Abstract

Weigh-in-motion (WIM) systems measure truck volumes, assist in pavement design and management, and enforce truck size and weight regulations. Although WIM systems provide truck classification based on the FHWA axle configuration classification scheme, more specific vehicle characteristics such as body configuration are necessary for freight planning and pollution monitoring. A modified decision tree model was developed to estimate truck volumes and gross vehicle weight (GVW) distributions by body configuration for five-axle semi-tractor trailers (3S2) with the use of existing WIM system measurements such as axle spacing and vehicle length. This method allows more information to be extracted from axle-based measurement data to leverage the significant investments in existing WIM systems better. Data for model development were collected at three WIM sites spanning rural and urban locations in California and described more than 7,500 3S2 trucks stratified into five trailer body categories: vans, tanks, platforms, 40-ft intermodal containers, and other. Model estimates of trailer body configuration volumes differ by only 8% from actual volumes when averaged across all body configurations on an independent test data set. A normalization procedure was designed to improve the model's robustness against systematic and random calibration inaccuracies at WIM sites. An algorithm based on Gaussian mixture models was developed to estimate GVW distribution by body configuration. Results show that estimated GVW distributions statistically capture the actual GVW distribution of each body configuration and are temporally and spatially transferable.

Full Text
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