Systematic bias in transport model calibration arising from the variability of linear data projection

Abstract

In transportation and traffic planning studies, accurate traffic data are required for reliable model calibration to accurately predict transportation system performance and ensure better traffic planning. However, it is impractical to gather data from an entire population for such estimations because the widely used loop detectors and other more advanced wireless sensors may be limited by various factors. Thus, making data inferences based on smaller populations is generally inevitable. Linear data projection is a commonly and intuitively adopted method for inferring population traffic characteristics. It projects a sample of observable traffic quantities such as traffic count based on a set of scaling factors. However, scaling factors are subject to different types of variability such as spatial variability. Models calibrated based on linearly projected data that do not account for variability may introduce a systematic bias into their parameters. Such a bias is surprisingly often ignored. This paper reveals the existence of a systematic bias in model calibration caused by variability in the linear data projection. A generalized multivariate polynomial model is applied to examine the effect of this variability on model parameters. Adjustment factors are derived and methods are proposed for detecting and removing the embedded systematic bias. A simulation is used to demonstrate the effectiveness of the proposed method. To illustrate the applicability of the method, case studies are conducted using real-world global positioning system data obtained from taxis. These data calibrate the Macroscopic Bureau of Public Road function for six 1 × 1 km regions in Hong Kong.