Verification, Refinement, and Applicability of Long-term Pavement Performance Vehicle Classification Rules
Verification, Refinement, and Applicability of Long-term Pavement Performance Vehicle Classification Rules

Author: Mark E. Hallenbeck

Publisher:

Published: 2014

Total Pages: 147

ISBN-13:

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The Long-Term Pavement Performance (LTPP) project has developed and deployed a set of rules for converting axle spacing and weight data into estimates of a vehicle's classification. These rules are being used at Transportation Pooled Fund Study (TPF) weigh-in-motion (WIM) sites across the country. This report examines the performance of those rules and the implications of their use for the development and application of default values for use within the Mechanistic-Empirical Pavement Design Guide. The report is divided into three parts. In part I, the report examines 1) how the LTPP rules differ from classification rules used by many States, 2) the performance of the LTPP rules in terms of their accuracy across truck types and at different LTPP WIM sites across the country, and 3) the size of the error that can be introduced into the estimation of traffic loading inputs for pavement design when load spectra developed from the LTPP TPF sites using these rules are combined with truck volume data collected using State-specific classification rule sets. Part II of this report examines the sensitivity of the pavement design models to the errors introduced by the use of these traffic loading inputs. Based on the results of these sensitivity tests, recommendations are made about the use of load spectra computed using Specific Pavement Studies TPF WIM data. Part III of this report describes minor changes to the LTPP classification rules recommended to improve their performance. Finally, the results of field tests of the recommended revised classification rules are presented.

Using the Traffic Monitoring Guide to Develop a Truck Weight Sampling Procedure for Use in Virginia
Using the Traffic Monitoring Guide to Develop a Truck Weight Sampling Procedure for Use in Virginia

Author: Benjamin H. Cottrell

Publisher:

Published: 1992

Total Pages: 54

ISBN-13:

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The Traffic Monitoring Guide (TMG) provides a method for the development of a statistically based procedure to monitor traffic characteristics such as traffic loadings. Truck weight data in particular are a major element of the pavement management process because there is a strong relationship between pavement deterioration and truck weights. Because truck weight data collected by weigh-in-motion (WIM) systems are more representative of actual traffic loadings and are more efficient than enforcement and static weight data, the use of the TMG and WIM systems together provide improved monitoring of truck weights. The objective of this research was to develop a plan for VDOT to implement a truck weight sampling procedure using the TMG and WIM systems. Four alternatives from the TMG that were based on different schemes for multiple measurements at permanent WIM sites were evaluated. A truck weight sampling plan was developed for the preferred alternative. Truck weight sample sites, data collection procedures, cost and resources estimates, data from permanent WIM sites, and data management information are included in the plan.

Wireless Weigh-In-Motion
Wireless Weigh-In-Motion

Author: Ravneet Singh Bajwa

Publisher:

Published: 2013

Total Pages: 118

ISBN-13:

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Vehicle count and weight data plays an important role in traffic planning, weight enforcement, and pavement condition assessment. This data is primarily obtained through weigh stations and weigh-in-motion stations which are currently very expensive to install and maintain. This dissertation presents a wireless sensor-based solution that is relatively inexpensive, and uses the measured pavement vibrations to estimate weight of moving vehicles. The proposed wireless sensor network (WSN) consists of vibration sensors that report pavement acceleration and temperature; vehicle detection sensors that report a vehicle's arrival and departure times; and an access point (AP) that controls the sensors and processes the incoming sensor data. The system can enable many new applications in infrastructure monitoring and intelligent transportation. We present energy-efficient algorithms for three such applications: automatic vehicle classification for categorizing each passing vehicle based on its axle count and inter-axle spacings; weigh-in-motion for estimating individual axle weight and total weight of trucks while they are traveling at normal speeds; and estimating pavement displacement from measured acceleration. The wireless vibration sensor developed for this project has a high resolution (≈ 400 [mu]g) and is immune to traffic sounds that are generally picked up by MEMS accelerometers. The prototype system was deployed on real highways and results for vehicle classification, weigh- in-motion, and displacement estimation were compared against reference measurements. The system passed the accuracy standards for weigh-in-motion (WIM) systems and outperformed a nearby commercial WIM station, based on conventional technology. Since sensors are embedded directly in the pavement, the system can also enable real-time monitoring of pavement condition.

Implementation of Traffic Data Quality Verification for Weight in Motion Sites
Implementation of Traffic Data Quality Verification for Weight in Motion Sites

Author:

Publisher:

Published: 2015

Total Pages: 68

ISBN-13:

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Weigh-In-Motion (WIM) system tends to go out of calibration from time to time, as a result generate biased and inaccurate measurements. Several external factors such as vehicle speed, weather, pavement conditions, etc. can be attributed to such anomaly. To overcome this problem, a statistical quality control technique is warranted that would provide the WIM operator with some guidelines whenever the system tends to go out of calibration. A mixture modeling technique using Expectation Maximization (EM) algorithm was implemented to divide the Gross Vehicle Weight (GVW) measurements of vehicle class 9 into three components, (unloaded, partially loaded, and fully loaded). Cumulative Sum (CUSUM) statistical process technique was used to identify any abrupt change in mean level of GVW measurements. Special attention was given to the presence of auto-correlation in the data by fitting an auto-regressive time series model and then performing CUSUM analysis on the fitted residuals. A data analysis software tool was developed to perform EM Fitting and CUSUM analyses. The EM analysis takes monthly WIM raw data and estimates the mean and deviations of GVW of class 9 fully loaded trucks. Results of the EM analyses are stored in a file directory for CUSUM analysis. Output from the CUSUM analysis will indicate whether there is any sensor drift during the analysis period. Results from the analysis suggest that the proposed methodology is able to estimate a shift in the WIM sensor accurately and also indicate the time point when the WIM system went out-of-calibration. A data analysis software tool, WIM Data Analyst, was developed using the Microsoft Visual Studio software development package based on the Microsoft Windows® .NET framework. An open source software tool called R.NET was integrated into the Microsoft .NET framework to interface with the R software which is another open source software package for statistical computing and analysis.

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.