Browsing by Subject "Weigh in motion scales"

Now showing 1 - 4 of 4
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    Development of a Weigh-Pad-Based Portable Weigh-In-Motion System
    (Minnesota Department of Transportation, 2012-12) Kwon, Taek M.
    Installing permanent in-pavement weigh-in-motion (WIM) stations on local roads is very expensive and requires recurring costs of maintenance trips, electricity, and communication. For county roads with limited average daily traffic (ADT) volume, such a high cost of installation and maintenance is rarely justifiable. One solution to bring WIM technologies to local roads is to utilize a portable WIM system, much like pneumatic tube counters used in short-duration traffic counts. That is, a single unit is reused in multiple locations for few days at a time. This way, WIM data is obtained without the cost of permanent in-pavement WIM stations. This report describes the results of a two-year research project sponsored by the Minnesota Department of Transportation (MnDOT) to develop a portable WIM system that can be readily deployed on local roads. The objective of this project was to develop a portable WIM system that would be used much like a pneumatic tube counter. The developed system is battery operated, low cost, portable, and easily installable on both rigid and flexible pavements. The report includes a sideby- side comparison of data between the developed on-pavement portable WIM system and an in-pavement permanent WIM system.
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    Enhancement and Field Test Evaluation of New Battery-Less Wireless Traffic Sensors
    (Center for Transportation Studies, 2011-10) Pruden, Sean; Vijayaraghavan, Krishna; Rajamani, Rajesh
    This project focused on the enhancement of a previous battery-less wireless traffic flow sensor so as to enable it to provide weigh-in-motion (WIM) measurements and provide enhanced telemetry distance. The sensor consists of a 6-feet-long device which is embedded in a slot in the road flush with the pavement. As a vehicle travels over the sensor, vibrations are induced in the sensor. Using piezoelectric elements, energy is harvested from the vibrations and used to power the electronics in the sensor for signal measurements and wireless transmission. The sensor’s performance was evaluated by embedding it in a slot in concrete pavement and driving various vehicles of known weight over it at a number of different speeds on different days. The sensor was found to meet the specification of 500 feet telemetry distance. It was able to provide WIM measurements with an accuracy of better than ±15% in the absence of vehicle suspension vibrations. However, much of the WIM data during the latter period of sensor testing was obtained in the presence of significant suspension vibrations. The project also evaluated the use of 4 consecutive WIM sensors in the road to remove the influence of suspension vibrations.
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    Implementation of Traffic Data Quality Verification for WIM Sites
    (Center for Transportation Studies University of Minnesota, 2015-05) Liao, Chen-Fu; Chatterjee, Indrajit; Davis, Gary A.
    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.
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    Weigh-in-Motion Sensor and Controller Operation and Performance Comparison
    (Minnesota Department of Transportation, 2018-01) Gupta, Diwakar; Tang, Xiaoxu; Yuan, Lu
    This research project utilized statistical inference and comparison techniques to compare the performance of different Weigh-in-Motion (WIM) sensors. First, we analyzed test-vehicle data to perform an accuracy check of the results reported by the sensor-vendor Intercomp. The results reported by Intercomp mostly matched with our own analysis, but the data were found to be insufficient to reach any conclusions about the accuracy of the sensor under different temperature and speed conditions. Second, based on the limited data from the Intercomp and IRD sensor systems, we performed tests of self-consistency and comparisons of measurements to inform the selection of a superior system. Intercomp sensor data were found to be not self-consistent but IRD data were. Given the different measurements provided by the two sensors, without additional data, we were not able to reach a conclusion regarding the relative accuracy or the duration of consistent observations before needing recalibration. Initial comparisons indicated potential problems with the Intercomp sensor. We then suggested alternate approaches that MNDOT could use to determine whether recalibration was required. Finally, we analyzed ten-month data from the IRD WIM system and four-month data from the Kistler WIM system to evaluate relative sensor accuracy. While both systems were found to be self-consistent within the data time frame, the Kistler system generated more errors than the IRD system. Conclusions regarding relative accuracy could not be reached without additional data. We identified the sorts of measurements that would need to be monitored for recalibration and the methodology needed for estimating future recalibration time.