Preprocessing Volume Input Data for Improved Traffic Simulation

Abstract

Microsimulation has become an increasingly indispensable tool in demanding intelligent transportation systems and planning applications. To build reliable and realistic simulation models, high-quality input data, including roadway geometry, vehicle and driver characteristics, traffic volumes, and composition, are required. Volumes of these data are important but hard to obtain; even when collected with advanced surveillance systems, they are susceptible to miscounting, gaps in time and space, and other inaccuracies. Data that appear to be accurate often do not balance out (i.e., they are inconsistent in terms of maintaining conservation throughout the system). These problems could lead to anomalies or errors during the simulation, seriously tainting the reliability and accuracy of the outputs and weakening the credibility of the conclusions. A comprehensive methodology is proposed for improving the quality of freeway traffic volumes for simulation purposes. Established and enhanced procedures for checking and correcting temporal errors are integrated with an optimization-based algorithm for reconciling spatial inconsistencies in traffic counts. A real-life freeway section was selected to show the effectiveness of the methodology over several days of varying demands. The proposed methodology improved goodness-of-fit measures like root-mean-square percentage, correlation coefficient, and Theil's coefficients. Typical traffic measures of effectiveness produced with reconciled data are closer to ground truth than those produced with corrupted data.