NEW APPROACH TO ESTIMATING THE SATURATION FLOW RATE OF A SHARED LANE WITH PERMITTED LEFT TURNS

dc.citation.epage583
dc.citation.spage573
dc.citation.volume32
dc.contributor.authorRADIČEVIĆ, VELJKO
dc.contributor.authorKRSTANOSKI, NIKOLA
dc.contributor.authorSUBOTIĆ, MARKO
dc.date.accessioned2023-07-04T07:13:37Z
dc.date.available2023-07-04T07:13:37Z
dc.date.issued2020
dc.description.abstractThe estimation of the saturation flow rate is of utmost importance when defining the signal plan at intersections. Because of the numerous influential factors, the values of which are hard to be determined, the subject problem is to be regarded as an extremely complex one. This research deals with the estimation of a saturation flow rate of a shared lane with permitted left turns. The suggested algorithm is based on the application of the artificial neural networks where the data for training are received by simulation. The results obtained by the neural networks are compared with multiple linear regression and the known HCM 2010 approach for determining the saturated flow of a shared lane. The testing data have shown that the approach based on the artificial neural networks foresaw statistically significantly better values than the ones obtained by multiple linear regression, with an error of 27 veh/h against 49 veh/h. The HCM 2010 approach is significantly worse than the two others included in this research. The ways of the future development of the suggested method could include additional factors, such as the grade of the traffic lane, the proximity of the bus stops, and others.
dc.identifier.doi10.7307/ptt.v32i4.3458
dc.identifier.urihttps://vaseljena.ues.rs.ba/handle/123456789/329
dc.language.isoen
dc.publisherFaculty of Traffic Sciences, University of Zagreb
dc.sourcePromet - Traffic&Transportation
dc.subjectartificial neural networks; multiple regression; permitted left turns; shared lane; simulation
dc.titleNEW APPROACH TO ESTIMATING THE SATURATION FLOW RATE OF A SHARED LANE WITH PERMITTED LEFT TURNS
dc.typeArticle
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