A conceptual LUTI model based on neural networks

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    2 Downloads (Pure)

    Abstract

    This paper deals with Land-use-Transport-Interaction (LUTI) and presents a conceptual model of a data driven LUTI modelling tool based on neural networks. A starting point is the general opinion of experts on the state of the art LUTI models; currently used land use transportation models are too aggregate in substance. Therefore to enhance the interaction between transport and land-use, researchers propose the refinement of the models; internalising more comprehensive relationships. However, the lack of a good theoretical framework impedes the development and consequently the use of these models on a large scale. This fact has fuelled questions: (i) does transport planning need these comprehensive descriptions of land use; (ii) is it necessary to disaggregate and refine the models further in order to be able to do consistent transport planning; (iii) which land use characteristics should at least be internalised; and (iv) which modelling method is suitable for implementation.
    Based on literature, the hypothesis is that the first question can be answered by ’no’ and no further refinements are needed. Literature shows that the main drivers for land-use changes are the location choice of both households and firms. Therefore only these two building blocks are used in relation with the transport component. Artificial neural networks (ANNs) will be used as the modelling technique. ANNs are data driven techniques that find relationships in
    data during an auto calibration process. Therefore ANNs can work without having a sound theoretical framework. This characteristic offers possibilities for LUTI modelling that lacks a sound theoretical framework. The research leads to a conceptual model. A literature review shows that the individual building blocks of the conceptual LUTI model can be modelled using neural networks. However, an integration of the building blocks has not been established yet. Further research has to result in the actual implementation of the proposed model.
    Original languageEnglish
    Title of host publicationUrban Transport X
    Subtitle of host publicationUrban transport and the environment in the 21st century
    EditorsC.A Brebbia, L.C. Wadhwa
    Place of PublicationSouthampton
    PublisherWIT Press
    Pages193-202
    ISBN (Print)1-85312-716-7
    Publication statusPublished - 2004

    Publication series

    NameWIT Transactions on The Built Environment
    PublisherWIT Press
    Volume75
    ISSN (Print)1462-608X
    NameAdvances in transport
    PublisherWIT Press
    Volume16
    ISSN (Print)1462-608X

    Fingerprint

    land use
    artificial neural network
    modeling
    literature review
    land use change
    calibration

    Keywords

    • PGM
    • ADLIB-ART-166
    • Land use
    • Transport
    • LUTI
    • Neural network
    • Planning

    Cite this

    Tillema, F., & van Maarseveen, M. F. A. M. (2004). A conceptual LUTI model based on neural networks. In C. A. Brebbia, & L. C. Wadhwa (Eds.), Urban Transport X: Urban transport and the environment in the 21st century (pp. 193-202). (WIT Transactions on The Built Environment; Vol. 75), (Advances in transport; Vol. 16). Southampton: WIT Press.
    Tillema, F. ; van Maarseveen, M.F.A.M. / A conceptual LUTI model based on neural networks. Urban Transport X: Urban transport and the environment in the 21st century. editor / C.A Brebbia ; L.C. Wadhwa. Southampton : WIT Press, 2004. pp. 193-202 (WIT Transactions on The Built Environment). (Advances in transport).
    @inbook{a592855289634e16a5276f32a6d85cf1,
    title = "A conceptual LUTI model based on neural networks",
    abstract = "This paper deals with Land-use-Transport-Interaction (LUTI) and presents a conceptual model of a data driven LUTI modelling tool based on neural networks. A starting point is the general opinion of experts on the state of the art LUTI models; currently used land use transportation models are too aggregate in substance. Therefore to enhance the interaction between transport and land-use, researchers propose the refinement of the models; internalising more comprehensive relationships. However, the lack of a good theoretical framework impedes the development and consequently the use of these models on a large scale. This fact has fuelled questions: (i) does transport planning need these comprehensive descriptions of land use; (ii) is it necessary to disaggregate and refine the models further in order to be able to do consistent transport planning; (iii) which land use characteristics should at least be internalised; and (iv) which modelling method is suitable for implementation.Based on literature, the hypothesis is that the first question can be answered by ’no’ and no further refinements are needed. Literature shows that the main drivers for land-use changes are the location choice of both households and firms. Therefore only these two building blocks are used in relation with the transport component. Artificial neural networks (ANNs) will be used as the modelling technique. ANNs are data driven techniques that find relationships indata during an auto calibration process. Therefore ANNs can work without having a sound theoretical framework. This characteristic offers possibilities for LUTI modelling that lacks a sound theoretical framework. The research leads to a conceptual model. A literature review shows that the individual building blocks of the conceptual LUTI model can be modelled using neural networks. However, an integration of the building blocks has not been established yet. Further research has to result in the actual implementation of the proposed model.",
    keywords = "PGM, ADLIB-ART-166, Land use, Transport, LUTI, Neural network, Planning",
    author = "F. Tillema and {van Maarseveen}, M.F.A.M.",
    year = "2004",
    language = "English",
    isbn = "1-85312-716-7",
    series = "WIT Transactions on The Built Environment",
    publisher = "WIT Press",
    pages = "193--202",
    editor = "C.A Brebbia and L.C. Wadhwa",
    booktitle = "Urban Transport X",
    address = "United Kingdom",

    }

    Tillema, F & van Maarseveen, MFAM 2004, A conceptual LUTI model based on neural networks. in CA Brebbia & LC Wadhwa (eds), Urban Transport X: Urban transport and the environment in the 21st century. WIT Transactions on The Built Environment, vol. 75, Advances in transport, vol. 16, WIT Press, Southampton, pp. 193-202.

    A conceptual LUTI model based on neural networks. / Tillema, F.; van Maarseveen, M.F.A.M.

    Urban Transport X: Urban transport and the environment in the 21st century. ed. / C.A Brebbia; L.C. Wadhwa. Southampton : WIT Press, 2004. p. 193-202 (WIT Transactions on The Built Environment; Vol. 75), (Advances in transport; Vol. 16).

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    TY - CHAP

    T1 - A conceptual LUTI model based on neural networks

    AU - Tillema, F.

    AU - van Maarseveen, M.F.A.M.

    PY - 2004

    Y1 - 2004

    N2 - This paper deals with Land-use-Transport-Interaction (LUTI) and presents a conceptual model of a data driven LUTI modelling tool based on neural networks. A starting point is the general opinion of experts on the state of the art LUTI models; currently used land use transportation models are too aggregate in substance. Therefore to enhance the interaction between transport and land-use, researchers propose the refinement of the models; internalising more comprehensive relationships. However, the lack of a good theoretical framework impedes the development and consequently the use of these models on a large scale. This fact has fuelled questions: (i) does transport planning need these comprehensive descriptions of land use; (ii) is it necessary to disaggregate and refine the models further in order to be able to do consistent transport planning; (iii) which land use characteristics should at least be internalised; and (iv) which modelling method is suitable for implementation.Based on literature, the hypothesis is that the first question can be answered by ’no’ and no further refinements are needed. Literature shows that the main drivers for land-use changes are the location choice of both households and firms. Therefore only these two building blocks are used in relation with the transport component. Artificial neural networks (ANNs) will be used as the modelling technique. ANNs are data driven techniques that find relationships indata during an auto calibration process. Therefore ANNs can work without having a sound theoretical framework. This characteristic offers possibilities for LUTI modelling that lacks a sound theoretical framework. The research leads to a conceptual model. A literature review shows that the individual building blocks of the conceptual LUTI model can be modelled using neural networks. However, an integration of the building blocks has not been established yet. Further research has to result in the actual implementation of the proposed model.

    AB - This paper deals with Land-use-Transport-Interaction (LUTI) and presents a conceptual model of a data driven LUTI modelling tool based on neural networks. A starting point is the general opinion of experts on the state of the art LUTI models; currently used land use transportation models are too aggregate in substance. Therefore to enhance the interaction between transport and land-use, researchers propose the refinement of the models; internalising more comprehensive relationships. However, the lack of a good theoretical framework impedes the development and consequently the use of these models on a large scale. This fact has fuelled questions: (i) does transport planning need these comprehensive descriptions of land use; (ii) is it necessary to disaggregate and refine the models further in order to be able to do consistent transport planning; (iii) which land use characteristics should at least be internalised; and (iv) which modelling method is suitable for implementation.Based on literature, the hypothesis is that the first question can be answered by ’no’ and no further refinements are needed. Literature shows that the main drivers for land-use changes are the location choice of both households and firms. Therefore only these two building blocks are used in relation with the transport component. Artificial neural networks (ANNs) will be used as the modelling technique. ANNs are data driven techniques that find relationships indata during an auto calibration process. Therefore ANNs can work without having a sound theoretical framework. This characteristic offers possibilities for LUTI modelling that lacks a sound theoretical framework. The research leads to a conceptual model. A literature review shows that the individual building blocks of the conceptual LUTI model can be modelled using neural networks. However, an integration of the building blocks has not been established yet. Further research has to result in the actual implementation of the proposed model.

    KW - PGM

    KW - ADLIB-ART-166

    KW - Land use

    KW - Transport

    KW - LUTI

    KW - Neural network

    KW - Planning

    UR - https://ezproxy2.utwente.nl/login?url=https://webapps.itc.utwente.nl/library/2004/scie/vanmaarseveen_con.pdf

    M3 - Chapter

    SN - 1-85312-716-7

    T3 - WIT Transactions on The Built Environment

    SP - 193

    EP - 202

    BT - Urban Transport X

    A2 - Brebbia, C.A

    A2 - Wadhwa, L.C.

    PB - WIT Press

    CY - Southampton

    ER -

    Tillema F, van Maarseveen MFAM. A conceptual LUTI model based on neural networks. In Brebbia CA, Wadhwa LC, editors, Urban Transport X: Urban transport and the environment in the 21st century. Southampton: WIT Press. 2004. p. 193-202. (WIT Transactions on The Built Environment). (Advances in transport).