5  f, α=  a1  3a2+a1a3ω1+a2a3ω2+a3  2ω3  a13a3, (6.26)
    λ1=  a13a2a12a13a1  3a3a122+a2a3a12a13ω2+a2a3a132ω3+a2a3a13a14λ3  a3a12a1a14  +a32a122ω2a32a12a13ω3a32a12a14λ3  a3a12a1a14  ,
    λ2=  a13(a12ω2+a13ω3+a14λ3)a12a14  .

    Case XII:

    f=ea4t+a1x  a3ω3yω2  +a3z+a5  +tanh(ta9+ya7  za7ω2  ω3  +a10)+tan(ta14 ya13ω3  ω2  +za13+a15)+a17. (6.27)

    Inserting (6.27) into (6.1) and (6.2), the solitary solution of Eq (1.8) will be gained as below form:

    Ψ12=v0+2a1ea4t+a1x  a3ω3yω2  +a3z+a5  f, α=a13a2+a1a3ω1+a2a3ω2+a32ω3  a13a3, (6.28)
    λ1=  a13a2a12a13a1  3a3a122+a2a3a12a13ω2+a2a3a132ω3+a2a3a13a14λ3  a3a12a1a14  +a32a122ω2a32a12a13ω3a32a12a14λ3  a3a12a1a14  ,
    λ2=  a13(a12ω2+a13ω3+a14λ3)a12a14  .

    In the current section, we will analyze the continuous modulational instability of the nonlinear generalized KP equation. In addition, the feasibility of the localized waves in the present system is certified by linear stability analysis. First, we search the perturbed solution for the giving Eq (1.8) of the form

    Ψ(x,y,z,t)=ζ+δ Θ, (7.1)

    where Θ=Θ(x,y,z,t) and ζ is a steady state solution. Inserting (7.1) into Eq (1.8), become

    δ  4x3yΘ+αδ  4x3zΘ+3δ2(  2x2  Θ) yΘ+3δ2(xΘ)  2xyΘ+3αδ2(  2x2  Θ)  zΘ+ (7.2)
    3αδ2(  xΘ) 2xzΘ+λ1δ  2txΘ+λ2δ2tyΘ+λ3δ 2tzΘ+ω1δ2xzΘ+ω2δ2yzΘ+ω3δ 2z2  Θ=0,

    by linerization Eq (7.2), one gets

    δ  4x3yΘ+αδ  4x3zΘ+λ1δ  2txΘ+λ2δ  2tyΘ+λ3δ  2tzΘ+ω1δ  2xzΘ+ω2δ  2yzΘ+ω3δ  2z2  Θ=0. (7.3)

    Theorem 7.1. Assume that the solution of Eq (7.3) has the following case as

    Θ(x,y,z,t)=ρ1 ei(Mx+Ny+Pz+Bt), (7.4)

    in which M,N,P are the normalized wave numbers, by plugging (7.4) into Eq (7.3), separation the coefficients of ei(Mx+Ny+Pz+Wt) one gets

    B(M,N,P)=  M3Pα+M3NMPω1NPω2P2ω3  Mλ1+Nλ2+Pλ3  . (7.5)

    Proof. By putting (7.4) into (7.3), becomes

    δ  4x3y¯Θ+αδ  4x3z¯Θ+λ1δ  2tx¯Θ+λ2δ  2ty¯Φ+λ3δ  2tz¯Θ+ω1δ  2xz¯Θ+ω2δ  2yz¯Θ+ω3δ  2z2  ¯Θ (7.6)
    =ei(Mx+Ny+Pz+Bt)δρ1(M3Pα+M3NMPω1MBλ1NPω2NBλ2P2ω3PBλ3),

    in which ¯Θ=Θ(x,y,z,t). By solving and simplifying we can determine the function of B(M,N,P) as the following

    B(M,N,P)=  M3Pα+M3NMPω1NPω2P2ω3  Mλ1+Nλ2+Pλ3  . (7.7)

    Accordingly, the considered solution was obtained. Thereupon the proof is perfect.

    It is easy to notice that modulation stability occurs when Mλ1+Nλ2+Pλ30. So, the modulation stability achieve spectrum Υ(B) will be as below form:

    Υ(B)=  M3Pα+M3NMPω1NPω2P2ω3  Mλ1+Nλ2+Pλ3  . (7.8)

    In Figures 12-14 can be discovered that while the sign of B(M,N,P) is positive for all quantity of M. Furthermore, in Figure 12 can be observed if the B(M,N,P) is positive or negative for some quantities of M. Finally, in Figure 13 and 14 can be perceived that while the sign of B(M,N,P) is positive for all quantity of M.

    Figure 12.  The graphic representation Υ(B) for the wave number M via considering the diverse quantities λ1=1,λ2=1.5,λ3=2,ω1=2.2,ω2=2,ω3=3,α=0.5.
    Figure 13.  The graphic representation Υ(B) for the wave number M via considering the diverse quantities λ1=1,λ2=1.5,λ3=2,ω1=2.2,ω2=2,ω3=3,α=0.5.
    Figure 14.  The graphic representation Υ(B) for the wave number M via considering the diverse quantities λ1=1,λ2=1.5,λ3=2,ω1=2.2,ω2=2,ω3=3,α=0.5.

    Via employing the wave alteration ξ=k(x+ay+bzct) in Eq (1.8) once can gain to the below ODE as

    k2(αb+a)Ψ+6k(αb+a)ΨΨ+(abω2acλ2+b2ω3bcλ3+bω1cλ1)Ψ=0, (8.1)

    in which Ψ=Ψ(ξ) and Ψ=dΨdξ. According to the SIVP [16,17] and by multiplying Eq (8.1) with Ψ and integrating once respect to ξ, the following stationary integral will be arises

    J=0[A1(ΨΨ12(Ψ2)+13A2(Ψ3+12A3(Ψ2]dξ, (8.2)

    in which

    A1=k2(αb+a),   A2=6k(αb+a),   A3=abω2acλ2+b2ω3bcλ3+bω1cλ1.

    We utilize the solitary wave function as the following

    u(ξ)=δ sech(μξ). (8.3)

    Hence, the stationary integral transforms to

    J=130k2δ2μ21αbk2μ221ak2μ28αbδkμ8aδkμ+5abω25acλ2+5b2ω35bcλ 3+5bω15cλ1). (8.4)

    Based on the SIVP and using derivative J respect to A and B, one get

    JA=115k2δμ21αbk2μ221ak2μ28αbδkμ8aδkμ+5abω25acλ2+5b2ω35bcλ3+5bω15cλ1)+130k2δ2μ(8αbkμ8akμ)=0, (8.5)

    and

    JB=130k2δ221αbk2μ221a k2μ28αbδkμ8aδkμ+5abω25acλ2+5b2ω35bcλ3+5bω15cλ1)+130k2δ2μ(42αbk2μ42ak2μ8αbδk8aδk)=0. (8.6)

    Solve the Eqs (8.5) and (8.6), become

    δ=±212  abω2acλ2+b2ω3bcλ3+bω1cλ1  (21αb+21a)(abω2acλ2+b2ω3bcλ3+bω1cλ1),    (8.7)
    μ=±121  (21αb+21a)(abω2acλ2+b2ω3bcλ3+bω1cλ1)(αb+a)k.

    The condition can be obtained as below

    (αb+a)(abω2acλ2+b2ω3bcλ3+bω1cλ1)<0. (8.8)

    Finally, the solitary solution received by utilizing of SIVP can be reached as

    Ψ(x,y,z,t)=δ sech[kμ(x+ay+bzct)]. (8.9)

    We utilize the bright wave function as the following

    u(ξ)=δ sech2(μξ). (8.10)

    Hence, the stationary integral transforms to

    J=  2δ2μ(120αbk2μ2+120ak2μ2+35αbδkμ+35aδkμ14abω2  105+14acλ214b2ω3+14bcλ314bω1+14cλ1)105. (8.11)

    Based on the SIVP and using derivative J respect to A and B, one get

    JA=  4δμ(120αbk2μ2+120ak2μ2+35αbδkμ+35aδkμ14abω2+14acλ2  105+14b2ω3+14bcλ314bω1+14cλ1)2δ2μ(35αbkμ+35akμ)105=0. (8.12)

    and

    JB=  2δ2(120αbk2μ2+120a k2μ2+35αbδkμ+35aδkμ14abω2+14acλ214b2ω3  105++14bcλ314bω1+14cλ1)2δ2μ(240αbk2μ+240ak2μ+35αbδk+35aδk)105=0. (8.13)

    Solve the Eqs (8.12) and (8.13), become

    δ=±485  abω2acλ2+b2ω3bcλ3+bω1cλ1  (21αb+21a)(abω2acλ2+b2ω3bcλ3+bω1cλ1),    (8.14)
    μ=±130  (21αb+21a)(abω2acλ2+b2ω3bcλ3+bω1cλ1)(αb+a)k.

    The condition of definition of the above relations can be expressed as

    (αb+a)(abω2acλ2+b2ω3bcλ3+bω1cλ1)<0. (8.15)

    Finally, the solitary solution gained by utilizing of SIVP will be as

    Ψ(x,y,z,t)=δ sech2[kμ(x+ay+bzct)]. (8.16)

    Assume the dark soliton wave solution be as the below case as

    u(ξ)=δ tanh2(μξ). (8.17)

    Hence, the stationary integral transforms to

    J=  2δ2μ(120αbk2μ2120ak2μ2+35αbδkμ+35aδkμ+14abω2  105+14acλ2+14b2ω314bcλ3+14bω114cλ1)105. (8.18)

    Based on the SIVP and using derivative J respect to A and B, one get

    Jδ=  4δμ(120αbk2μ2120ak2μ2+35αbδkμ+35aδkμ+14abω214acλ2  105++14b2ω314bcλ3+14bω114cλ1)+2δ2μ(35αbkμ+35akμ)105=0, (8.19)

    and

    Jμ=  2δ2(120αbk2μ2120a k2μ2+35αbδkμ+35aδkμ+14abω214acλ2+105+14b2ω314bcλ3+14bω114cλ1)+2δ2μ(240αbk2μ240ak2μ+35αbδk+35aδk)105=0. (8.20)

    Solve the Eqs (8.19) and (8.20), one get

    δ=485  abω2acλ2+b2ω3bcλ3+bω1cλ1  (21αb+21a)(abω2acλ2+b2ω3bcλ3+bω1cλ1),    (8.21)
    μ=±130  (21αb+21a)(abω2acλ2+b2ω3bcλ3+bω1cλ1)(αb+a)k.

    The condition of definition of the above relations can be presented the following form

    (αb+a)(abω2acλ2+b2ω3bcλ3+bω1cλ1)<0. (8.22)

    Finally, the dark solution acquired by utilizing of SIVP will be as

    Ψ(x,y,z,t)=δ tanh2[kμ(x+ay+bzct)]. (8.23)

    Let the singular soliton wave solution be as the below case as

    u(ξ)=δ csch2(μξ). (8.24)

    Then, the stationary integral transforms to

    J=  2δ2μ(120αbk2μ2120ak2μ270αbδkμ70aδkμ+14abω2  105+14acλ2+14b2ω314bcλ3+14bω114cλ1)105. (8.25)

    Based on the SIVP and using derivative J respect to A and B, become

    Jδ=  4δμ(120αbk2μ2120ak2μ270αbδkμ70aδkμ+14abω214acλ2105++14b2ω314bcλ3+14bω114cλ1)2δ2μ(70αbkμ70akμ)105=0 (8.26)

    and

    Jμ=  2δ2(120αbk2μ2120ak2μ270αbδkμ70aδkμ+14abω214acλ2+14b2ω3  105+14bcλ3+14bω114cλ1)2δ2μ(240αbk2μ240ak2μ70αbδk70aδk)105=0. (8.27)

    Solve the Eqs (8.26) and (8.27), one get

    δ=±245  abω2acλ2+b2ω3bcλ3+bω1cλ1  (21αb+21a)(abω2acλ2+b2ω3bcλ3+bω1cλ1),    (8.28)
    μ=±130  (21αb+21a)(abω2acλ2+b2ω3bcλ3+bω1cλ1)(αb+a)k.

    The condition of definition of the above relations can be presented as the below form as

    (αb+a)(abω2acλ2+b2ω3bcλ3+bω1cλ1)<0. (8.29)

    Finally, the singular solution acquired by utilizing of SIVP will be as

    Ψ(x,y,z,t)=δ csch2[kμ(x+ay+bzct)]. (8.30)

    In this article, the MEFM employed for searching the MSSs for the gKP equation, which contains 1-wave, 2-wave, and 3-wave solutions. The periodic wave, cross-kink, and solitary wave solutions have been obtained. In continuing, the modulation instability applied to discuss the stability of earned solutions. It is quite visible that these novel schemes have plenty of family solutions containing rational exponential, hyperbolic, and periodic functions with selecting particular parameters. Also, the semi-inverse variational principle will be used for the gKP equation. Four major cases containing the solitary, bright, dark and singular wave solutions were studied from four different ansatzes.

    By means of symbolic computation, these analytical solutions and corresponding rogue waves are obtained. Via various curve plots, density plot and three-dimensional plots, dynamical characteristics of these rouge waves are exhibited. Because of the strong nonlinear characteristic of Hirota bilinear method, the test function constructed by the Hirota operator, which can be regarded as the test function constructed by considered model. The results are beneficial to the study of the plasma, optics, acoustics, fluid dynamics and fluid mechanics. All computations in this paper have been employed quickly with the help of the Maple 18. Moreover, the method applied in this paper provides an effective tool to obtain exact solutions of nonlinear system and can in common use for other NLEEs.

    This work is supported by the Education and scientific research project for young and middle-aged teachers of Fujian Province (No. JAT.190666-No.JAT200469)

    The authors declare that there is no conflict of interests regarding the publication of this paper.



    [1] Y. Zhang, E. Yao, R. Zhang, H. Xu, Analysis of elderly people's travel behaviours during the morning peak hours in the context of the free bus programme in Beijing, China, J. Transp. Geogr., 76 (2019), 191–199. https://doi.org/10.1016/j.jtrangeo.2019.04.002 doi: 10.1016/j.jtrangeo.2019.04.002
    [2] P. Thaithatkul, S. Chalermpong, W. Laosinwattana, H. Kato, Mobility, activities, and happiness in old age: case of the elderly in Bangkok, Case Stud. Transp. Policy, 10 (2022), 1462–1471. https://doi.org/10.1016/j.cstp.2022.05.010 doi: 10.1016/j.cstp.2022.05.010
    [3] A. Jones, A. Goodman, H. Roberts, R. Steinbach, J. Green, Entitlement to concessionary public transport and wellbeing: a qualitative study of young people and older citizens in London, UK, Social Sci. Med., 91 (2013), 202–209. https://doi.org/10.1016/j.socscimed.2012.11.040 doi: 10.1016/j.socscimed.2012.11.040
    [4] F. Shao, Y. Sui, X. Yu, R. Sun, Spatio-temporal travel patterns of elderly people–A comparative study based on buses usage in Qingdao, China, J. Transp. Geogr., 76 (2019), 178–190. https://doi.org/10.1016/j.jtrangeo.2019.04.001 doi: 10.1016/j.jtrangeo.2019.04.001
    [5] J. R. Hjorthol, L. Levin, A. Sirén, Mobility in different generations of older persons: the development of daily travel in different cohorts in Denmark, Norway and Sweden, J. Transp. Geogr., 18 (2010), 624–633. https://doi.org/10.1016/j.jtrangeo.2010.03.011 doi: 10.1016/j.jtrangeo.2010.03.011
    [6] J. Kim, D. J. Schmöcker, T. Nakamura, N. Uno, T. Iwamoto, Integrated impacts of public transport travel and travel satisfaction on quality of life of older people, Transp. Res. Part A: Policy Pract., 138 (2020), 15–27. https://doi.org/10.1016/j.tra.2020.04.019 doi: 10.1016/j.tra.2020.04.019
    [7] X. Dong, Addressing health and well-being of U.S. Chinese older adults through community-based participatory research: introduction to the PINE study, AIMS Med. Sci., 2 (2015), 261–270. https://doi.org/10.3934/medsci.2015.3.261 doi: 10.3934/medsci.2015.3.261
    [8] C. Dillon, F. E. Taragano, Activity and lifestyle factors in the elderly: their relationship with degenerative diseases and depression, AIMS Med. Sci., 3 (2016), 213–216. https://doi.org/10.3934/medsci.2016.2.213 doi: 10.3934/medsci.2016.2.213
    [9] S. Zhang, P. Jing, D. Yuan, C. Yang, On parents' choice of the school travel mode during the COVID-19 pandemic, Math. Biosci. Eng., 19 (2022), 9412−9436. https://doi.org/10.3934/mbe.2022438 doi: 10.3934/mbe.2022438
    [10] X. Hu, J. Wang, L. Wang, Understanding the travel behavior of elderly people in the developing country: a case study of Changchun, China, Procedia - Social Behav. Sci., 96 (2013), 873–880. https://doi.org/10.1016/j.sbspro.2013.08.099 doi: 10.1016/j.sbspro.2013.08.099
    [11] J. Mak, L. Carlile, S. Dai, Impact of population aging on Japanese international travel to 2025, J. Travel Res., 44 (2005), 151–162. https://doi.org/10.1177/0047287505278993 doi: 10.1177/0047287505278993
    [12] M. Wei, T. Liu, B. Sun, Optimal routing design of feeder transit with stop selection using aggregated cell phone data and open source GIS tool, IEEE Trans. Intell. Transp. Syst., 22 (2021), 2452–2463. https://doi.org/10.1109/TITS.2020.3042014 doi: 10.1109/TITS.2020.3042014
    [13] M. Wei, B. Jing, J. Yin, Y. Zang, A green demand-responsive airport shuttle service problem with time-varying speeds, J. Adv. Transp., 2020 (2020), 1–13. https://doi.org/10.1155/2020/9853164 doi: 10.1155/2020/9853164
    [14] M. Wei, T. Liu, B. Sun, B. Jing, Optimal integrated model for feeder transit route design and frequency-setting problem with stop selection, J. Adv. Transp., 2020 (2020), 1–12. https://doi.org/10.1155/2020/6517248 doi: 10.1155/2020/6517248
    [15] Y. Hou, Polycentric urban form and non-work travel in Singapore: a focus on seniors, Transp. Res. D Transp. Environ., 73 (2019), 245–275. https://doi.org/10.1016/j.trd.2019.07.003 doi: 10.1016/j.trd.2019.07.003
    [16] J. Tang, J. Liang, F. Liu, J. Hao, Y. Wang, Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network, Transp. Res. Part C: Emerging Technol., 124 (2019), 1–18. https://doi.org/10.1016/j.trc.2020.102951 doi: 10.1016/j.trc.2020.102951
    [17] S. Halyal, R. H. Mulangi, M. M.Harsha, Forecasting public transit passenger demand: With neural networks using APC data, Case Stud. Transp. Policy, 10 (2022), 965–975. https://doi.org/10.1016/j.cstp.2022.03.011 doi: 10.1016/j.cstp.2022.03.011
    [18] Y. Feng, J. Hao, X. Sun, J. Li, Forecasting short-term tourism demand with a decomposition-ensemble strategy, Procedia Comput. Sci., 199 (2022), 879–884. https://doi.org/10.1016/j.procs.2022.01.110 doi: 10.1016/j.procs.2022.01.110
    [19] Y. Bai, Z. Sun, B. Zeng, J. Deng, C. Li, A multi-pattern deep fusion model for short-term bus passenger flow forecasting, Appl. Soft Comput., 58 (2017), 669–680. https://doi.org/10.1016/j.asoc.2017.05.011 doi: 10.1016/j.asoc.2017.05.011
    [20] G. Lin, A. Lin, D. Gu, Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient, Inf. Sci., 608 (2022), 517–531. https://doi.org/10.1016/j.ins.2022.06.090 doi: 10.1016/j.ins.2022.06.090
    [21] O. Giraka, K. V. Selvaraj, Short-term prediction of intersection turning volume using seasonal ARIMA model, Transp. Lett., 2019 (2019), 483–490. https://doi.org/10.1080/19427867.2019.1645476 doi: 10.1080/19427867.2019.1645476
    [22] A. Emami, M. Sarvi, S. A. Bagloee, Short-term traffic flow prediction based on faded memory Kalman Filter fusing data from connected vehicles and Bluetooth sensors, Simul. Modell. Pract. Theory, 102 (2020), 1–17. https://doi.org/10.1016/j.simpat.2019.102025 doi: 10.1016/j.simpat.2019.102025
    [23] V. S. Kumar, Traffic flow prediction using Kalman filtering technique, Procedia Eng., 187 (2017), 582–587. https://doi.org/10.1016/j.proeng.2017.04.417 doi: 10.1016/j.proeng.2017.04.417
    [24] Z. Shi, N. Zhang, P. M. Schonfeld, J. Zhang, Short-term metro passenger flow forecasting using ensemble-chaos support vector regression, Transp. A: Transp. Sci., 16 (2019), 194–212. https://doi.org/10.1080/23249935.2019.1692956 doi: 10.1080/23249935.2019.1692956
    [25] Y. Sun, B. Leng, W. Guan, A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system, Neurocomputing, 166 (2015), 109–121. https://doi.org/10.1016/j.neucom.2015.03.085 doi: 10.1016/j.neucom.2015.03.085
    [26] Y. Liu, Z. Liu, R. Jia, DeepPF: a deep learning based architecture for metro passenger flow prediction, Transp. Res. Part C: Emerging Technol., 101 (2019), 18–34, https://doi.org/10.1016/j.trc.2019.01.027 doi: 10.1016/j.trc.2019.01.027
    [27] C. W. Tsai, C. H. Hsia, S. J. Yang, S. J. Liu, Z. Y. Fang, Optimizing hyperparameters of deep learning in predicting bus passengers based on simulated annealing, Appl. Soft Comput., 88 (2020), 18–34. https://doi.org/10.1016/j.asoc.2020.106068 doi: 10.1016/j.asoc.2020.106068
    [28] B. Sun, T. Sun, P. Jiao, Spatio-temporal segmented traffic flow prediction with ANPRS data based on improved XGBoost, J. Adv. Transp., 2021 (2021), 1–24. https://doi.org/10.1155/2021/5559562 doi: 10.1155/2021/5559562
    [29] J. J. Buckley, Y. Hayashi, Fuzzy neural networks: a survey, Fuzzy Sets Syst., 66 (1994), 1–13. https://doi.org/10.1016/0165-0114(94)90297-6 doi: 10.1016/0165-0114(94)90297-6
    [30] H. Peng, H. Wang, B. Du, M. Z. A. Bhuiyan, H. Ma, J. Liu, et al., Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting, Inf. Sci., 521 (2020), 277–290. https://doi.org/10.1016/j.ins.2020.01.043 doi: 10.1016/j.ins.2020.01.043
    [31] X. Yang, Q. Xue, X. Yang, H. Yin, Y. Qua, X. Li, et al., A novel prediction model for the inbound passenger flow of urban rail transit, Inf. Sci., 566 (2021), 347–363. https://doi.org/10.1016/j.ins.2021.02.036 doi: 10.1016/j.ins.2021.02.036
    [32] X. Fu, Y. Zuo, J. Wu, Y. Yuan, S. Wang, Short-term prediction of metro passenger flow with multi-source data: a neural network model fusing spatial and temporal features, Tunnelling Underground Space Technol., 124 (2022), 1–15. https://doi.org/10.1016/j.tust.2022.104486 doi: 10.1016/j.tust.2022.104486
    [33] L. Liu, C. R. Chen, A novel passenger flow prediction model using deep learning methods, Transp. Res. Part C: Emerging Technol., 84 (2017), 74–91. https://doi.org/10.1016/j.trc.2017.08.001 doi: 10.1016/j.trc.2017.08.001
    [34] D. Luo, D. Zhao, Q. Ke, X. You, L. Liu, H. Ma, Spatiotemporal hashing multigraph convolutional network for service-level passenger flow forecasting in bus transit systems, IEEE Internet Things J., 9 (2021), 6803–6815. https://doi.org/10.1109/JIOT.2021.3116241 doi: 10.1109/JIOT.2021.3116241
    [35] Y. Gao, Z. Guo, Y. Long, Z. Cui, X. Li, Passengers' travel behavior before and after the adjustment of regular bus collinear sections: a case study in the incipient phase of metro operation in Xiamen, Travel Behav. Soc., 26 (2022), 221–230. https://doi.org/10.1016/j.tbs.2021.10.006 doi: 10.1016/j.tbs.2021.10.006
    [36] Y. Yang, M. Cao, L. Cheng, K. Zhai, X. Zhao, J. D. Vos, Exploring the relationship between the COVID-19 pandemic and changes in travel behaviour: a qualitative study, Transp. Res. Interdiscip. Perspect., 11 (2021), 1–4. https://doi.org/10.1016/j.trip.2021.100450 doi: 10.1016/j.trip.2021.100450
    [37] S. Hu, Q. Liang, H. Qian, J. Weng, W. Zhou, Frequent-pattern growth algorithm based association rule mining method of public transport travel stability, Int. J. Sustainable Transp., 15 (2021), 879–892. https://doi.org/10.1080/15568318.2020.1827318 doi: 10.1080/15568318.2020.1827318
    [38] Z. Ma, J. Xing, M. Mesbah, L. Ferreira, Predicting short-term bus passenger demand using a pattern hybrid approach, Transp. Res. Part C: Emerging Technol., 39 (2014), 148–163. https://doi.org/10.1016/j.trc.2013.12.008 doi: 10.1016/j.trc.2013.12.008
    [39] N. Oort, T. Brands, E. Romph, Short-term prediction of ridership on public transport with smart card data, Transp. Res. Rec., 2535 (2015), 105–111. https://doi.org/10.3141/2535-12 doi: 10.3141/2535-12
    [40] I. Okutani, Y. J. Stephanedes, Dynamic prediction of traffic volume through Kalman filtering theory, Transp. Res. Part B: Methodol., 18 (1984), 1–11. https://doi.org/10.1016/0191-2615(84)90002-X doi: 10.1016/0191-2615(84)90002-X
    [41] W. Min, L. Wynter, Real-time road traffic prediction with spatio-temporal correlations, Transp. Res. Part C: Emerging Technol., 19 (2011), 606–616. https://doi.org/10.1016/j.trc.2010.10.002 doi: 10.1016/j.trc.2010.10.002
    [42] Y. K. Chan, S. T. Dillon, J. Singh, E. Chang, Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg–Marquardt algorithm, IEEE Trans. Intell. Transp. Syst., 13 (2012), 644–654. https://doi.org/10.1109/TITS.2011.2174051 doi: 10.1109/TITS.2011.2174051
    [43] R. Xue, J. D. Sun, S. Chen, Short-term bus passenger demand prediction based on time series model and interactive multiple model approach, Discrete Dyn. Nat. Soc., 2015 (2015), 1–11. https://doi.org/10.1155/2015/682390 doi: 10.1155/2015/682390
    [44] F. Toqué, M. Khouadjia, E. Come, M. Trepanier, L. Oukhellou, Short & long term forecasting of multimodal transport passenger flows with machine learning methods, in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017 (2017), 560–566. https://doi.org/10.1109/ITSC.2017.8317939
    [45] C. Li, X. Wang, Z. Cheng, Y. Bai, Forecasting bus passenger flows by using a clustering-based support vector regression approach, IEEE Access, 8 (2020), 19717–19725. https://doi.org/10.1109/ACCESS.2020.2967867 doi: 10.1109/ACCESS.2020.2967867
    [46] F. Jiao, L. Huang, Z. Gao, Multi-step time series forecasting of bus passenger flow with deep learning methods, in Liss 2020, 2021 (2021), 539–553. https://doi.org/10.1007/978-981-33-4359-7_38
    [47] W. Lv, Y. Lv, Q. Ouyang, Y. Ren, A bus passenger flow prediction model fused with point-of-interest data based on extreme gradient boosting, Appl. Sci., 12 (2022), 1–14. https://doi.org/10.3390/app12030940 doi: 10.3390/app12030940
    [48] Z. Gan, T. Feng, Y. Wu, M. Yang, H. Timmermans, Station-based average travel distance and its relationship with urban form and land use: an analysis of smart card data in Nanjing City, China, Transp. Policy, 79 (2019), 137–154. https://doi.org/10.1016/j.tranpol.2019.05.003 doi: 10.1016/j.tranpol.2019.05.003
    [49] J. Yong, L. Zheng, X. Mao, X. Tang, A. Gao, W. Liu, Mining metro commuting mobility patterns using massive smart card data, Physica A, 584 (2021), 1–16. https://doi.org/10.1016/j.physa.2021.126351 doi: 10.1016/j.physa.2021.126351
    [50] O. Egu, P. Bonnel, Investigating day-to-day variability of transit usage on a multimonth scale with smart card data. A case study in Lyon, Travel Behav. Soc., 19 (2020), 112–123. https://doi.org/10.1016/j.tbs.2019.12.003 doi: 10.1016/j.tbs.2019.12.003
    [51] E. F. Grubbs, Sample criteria for testing outlying observations, Ann. Math. Stat., 21 (1950), 27–58. https://www.jstor.org/stable/2236553
    [52] C. S. Möller-Levet, F. Klawonn, H. K. Cho, O. Wolkenhauer, Fuzzy clustering of short time-series and unevenly distributed sampling points, Adv. Intell. Data Anal., 2810 (2003), 330–340 https://doi.org/10.1007/978-3-540-45231-7_31 doi: 10.1007/978-3-540-45231-7_31
    [53] J. R. Hodrick, C. E. Prescott, Postwar US business cycles: an empirical investigation, J. Money Credit Banking, 29 (1997), 1–16. https://doi.org/10.2307/2953682 doi: 10.2307/2953682
    [54] H. Zhai, L. Cui, Y. Nie, X. Xu, W. Zhang, A comprehensive comparative analysis of the basic theory of the short term bus passenger flow prediction, Symmetry, 10 (2018), 1–23. https://doi.org/10.3390/sym10090369 doi: 10.3390/sym10090369
    [55] L. hang, Q. Liu, W. Yang, N. Wei, D. Dong, An improved k-nearest neighbor model for short-term traffic flow prediction, Procedia - Social Behav. Sci., 96 (2013), 653–662. https://doi.org/10.1016/j.sbspro.2013.08.076 doi: 10.1016/j.sbspro.2013.08.076
    [56] G. Cheng, S. Zhao, J. Li, The effects of latent attitudinal variables and sociodemographic differences on travel behavior in two small, underdeveloped cities in China, Sustainability, 11 (2019), 1–17. https://doi.org/10.3390/su11051306 doi: 10.3390/su11051306
    [57] G. Cheng, S. Jiang, T. Zhang, Fuzzy multidimensional assessment approach of travel deprivation in small underdeveloped cities: case study of Lhasa, China, J. Adv. Transp., 2021 (2021), 1–12. https://doi.org/10.1155/2021/8851449 doi: 10.1155/2021/8851449
    [58] G. Cheng, L. Guo, T. Zhang, Spatial equity assessment of bus travel behavior for pilgrimage: evidence from Lhasa, Tibet, China, Sustainability, 14 (2022), 1–15. https://doi.org/10.3390/su141710486 doi: 10.3390/su141710486
    [59] S. Liu, T. Yamamoto, E. Yao, T. Nakamura, Examining public transport usage by older adults with smart card data: a longitudinal study in Japan, J. Transp. Geogr., 93 (2021), 1–12. https://doi.org/10.1016/j.jtrangeo.2021.103046 doi: 10.1016/j.jtrangeo.2021.103046
    [60] A. Barnett, E. Cerin, C. M. Cheung, H. C. Sit, J. D. Macfarlane, M. W. Chan, Reliability and validity of the IPAQ-L in a sample of Hong Kong urban older adults: does neighborhood of residence matter, J Aging Phys. Act., 20 (2012), 402–420. https://doi.org/10.1123/japa.20.4.402 doi: 10.1123/japa.20.4.402
    [61] H. Wang, L. Fu, Y. Zhou, H. Li, Modelling of the fuel consumption for passenger cars regarding driving characteristics, Transp. Res. Part D: Transp. Environ., 13 (2008), 479–482. https://doi.org/10.1016/j.trd.2008.09.002 doi: 10.1016/j.trd.2008.09.002
    [62] R. Mackett, Improving accessibility for older people–Investing in a valuable asset, J. Transp. Health, 2 (2015), 5–13. https://doi.org/10.1016/j.jth.2014.10.004 doi: 10.1016/j.jth.2014.10.004
  • This article has been cited by:

    1. Chaudry Masood Khalique, Oke Davies Adeyemo, Kentse Maefo, Symmetry solutions and conservation laws of a new generalized 2D Bogoyavlensky-Konopelchenko equation of plasma physics, 2022, 7, 2473-6988, 9767, 10.3934/math.2022544
    2. K. Hosseini, D. Baleanu, S. Rezapour, S. Salahshour, M. Mirzazadeh, M. Samavat, Multi-complexiton and positive multi-complexiton structures to a generalized B-type Kadomtsev−Petviashvili equation, 2022, 24680133, 10.1016/j.joes.2022.06.020
    3. Abdul-Majid Wazwaz, Naisa S. Alatawi, Wedad Albalawi, S. A. El-Tantawy, Painlevé analysis for a new (3 +1 )-dimensional KP equation: Multiple-soliton and lump solutions, 2022, 140, 0295-5075, 52002, 10.1209/0295-5075/aca49f
    4. Yan Zhu, Chuyu Huang, Junjie Li, Runfa Zhang, Lump solitions, fractal soliton solutions, superposed periodic wave solutions and bright-dark soliton solutions of the generalized (3+1)-dimensional KP equation via BNNM, 2024, 112, 0924-090X, 17345, 10.1007/s11071-024-09911-2
    5. Adisie Fenta Agmas, Fasika Wondimu Gelu, Meselech Chima Fino, A robust, exponentially fitted higher-order numerical method for a two-parameter singularly perturbed boundary value problem, 2025, 10, 2297-4687, 10.3389/fams.2024.1501271
  • Reader Comments
  • © 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2687) PDF downloads(183) Cited by(4)

Figures and Tables

Figures(6)  /  Tables(5)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog