Short-term traffic prediction with visitor location registry data
| dc.contributor.author | Dilhasha, F | |
| dc.contributor.author | Fernando, K | |
| dc.contributor.author | Godahewa, R | |
| dc.contributor.author | Ossen, S | |
| dc.contributor.author | Perara, AS | |
| dc.contributor.author | Walpola, M | |
| dc.contributor.editor | Jayasekara, AGBP | |
| dc.contributor.editor | Amarasinghe, YWR | |
| dc.date.accessioned | 2022-11-17T09:31:28Z | |
| dc.date.available | 2022-11-17T09:31:28Z | |
| dc.date.issued | 2016-04 | |
| dc.description.abstract | Increasing road traffic is a major issue in current world. In this paper, we propose a set of prediction models that can perform short term traffic prediction for a given road segment. These prediction models have been developed using Neural Networks (NN), Bayesian Networks, Hidden Markov Models, variations of Regression and ensemble approaches of these models. CCTV records are used for validation of the results based on which a maximum accuracy of 85% was achieved. | en_US |
| dc.identifier.citation | **** | en_US |
| dc.identifier.conference | ERU Symposium 2016 | en_US |
| dc.identifier.department | Engineering Research Unit, University of Moratuwa | en_US |
| dc.identifier.faculty | Engineering | en_US |
| dc.identifier.place | Moratuwa, Sri Lanka | en_US |
| dc.identifier.proceeding | Proceedings of the ERU Symposium 2016 | en_US |
| dc.identifier.uri | http://dl.lib.uom.lk/handle/123/19549 | |
| dc.identifier.year | 2016 | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Engineering Research Unit, Faculty of Engiennring, University of Moratuwa | en_US |
| dc.subject | VLR | en_US |
| dc.subject | Traffic Prediction | en_US |
| dc.subject | NN | en_US |
| dc.subject | BCNN | en_US |
| dc.subject | HMM | en_US |
| dc.subject | Regression | en_US |
| dc.subject | Ensemble Models | en_US |
| dc.title | Short-term traffic prediction with visitor location registry data | en_US |
| dc.type | Conference-Abstract | en_US |