Trip distribution modelling using neural network
In this research a new generalized regression neural network (GRNN) model has been researched to estimate the distribution of journey to work trips. As a case study, the model was applied to the journey to work trips in the City of Mandurah in Western Australia. The results of the GRNN model were co...
| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
Curtin University
2014
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| Online Access: | 32121 http://hdl.handle.net/20.500.11937/669 |
| _version_ | 1848743445680619520 |
|---|---|
| author | Rasouli, Mohammad |
| author_facet | Rasouli, Mohammad |
| author_sort | Rasouli, Mohammad |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this research a new generalized regression neural network (GRNN) model has been researched to estimate the distribution of journey to work trips. As a case study, the model was applied to the journey to work trips in the City of Mandurah in Western Australia. The results of the GRNN model were compared with the well-known doubly-constrained gravity model and the Back-Propagation model and its superiority over these models has been demonstrated. |
| first_indexed | 2025-11-14T05:45:41Z |
| format | Thesis |
| id | curtin-20.500.11937-669 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T05:45:41Z |
| publishDate | 2014 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-6692018-04-13T06:36:30Z Trip distribution modelling using neural network Rasouli, Mohammad In this research a new generalized regression neural network (GRNN) model has been researched to estimate the distribution of journey to work trips. As a case study, the model was applied to the journey to work trips in the City of Mandurah in Western Australia. The results of the GRNN model were compared with the well-known doubly-constrained gravity model and the Back-Propagation model and its superiority over these models has been demonstrated. 2014 Thesis http://hdl.handle.net/20.500.11937/669 en 32121 Curtin University fulltext |
| spellingShingle | Rasouli, Mohammad Trip distribution modelling using neural network |
| title | Trip distribution modelling using neural network |
| title_full | Trip distribution modelling using neural network |
| title_fullStr | Trip distribution modelling using neural network |
| title_full_unstemmed | Trip distribution modelling using neural network |
| title_short | Trip distribution modelling using neural network |
| title_sort | trip distribution modelling using neural network |
| url | 32121 http://hdl.handle.net/20.500.11937/669 |