The Asymmetric Cell Transmission model can be used to simulate traffic flows in freeway sections. The model is specified by fundamental diagram parameters—determined from mainline data, and on-ramp and off-ramp flows. The mainline flow/density data are efficiently archived and readily available, but the ramp flow data are generally found missing. This paper presents an imputation technique based on iterative learning control to determine these flows. The imputation technique is applied sequentially on all the segments of the freeway, and the ramp flows, which minimize the error between the model calculated densities/flows and measurements are investigated. The stability and convergence of the density and flow errors using the imputation updates is also presented. Finally an example is shown to illustrate its use in a practical scenario.

This content is only available via PDF.
You do not currently have access to this content.