Effect of Weigh-in-Motion System Measurement Errors on Load-Pavement Impact Estimation

Prozzi, Jorge A; Hong, Feng

doi:10.1061/(asce)0733-947x(2007)133:1(1)

Cited by 32 publications

(15 citation statements)

References 4 publications

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“…Haider et al [21,23] investigated the effect of change in systematic error for a given random error on both flexible and rigid pavement design and concluded that a 10% positive bias leads to overestimation of pavement life by approximately 5%, while in contrast, a 10% negative bias will result in underestimation of pavement life by approximately 30% to 40%. Prozi and Hong [22] reached similar conclusions in their work, whose results suggested that load-pavement impact estimation error is more sensitive to over-calibration than under-calibration of the WIM system. The aforementioned findings prove the significance of WIM system accuracy when the statistical data are further used for pavement design and analysis.…”

Section: Introductionmentioning

confidence: 57%

“…Efforts to improve WIM data quality are laid both on the development of new solutions for WIM construction, like multi-sensors systems [17,18], and in developing new procedures for data processing [19]. The significance of data quality in pavement design was studied in [16,20,21,22], Farkhideh and Nassiri [20] investigated weight measurements from WIM systems installed in asphalt concrete pavements. They reported that WIM errors were reflected in the axle load characteristics for design according to the Mechanistic-Empirical Pavement Design Guide (MEPDG), and they did affect the thickness of the designed pavement structure—in some cases by more than 100%.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of Weigh-in-Motion Measurement Accuracy on the Basis of Steering Axle Load Spectra

Ryś

2019

Sensors

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Weigh-in-motion systems are installed in pavements or on bridges to identify and reduce the number of overloaded vehicles and minimise their adverse effect on road infrastructure. Moreover, the collected traffic data are used to obtain axle load characteristics, which are very useful in road infrastructure design. Practical application of data from weigh-in-motion has become more common recently, which calls for adequate attention to data quality. This issue is addressed in the presented paper. The aim of the article is to investigate the accuracy of 77 operative weigh-in-motion stations by analysing steering axle load spectra. The proposed methodology and analysis enabled the identification of scale and source of errors that occur in measurements delivered from weigh-in-motion systems. For this purpose, selected factors were investigated, including the type of axle load sensor, air temperature and vehicle speed. The results of the analysis indicated the obvious effect of the axle load sensor type on the measurement results. It was noted that systematic error increases during winter, causing underestimation of axle loads by 5% to 10% for quartz piezoelectric and bending beam load sensors, respectively. A deterioration of system accuracy is also visible when vehicle speed decreases to 30 km/h. For 25% to 35% of cases, depending on the type of sensor, random error increases for lower speeds, while it remains at a constant level at higher speeds. The analysis also delivered a standard steering axle load distribution, which can have practical meaning in the improvement of weigh-in-motion accuracy and traffic data quality.

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Section: Introductionmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Investigation of Weigh-in-Motion Measurement Accuracy on the Basis of Steering Axle Load Spectra

Ryś

2019

Sensors

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“…In general, acceptable error limits of WIM range from ±6% to ±15% by the type of technologies [11]. However, the practical performance may substantially deteriorate, depending on the operating environment, level of maintenance and calibration work carried out, and pavement condition [6], [12], [13]. To prevent overweight trucks from being bypassed due to WIM errors, typical weight stations operate a weight threshold value that is lower than the legal limit.…”

Section: Problem Statementmentioning

confidence: 99%

Adaptive Metering Algorithm for Electronic Commercial Vehicle Preclearance Systems

Lee

Chow

2012

IEEE Trans. Intell. Transport. Syst.

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This paper presents an adaptive metering algorithm for enhancing the electronic screening (e-screening) operation at truck weight stations. This algorithm uses a feedback control mechanism to control the level of truck vehicles entering the weight station. The basic operation of the algorithm allows more trucks to be inspected when the weight station is underutilized by adjusting the weight threshold lower. Alternatively, the algorithm restricts the number of trucks to inspect when the station is overutilized to prevent queue spillover. The proposed control concept is demonstrated and evaluated in a simulation environment. The simulation results demonstrate the considerable benefits of the proposed algorithm in improving overweight enforcement with minimal negative impacts on nonoverweighed trucks. The test results also reveal that the effectiveness of the algorithm improves with higher truck participation rates in the e-screening program.Index Terms-Commercial vehicle operations (CVO), electronic screening (e-screening), traffic control, truck weight, weight station.

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“…In addition, it is important to point out that axle load data collected by WIM equipment can be subject to two types of measurement errors: random error, which is due to the equipment's intrinsic properties, and systematic error, which is due to external factors such as roadway and environmental conditions. However, it has been found that given well-calibrated conditions the effect of measurement error, mainly from random error, does not have a significant impact on load-pavement impact estimation (Prozzi and Hong 2007). In this example, the traffic data can be regarded "good quality" since the WIM scale was frequently calibrated by Texas Department of Transportation (TxDOT) personnel.…”

Section: Proposed Approach To Highway Cost Allocationmentioning

confidence: 99%

A New Approach for Allocating Highway Costs

Hong¹,

Prozzi²,

Prozzi³

2010

J Transp Res Forum

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The allocation of highway costs is constantly debated among legislatures, highway agencies, and highway users as it directly relates to concerns about equity in terms of cost responsibility and actual user charges. One of the major challenges in highway cost allocation stems from the need to estimate pavement damage by different vehicle classes. Normally, the calculation of damage caused by heavy vehicles to the highway infrastructure utilizes the concept of Equivalent Single Axle Load (ESAL). This concept was empirically established after the American Association of State Highway Officials America (AASHO) Road Test almost half a century ago. Although the ESAL concept is widely used in pavement design, it has a number of shortcomings when applied for the estimation of pavement damage by different vehicle classes. Some of these limitations include: failure to account for specific infrastructure and environmental conditions, disregard of the differences in traffic configurations and composition, and the inability to capture different distress types. This leads to a fairly inaccurate and generic estimation of pavement damage by vehicle class. This paper proposes an innovative and more rational highway cost allocation approach based on the recently completed guide for the "Mechanistic-Empirical Design Guide of New and Rehabilitated Pavement Structures" developed under the National Cooperative Highway Research Program (NCHRP) Project 1-37A. The Guide accounts for all factors that contribute to pavement deterioration, thereby addressing the shortcomings of an ESAL-based analysis listed earlier. Estimates for pavement damage attributable to each vehicle class can thus be accurately simulated. For the purposes of this study, traffic data collected at a weigh-in-motion station in Texas were used to estimate the highway cost shares of different vehicle classes, given different pavement structural capacities.

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Effect of Weigh-in-Motion System Measurement Errors on Load-Pavement Impact Estimation

Cited by 32 publications

References 4 publications

Investigation of Weigh-in-Motion Measurement Accuracy on the Basis of Steering Axle Load Spectra

Investigation of Weigh-in-Motion Measurement Accuracy on the Basis of Steering Axle Load Spectra

Adaptive Metering Algorithm for Electronic Commercial Vehicle Preclearance Systems

A New Approach for Allocating Highway Costs

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