Direct usage of occupancy data for multiregime speed-flow rate models

Abstract

Early macroscopic traffic flow models were based on observations of volume, speed, and density. The invention of traffic sensors has supplied a wealth of data for the development of more accurate macroscopic flow models. However, traffic sensors typically collect volume, speed, and occupancy data. Researchers prefer to convert occupancy to density because of the density usage in earlier models; however, for this conversion, the average length of passed vehicles must be determined. This length is frequently estimated by researchers. However, because the explanatory variable (density) is not observed but produced, this estimation weakens the model results. Considering these challenges, this research proposes a novel traffic flow modeling approach based on occupancy. The proposed method was tested in three speed-flow rate relationship regions, one of which is congested and two of which are free flow. Free flow speed, capacity, queue discharge flow, breakpoint flow rate, and optimum speed can all be determined more precisely with this method. Furthermore, the nonlinear relationship between speed and flow rate was clarified. The proposed traffic flow model is extremely useful, especially for dynamic traffic management applications, because it is based on directly gathered data such as volume, speed, and occupancy.