Trip distribution modelling using neural network
| dc.relation.isnodouble | 32121 | * |
| dc.contributor.author | Rasouli, Mohammad | |
| dc.contributor.supervisor | Prof. Hamid Nikraz | |
| dc.contributor.supervisor | Assoc. Prof. Faisal Anwar | |
| dc.date.accessioned | 2017-01-30T09:52:13Z | |
| dc.date.available | 2017-01-30T09:52:13Z | |
| dc.date.created | 2015-09-02T04:09:03Z | |
| dc.date.issued | 2014 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/669 | |
| dc.description.abstract |
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. | |
| dc.language | en | |
| dc.publisher | Curtin University | |
| dc.title | Trip distribution modelling using neural network | |
| dc.type | Thesis | |
| dcterms.educationLevel | PhD | |
| curtin.department | School of Civil and Mechanical Engineering | |
| curtin.accessStatus | Open access |