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    Trip distribution modelling using neural network

    237901_237901.pdf (1.279Mb)
    Access Status
    Open access
    Authors
    Rasouli, M.
    Nikraz, Hamid
    Date
    2013
    Type
    Conference Paper
    
    Metadata
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    Citation
    Rasouli, M. and Nikraz, H. 2013. Trip distribution modelling using neural network, in Proceedings of the Australasian Transport Research Forum (ATRF 2013), Oct 2-4 2013. Brisbane, Qld: Australasian Transport Research Forum.
    Source Title
    Australasian Transport Research Forum, ATRF 2013 - Proceedings
    School
    Department of Civil Engineering
    URI
    http://hdl.handle.net/20.500.11937/32141
    Collection
    • Curtin Research Publications
    Abstract

    Trip distribution is the second important stage in the 4-step travel demand forecasting. The purpose of the trip distribution forecasting is to estimates the trip linkages or interactions between traffic zones for trip makers. The problem of trip distribution is of non-linear nature and Neural Networks (NN) are well suited for addressing the non-linear problems. This fact supports the use of artificial neural networks for trip distribution problem. In this study a new approach based on the Generalised Regression Neural Network (GRNN) has been researched to estimate the distribution of the journey to work trips. The advantage of GRNN models among other feedforward or feedback neural network techniques is the simplicity and practicality of these models. As a case study the model was applied to the journey to work trips in City of Mandurah in WA. Keeping in view the gravity model, the GRNN model structure has been developed. The inputs for the GRNN model are kept same as that of the gravity model. Accordingly the inputs to the GRNN model is in the form of a vector consist of land use data for the origin and destination zones and the corresponding distance between the zones. The previous studies generally used trip generations and attractions as the inputs to the NN model while this study tried to estimate the trip distribution based on the land uses. For the purpose of comparison, gravity model was used as the traditional method of trip distribution. The modelling analysis indicated that the GRNN modelling could provide slightly better results than the Gravity model with higher correlation coefficient and less root mean square error and could be improved if the size of the training data set is increased.

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