Vehicular traffic flow information that is collected by traffic sensing devices is crucially important for transportation planning and transportation management. However, traffic sensing devices are typically distributed sparsely in road networks owing to their high installation and maintenance costs. The present study combines license plate recognition (LPR) data with taxi GPS trajectory data to develop a data-driven approach for estimating traffic flow in large road networks. The approach is applied to estimate traffic flow for an actual road network comprising 5,495 road segments using the traffic flow records of only 68 road segments (1.2% of the total) obtained with installed LPR devices. Five-fold cross validation is employed to directly verify the estimated traffic flow for road segments with LPR devices, and traffic speed is used to indirectly verify the estimated traffic flow for road segments without LPR devices. The developed data-driven approach provides an alternative and cost-efficient way of acquiring additional traffic flow information rather than installing more traffic sensing devices on roads. Keywords: Traffic Flow, Taxi GPS Data, LPR Data, Big Data

CICTP