Exploring Data Quality of Weigh-In-Motion Systems
Exploring Data Quality of Weigh-In-Motion Systems

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Publisher:

Published: 2013

Total Pages: 151

ISBN-13:

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This research focuses on the data quality control methods for evaluating the performance of Weigh-In-Motion (WIM) systems on Oregon highways. This research identifies and develops a new methodology and algorithm to explore the accuracy of each station's weight and spacing data at a corridor level, and further implements the Statistical Process Control (SPC) method, finite mixture model, axle spacing error rating method, and data flag method in published research to examine the soundness of WIM systems. This research employs the historical WIM data to analyze sensor health and compares the evaluation results of the methods. The results suggest the new triangulation method identified most possible WIM malfunctions that other methods sensed, and this method unprecedentedly monitors the process behavior with controls of time and meteorological variables. The SPC method appeared superior in differentiating between sensor noises and sensor errors or drifts, but it drew wrong conclusions when accurate WIM data reference was absent. The axle spacing error rating method cannot check the essential weight data in special cases, but reliable loop sensor evaluation results were arrived at by employing this multiple linear regression model. The results of the data flag method and the finite mixed model results were not accurate, thus they could be used as additional tools to complement the data quality evaluation results. Overall, these data quality analysis results are the valuable sources for examining the early detection of system malfunctions, sensor drift, etc., and allow the WIM operators to correct the situation on time before large amounts of measurement are lost.

Traffic Data Quality Verification and Sensor Calibration for Weigh-in-motion (WIM) Systems
Traffic Data Quality Verification and Sensor Calibration for Weigh-in-motion (WIM) Systems

Author: Chen-Fu Liao

Publisher:

Published: 2012

Total Pages:

ISBN-13:

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This project aims to understand the characteristics of WIM measurements, identify different WIM operational modes, and develop mixture models for each operation period. Several statistical data analysis methodologies were explored to detect measurement drifts and support sensor calibration. A mixture modeling technique using Expectation Maximization (EM) algorithm and cumulative sum (CUSUM) methodologies were explored for data quality assurance. An adjusting CUSUM methodology was used to detect data anomaly. The results indicated that the adjusting CUSUM methodology was able to detect the sensor drifts. The CUSUM curves can trigger a potential drifting alert to the WIM manager. Further investigation was performed to compare the CUSUM deviation and the calibration adjustment. However, the analysis results did not indicate any relationship between the computed CUSUM deviation and the calibration adjustment.

Freight Demand Modeling and Data Improvement
Freight Demand Modeling and Data Improvement

Author: Keith M. Chase and Patrick Anater

Publisher: Transportation Research Board

Published:

Total Pages: 158

ISBN-13: 0309129672

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This report from the second Strategic Highway Research Program (SHRP 2), which is administered by the Transportation Research Board of the National Academies, presents the process used to develop a strategic plan aimed at improving the state of the practice in freight demand modeling and freight data. The need for this plan is identified through the increasing number of freight bottlenecks found throughout the U.S. highway network, demonstrating that more information is needed on freight flows and their relation to highway capacity planning. The report documents the research approach and findings during the development of the C20 Strategic Plan, which is available on the TRB website. The report also includes documentation of the Innovations in Freight Demand Modeling and Data Symposium, a pilot effort initiated in September 2010.

Advanced Weigh-in-motion System for Weighing Vehicles at High Speed
Advanced Weigh-in-motion System for Weighing Vehicles at High Speed

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Publisher:

Published: 1998

Total Pages: 28

ISBN-13:

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A state-of-the-art, Advanced Weigh-In-Motion (WIM) system has been designed, installed, and tested on the west bound side of Interstate I-75/I-40 near the Knox County Weigh Station. The project is a Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and International Road Dynamics, Inc. (IRD) sponsored by the Office of Uranium Programs, Facility and Technology Management Division of the Department of Energy under CRADA No. ORNL95-0364. ORNL, IRD, the Federal Highway Administration, the Tennessee Department of Safety and the Tennessee Department of Transportation have developed a National High Speed WIM Test Facility for test and evaluation of high-speed WIM systems. The WIM system under evaluation includes a Single Load Cell WIM scale system supplied and installed by IRD. ORNL developed a stand-alone, custom data acquisition system, which acquires the raw signals from IRD's in-ground single load cell transducers. Under a separate contract with the Federal Highway Administration, ORNL designed and constructed a laboratory scale house for data collection, analysis and algorithm development. An initial advanced weight-determining algorithm has been developed. The new advanced WIM system provides improved accuracy and can reduce overall system variability by up to 30% over the existing high accuracy commercial WIM system.

High Speed Weigh-in-Motion Calibration Practices
High Speed Weigh-in-Motion Calibration Practices

Author: A. T. Papagiannakis

Publisher:

Published: 2010

Total Pages: 7

ISBN-13:

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This paper provides a summary of the weigh-in-motion (WIM) calibration practices used by state highway and load enforcement agencies in the United States. The detailed statistical data presented were collected through a web-based survey questionnaire. It covers three common WIM calibration practices, namely utilizing multiple passes of test trucks, utilizing traffic stream vehicles of known static weight, and employing only WIM data quality control (QC) techniques. To put the actual practice in perspective, an overview is provided of the current WIM calibration standard (ASTM E1318-02) and the new provisional standard for quantifying pavement roughness at the approach to WIM systems (AASHTO MP 14-05). Most agencies use a combination of two or more of these methods for WIM system calibration. The majority of agencies uses WIM data QC on a routine basis and they resort to one of the other two calibration methods when WIM data quality deteriorates. Test truck calibration typically involves one or two Class 9 trucks running at several speeds. Few of these agencies, however, perform actual pavement roughness measurements on the approach to the WIM sites. Agencies that use traffic stream vehicles of known static weight for WIM calibration obtain static weights manually using permanent static scales. The method involves up to 100 trucks selected by class, speed or both class and speed. Agencies use a variety of traffic elements and formulas for computing calibration factors. Similarly, a variety of traffic data element errors are computed and various approaches are used for computing calibration factors. In the light of these findings, the paper provides a number of recommendations for improving current WIM calibration practices.