• Journal of Geo-information Science
  • Vol. 22, Issue 4, 792 (2020)
Pengjun ZHAO* and Jie WAN
Author Affiliations
  • The Centre for Urban Planning and Transport Studies, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
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    DOI: 10.12082/dqxxkx.2020.190628 Cite this Article
    Pengjun ZHAO, Jie WAN. The Development and Innovation of the Core Algorithm of the Integrated Model of Urban Transport and Land Use[J]. Journal of Geo-information Science, 2020, 22(4): 792 Copy Citation Text show less

    Abstract

    The coordinated development of urban transport and land use is of great practical significance to optimize urban spatial structure, reduce traffic congestion, and improve transport level. The integration of land use and transport systems is important for smart growth and sustainable development of cities. The integrated model of urban transport and land use is a key scientific support to analyze and simulate the interaction between urban transport and land use. Over the years, scholars in different countries have developed operational models that can be used for urban spatial policy formulation. However, the core algorithm of these models still need to be further improved. In this paper, the theoretical characteristics of existing mainstream models were analyzed. Based on the theoretical basis and the core algorithms, the advantages and disadvantages of six models were discussed, including spatial interaction model, urban economics and mathematical planning model, spatial input-output model, discrete choice model, micro-simulation model, and cellular automata model. A new comprehensive equilibrium model was proposed to overcome the shortcomings of the existing models. On the one hand, endogenous variable processing and dynamic feedback of the key algorithms need to be improved. On the other hand, the existing models do not fully consider the impacts of exogenous variables such as technological innovation, urban management, and urban planning policies. Therefore, new thinking was put forward for the core algorithm incorporating three key variables: Accessibility, land use characteristics, and travel cost. The equilibrium model adopts land use intensity and diversity to represent land use characteristics, and uses the improved algorithm of accessibility that takes into account the repulsive force caused by housing price and the opportunity scale of land use characteristics. In the generalized travel cost, attributes at the individual level and characteristics at the urban environment level are considered comprehensively. New algorithms were also proposed for the three modules of the integrated model, which include the residence and employment location decision module that considers incremental discrete selection process, the land supply and real estate development module that subdivides real estate types and dynamically feeds back land development results to urban land use evolution, and the comprehensive transport model that adopts improved impedance function, dynamic travel cost, and car ownership. It has important theoretical value for quantitative simulation of urban spatial evolution, prediction of trip distribution, and evaluation of urban management and planning policies. At the same time, it is of practical significance to develop the integrated model of urban transport and land use in China, so as to optimize the distribution of population and trip demand, and ease urban traffic congestion.
    Fij=kMiMjf(cij)(1)

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    Ej,tR=λRPi,t-1Ai,t-1RWj,t-1RfRcij,t+1-λREj,t-1R(2)

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    Ni,tn=aRnEj,tRWi,tnfn(cij,t)Wi,tnfn(cij,t)(3)

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    minFx,s.t.gjxbj(j=1,2,,J)(4)

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    Xijmn=Yimamnmin(Kjn+Cijn+ejn)(5)

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    Xijmn=Yimamnexp(-βnVijn)exp(-βnVijn)(6)

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    Pk=exp[VkXk]exp[VkXk](7)

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    FARi,vt=FAi,vtLAi,vt(8)

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    LUMit=-v=1Npi,vtlnpi,vtlnN(9)

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    Ai=Djdij(10)

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    Ai=DjdijPitPt¯γ(16)

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    Djt=μvtFAj,vt(17)

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    fcijk,mt=Tcij,k,mt+Tex,ij,k,mtVOTit+Mcij,k,mt+uij,k,mt(18)

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    Ht+1i=Ht+1HtiFAt+1iexp(Vt+1i)[HtiFAt+1iexpVt+1i](19)

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    Vt+1i=β0+β1Pti+β2Ati+β3RTti+β4Eti+β5LUMti(20)

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    Ci,vt=(Pi,vt-Pi,vt-n)×(r+δ)(21)

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    Di,vt=H(γ0+γ1Rit+γ2Eit+γ3Ait+γ4LUMit+γ5RTit+γ6yit+γ7Ci,vt)(22)

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    Qi,vt=Di,vt=H[γ0+γ1Rit+γ2Eit+γ3Ait+γ4LUMit+γ5RTit+γ6yit+γ7Pi,vt-Pi,vt-n×r+δ](23)

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    Pi,vt=1γ7×r+δ· Qi,vtH-γ0+γ1Rit+γ2Eit+γ3Ait+γ4LUMit+γ5RTit+γ6yit+Pi,vt-n(24)

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    ps,i,vt=exp(Ui,vt)exp(Ui,vt)(25)

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    LAi,vt=ps,i,vtLSt(26)

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    f(Cij)t=α(cij)σe-τcij(27)

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    cij=tij+tcij(28)

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    PCo=exp(UCo)1+exp(UCo)(29)

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    UCo=α0+α1I+α2C+α3PR+α4Dc(30)

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    Pengjun ZHAO, Jie WAN. The Development and Innovation of the Core Algorithm of the Integrated Model of Urban Transport and Land Use[J]. Journal of Geo-information Science, 2020, 22(4): 792
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