| dc.contributor.advisor | Ghosh, Bidisha | |
| dc.contributor.author | Dunne, Stephen | |
| dc.date.accessioned | 2019-11-05T16:50:53Z | |
| dc.date.available | 2019-11-05T16:50:53Z | |
| dc.date.issued | 2013 | |
| dc.identifier.citation | Stephen Dunne, 'Short-term traffic condition variables forecasting using Artificial Neural Networks', [thesis], Trinity College (Dublin, Ireland). Department of Civil, Structural and Environmental Engineering, 2013, pp 203 | |
| dc.identifier.other | THESIS 10274 | |
| dc.description.abstract | Short-term traffic forecasting (STTF) is a critical element of Intelligent Transport Systems
(ITS). The use of ITS is vital in order to ensure the sustainability and increase the efficiency of the transportation network. ITS bases decisions on traffic conditions and so it can only be
effective if the forecasted future traffic conditions provided are accurate. | |
| dc.format | 1 volume | |
| dc.language.iso | en | |
| dc.publisher | Trinity College (Dublin, Ireland). Department of Civil, Structural and Environmental Engineering | |
| dc.relation.isversionof | http://stella.catalogue.tcd.ie/iii/encore/record/C__Rb15651559 | |
| dc.subject | Civil, Structural and Environmental Engineering | |
| dc.subject | Ph.D. Trinity College Dublin. | |
| dc.title | Short-term traffic condition variables forecasting using Artificial Neural Networks | |
| dc.type | thesis | |
| dc.type.supercollection | thesis_dissertations | |
| dc.type.supercollection | refereed_publications | |
| dc.type.qualificationlevel | Doctoral | |
| dc.type.qualificationname | Doctor of Philosophy (Ph.D.) | |
| dc.rights.ecaccessrights | openAccess | |
| dc.format.extentpagination | pp 203 | |
| dc.description.note | TARA (Trinity’s Access to Research Archive) has a robust takedown policy. Please contact us if you have any concerns: rssadmin@tcd.ie | |
| dc.identifier.uri | http://hdl.handle.net/2262/90116 | |
