Using WIM Systems and Tube Counters to Collect and Generate ME Traffic Data for Pavement Design and Analysis
Author: Lubinda F. Walubita
Publisher:
Published: 2019
Total Pages: 77
ISBN-13:
DOWNLOAD EBOOKAxle load spectra data, typically from permanent weigh-in-motion (WIM) stations, constitute the primary mechanistic-empirical (ME) traffic data input for accurate and optimal pavement design and analysis. However, due to the limited number of available permanent WIM stations (mostly located on interstate highways), most ME pavement designs rely on antiquated estimates, even for the 18-kip equivalent single axle loads (ESALs) that often result in un-optimized and costly designs and/or poor-performing pavement structures with increased maintenance costs or high construction costs due to overdesigning -- with high overall life-cycle costs. As a means to address these challenges, this study was initiated, among others, to (a) review the current state-of-the-art methodologies used for estimating ME traffic data inputs, (b) develop clustering algorithms for estimating site-specific ME traffic data, (c) explore the portable WIM as a supplement to the permanent WIM station data, and (d) develop and manage a Microsoft Access ME traffic data storage system (T-DSS). The scope of work included traffic data collection from numerous WIM stations and development of traffic data analysis macros and clustering algorithms. Key findings from the study indicated the following: (a) portable WIM is a cost-effective supplement for site-specific traffic data collection -- with proper installation and calibration, quality traffic data with an accuracy of up to 90% is attainable; (b) the developed WIM data analysis macros are satisfactorily able to compute and generate ME traffic inputs for both flexible and rigid (concrete) pavements; and (c) the developed clustering algorithms and macros constitute an ideal and rapid methodology for predicting and estimating ME traffic data inputs. Key recommendations are continued portable WIM data collection, particularly in West Texas and on farm-to-market (FM) roads, for population of the T-DSS and improved prediction accuracy of the clustering algorithms.
