Floating Car Data for Traffic Demand Estimation - Field and Simulation Studies

Abstract

Floating Car Data (FCD) represents a data source with exclusive features not available in conventional sources. FCD serves as motion sensors that can provide rich input for traffic demand models. An accurate traffic demand estimation is fundamental for many transportation-related applications. The goal of this research is to exploit the valuable information provided by FCD to enhance the accuracy and reduce the complexity of the traffic demand estimation process. We used the information minimization model to estimate origin-destination matrices. This model requires multiple inputs; namely, a seed matrix, route choice information, and traffic counts. We obtain the first two inputs directly from FCD. We also used FCD as a measure of attractiveness in the gravity model to calculate traffic volumes on turns to increase the number of available link traffic counts. A simulation and a field study are performed to depict the effect of FCD on the estimation process. All of the inputs and the reference data of the field study are real data gathered from different sources. The results of the GEH test and the correlation coefficient confirm that the use of FCD in the proposed model leads to an improvement of the OD-estimation in terms of accuracy and calculation time.