Link Flow Estimation for Parallel Transportation Management

Abstract

Link flow is critical to investigate the traffic state in parallel transportation management and thus has been object of growing interest in the past few years. However, tradition estimation methods mostly use partial link counts only and convert this problem into observability studies. This paper proposed a new mathematical model based on both partial link counts and the Automatic Vehicle Identification data. This approach is tested using the actual traffic data from the city of Chengdu, China. The results indicate it is feasible to combine these two data sources to estimate the total link flows.