Estimating Traffic Flow in Large Road Networks Based on Multi-Source Traffic Data
Pu Wang, Jiyu Lai, Zhiren Huang, Qian Tan, Tao Lin
Abstract
Traffic flow data 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). Five-fold cross validation is employed to verify the estimated traffic flow, and the data requirements for implementing the proposed method are analyzed. 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.