MEPDG;Weigh in Motion;Data Analysis;Gross Vehicle Weight;Quality Control;Truck Traffic Flow
Ramachandran, Aditya Narayanan ; John Stone, Committee Chair,William Rasdorf, Committee Member,Billy Williams, Committee Member,Ramachandran, Aditya Narayanan ; John Stone ; Committee Chair ; William Rasdorf ; Committee Member ; Billy Williams ; Committee Member
At hundreds of Weigh in Motion (WIM) stations, State Departments of Transportation collect traffic data every year to support pavement design, to enforce weight restrictions on highways and bridges, and to provide planning data for highway improvements.Reliable WIM data is particularly important to support the procedures in the FHWA Mechanistic Empirical Pavement Design Guide (MEPDG).The purpose of the research is to identify and resolve four related but relatively stand-alone problems associated with WIM data collected by NCDOT. Quality Control: After the NCDOT collects WIM data and converts it from proprietary vendor format to an ASCII text format, the quality of the data must be checked.During the quality control (QC) procedures, tests identify incomplete datasets, out of range values for individual vehicle classes, and other possible data problems.Vehicle class and weight checks generate 0.97% and 6.42% anomalies, respectively thus confirming that NCDOT equipment captured reliable WIM measurements.NC Urban and Rural Truck Traffic Profiles: Knowing the type of traffic by vehicle class by highway functional classification is critical to designing, maintaining and paying for North Carolina highway pavements.Thus, GVW plots by vehicle class and highway functional class are very important.The results indicate that in general, the class 5 and 9 GVW plots for all categories of WIM stations show expected trends.These results may be used by highway planners and pavement designers to quickly determine typical truck traffic profiles in the various NC regions and provide insight into NC truck transportation flows.NC vs. University Of Arkansas WIM QC Analysis: Most highway agencies have the data collection and design groups in different units.While a single software solution is not practical, it is recommended to perform two separate processes where the output of data QC meets the needs and standards of the design process.A comparative analysis between the QC methods followed by the University of Arkansas (UARK) and NCSU/NCDOT shows that while the UARK Pavement Designer software has better mapping functions and supports data analysis and design.However, from a WIM data analyst’s perspective it is a “black box†.In addition there is significant data reduction involved and the rigid nature of an automated QC process does not provide enough justification for the data to be used as input for the MEPDG.On the other hand, the NCSU/NCDOT approach is a two step procedure with a comprehensive QC procedure that provides the flexibility of manual overriding based on local knowledge of WIM stations and a separate unit managing the pavement design element.WIM Data Management and Analysis in SQL Server: While most research topics focus on collection and quality of data with little emphasis on the development of an integrated Database Management System (DBMS) to store and analyze traffic data.An innovative approach to perform quality control procedures and data analysis on two test WIM databases by using analysis cubes in SQL Server is the main objective of this chapter.It is a convenient method of disseminating data to users that do not have online access to the WIM database in SQL Server.The analysis cube files may also be used for data mining and exploration and could be used to observe trends in axle weights, axle spacings, vehicle class volumes etc.An additional objective is to discuss the advantages of transitioning from Access to a more comprehensive DBMS like SQL Server or Oracle to store and analyze WIM data for the NCDOT statewide WIM program.
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