An Artificial Neural Network Method for Length-based Vehicle Classification Using Single-Loop Outputs

Classified vehicle volumes are important inputs for traffic operation, pavement design, and transportation planning. However, such data are not available from single-loop detectors, the most widely deployed type of traffic sensor in the existing roadway infrastructure. Several attempts have been made to extract classified vehicle volumes from single-loop measurements in recent years. These studies use estimated speed for length calculation and classify vehicles into bins based on the calculated vehicle lengths. Due to the stochastic features of traffic flow, however, deterministic mathematical equations based on certain assumptions for speed calculation typically do not work well for all situations and may result in significant speed estimation errors under certain traffic conditions. Such errors accumulate when estimated speeds are used in vehicle length calculations and degrade the accuracy of vehicle classification. To solve this problem, we develop an artificial neural network method to estimate classified vehicle volumes directly from single-loop measurements. The proposed neural network is very simple. It is a three-layer neural network with back-propagation structure. This method is tested using data collected from several loop stations on I-5 over a long duration. The results show that the proposed artificial neural network model produces reliable estimates of classified vehicle volumes under various traffic conditions.