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

Abstract

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 volume data from single-loop measurements in recent years. These studies used estimated speed for length calculation and classified vehicles into bins based on the calculated vehicle lengths. However, because of the stochastic features of traffic flow, 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, an artificial neural network method was developed to estimate classified vehicle volume data directly from single-loop measurements. The proposed neural network is three-layered with a back-propagation structure. This method was tested with data collected from several loop stations on I-5 over a long duration. The proposed artificial neural network model produced reliable estimates of volumes of classified vehicles under various traffic conditions.

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