Average payloads define the ton-to-truck conversion factors that are critical inputs to commodity-based freight forecasting models. However, average payloads are derived primarily from outdated, unrepresentative truck surveys. With increasing technological and methodological means of concurrently measuring truck configurations, commodity types, and weights, there are now viable alternatives to truck surveys. In this paper, a method to derive average payloads by truck body type and using weight data from weigh-in-motion (WIM) sensors is presented. Average payloads by truck body type are found by subtracting an estimated average empty weight from an estimated average loaded weight. Empty and loaded weights are derived from a Gaussian mixture model fit to a gross vehicle weight distribution. An analysis of truck body type distributions, loaded weights, empty weights, and resulting payloads of five-axle tractor trailer (FHWA Class 9 or 3-S2) trucks is presented to compare national and state-level Vehicle Inventory and Use Survey (VIUS) data and the WIM-based approach. Results show statistically significant differences between the three data sets in each of the comparison categories. A challenge in this analysis is the definition of a correct set of payloads because the WIM and survey data are subject to their own inherent misrepresentations. WIM data, however, provide a continuous source of measured weight data that overcome the drawback of using out-of-date surveys. Overall, average payloads from measured weights are lower than those for the national or California VIUS estimates.
Using Statistical Analysis of an Acceleration-Based Bridge Weigh-In-Motion System for Damage Detection
