Numerical Characterization of Gross Vehicle Weight Distributions from Weigh-in-Motion Data

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 However, average payloads are derived primarily from outdated, unrepresentative truck surveys.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 WIM data, however, provide a continuous source of measured weight data that overcome the drawback of using out-of-date surveys.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.

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Vehicle Weight Distribution and Occupant Loadings

Volume 11: Transportation Systems ◽

10.1115/imece2012-87643 ◽

2012 ◽

Author(s):

Mark W. Arndt

Keyword(s):

Motor Vehicle ◽

Federal Aviation Administration ◽

Coast Guard ◽

Potential Conflict ◽

Statistical Probability ◽

Additional Weight ◽

Vehicle Weight ◽

Weight Distributions ◽

Gross Vehicle Weight ◽

Certification Requirements

The Federal Aviation Administration (FAA), Federal Transit Authority (FTA) and Coast Guard instituted or recently proposed an increase in the average passenger weight used to calculate load and conduct safety analysis and tests in multiple modes of transportation. The increased passenger weight requirements were created in response to the Center for Disease Control’s (CDC) documented rise in weight among the country’s citizens and followed crash or failure incidents in which a cause was overweight equipment. The current certification requirements under CFR 49, Part 567 state that Gross Vehicle Weight Rating (GVWR) of a motor vehicle shall not be less than the sum of the unloaded vehicle weight, rated cargo weight and 150 pounds times the number of designated seating positions. Actual occupant weight distributions versus certified weight per occupant seat causes a potential conflict between a vehicle’s in-use weight versus its certified GVWR. This paper is distinct in its contrasting of the 150 pound occupant standard in relation to documented actual occupant weight, clothing, personal items and baggage. A midsized bus example was used to explore the statistical probability that adult passengers and rated cargo would result in weight distributions that exceeded tire load capability, Gross Axle Weight Rating (GAWR), or GVWR. The unreliability of the 150 pounds per designated seat position in producing loaded weight under gross weight ratings was demonstrated for a midsized bus. Results demonstrated that load conditions and usage restrictions are identifiable and decrease the probability of operating in a condition that exceeds a weight rating. Weight assumptions that take into consideration well documented transportation industries baggage weight were identified as potentially confounding additional weight that may contribute to overload of midsized buses.

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Using Statistical Analysis of an Acceleration-Based Bridge Weigh-In-Motion System for Damage Detection

Applied Sciences ◽

10.3390/app10020663 ◽

2020 ◽

Vol 10 (2) ◽

pp. 663 ◽

Cited By ~ 1

Author(s):

Eugene OBrien ◽

Muhammad Arslan Khan ◽

Daniel Patrick McCrum ◽

Aleš Žnidarič

Keyword(s):

Statistical Analysis ◽

Damage Detection ◽

Interaction Model ◽

Statistical Properties ◽

Vehicle Weight ◽

Motion System ◽

Weigh In Motion ◽

Gross Vehicle Weight ◽

Bridge Damage ◽

Bridge Weigh In Motion

This paper develops a novel method of bridge damage detection using statistical analysis of data from an acceleration-based bridge weigh-in-motion (BWIM) system. Bridge dynamic analysis using a vehicle-bridge interaction model is carried out to obtain bridge accelerations, and the BWIM concept is applied to infer the vehicle axle weights. A large volume of traffic data tends to remain consistent (e.g., most frequent gross vehicle weight (GVW) of 3-axle trucks); therefore, the statistical properties of inferred vehicle weights are used to develop a bridge damage detection technique. Global change of bridge stiffness due to a change in the elastic modulus of concrete is used as a proxy of bridge damage. This approach has the advantage of overcoming the variability in acceleration signals due to the wide variety of source excitations/vehicles—data from a large number of different vehicles can be easily combined in the form of inferred vehicle weight. One year of experimental data from a short-span reinforced concrete bridge in Slovenia is used to assess the effectiveness of the new approach. Although the acceleration-based BWIM system is inaccurate for finding vehicle axle-weights, it is found to be effective in detecting damage using statistical analysis. It is shown through simulation as well as by experimental analysis that a significant change in the statistical properties of the inferred BWIM data results from changes in the bridge condition.

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High Accuracy Weigh-in-Motion Systems for Direct Enforcement

Sensors ◽

10.3390/s21238046 ◽

2021 ◽

Vol 21 (23) ◽

pp. 8046

Author(s):

Piotr Burnos ◽

Janusz Gajda ◽

Ryszard Sroka ◽

Monika Wasilewska ◽

Cezary Dolega

Keyword(s):

Climatic Factors ◽

High Sensitivity ◽

High Accuracy ◽

System Operation ◽

Short Term ◽

Vehicle Weight ◽

Weigh In Motion ◽

Moving Vehicles ◽

Gross Vehicle Weight ◽

Dynamic Weighing

In many countries, work is being conducted to introduce Weigh-In-Motion (WIM) systems intended for continuous and automatic control of gross vehicle weight. Such systems are also called WIM systems for direct enforcement (e-WIM). The achievement of introducing e-WIM systems is conditional on ensuring constant, known, and high-accuracy dynamic weighing of vehicles. WIM systems weigh moving vehicles, and on this basis, they estimate static parameters, i.e., static axle load and gross vehicle weight. The design and principle of operation of WIM systems result in their high sensitivity to many disturbing factors, including climatic factors. As a result, weighing accuracy fluctuates during system operation, even in the short term. The article presents practical aspects related to the identification of factors disturbing measurement in WIM systems as well as methods of controlling, improving and stabilizing the accuracy of weighing results. Achieving constant high accuracy in weighing vehicles in WIM systems is a prerequisite for their use in the direct enforcement mode. The research results presented in this paper are a step towards this goal.

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Truck weights as a function of regulatory limits

Canadian Journal of Civil Engineering ◽

10.1139/l90-007 ◽

1990 ◽

Vol 17 (1) ◽

pp. 45-54 ◽

Cited By ~ 5

Author(s):

A. Clayton ◽

R. Plett

Keyword(s):

Key Words ◽

Vehicle Weight ◽

Regulatory Limits ◽

Weight Distributions ◽

Gross Vehicle Weight ◽

Important Input ◽

Truck Weight

Models are developed for the gross vehicle weight and axle weight distributions of laden trucks as a function of governing weight limits. The models are based on truck weight surveys conducted in Manitoba between 1972 and 1986, a period of changing weight limits. They are developed for 2-axle trucks, 3-axle trucks, 5-axle (3-S2) tractor-semitrailers, 7-axle (3-S2-2) A-trains, and 7-axle (3-S2-S2) B-trains. The models can provide important input to the analysis of pavement loadings (and costs), given particular weight limits or changes in weight limits. They can also provide useful input to estimates of the relative benefits of alternative weight limit regimes. Key words: truck weights, weight limits.

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