Methods for Estimating Axle Factors and Axle Classes from Vehicle Length Data. Issue 43 (December 2018)

• Record Type: Journal Article
• Authors: Avelar, Raul E.; Petersen, Scott; Lindheimer, Tomas; Ashraf, Sruthi; Minge, Erik
• Is Part Of: Transportation research record. Volume 2672:Issue 43(2018)
• Page Start: 110
• Page End: 121
• Publication Date: 2018-12
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/0361198118794048
• Languages: English
• ISSNs: 0361-1981

Abstract

This study developed methods to estimate axle factors and vehicle class from length-based data streams. A set of eight methods was proposed and evaluated in different testing schemes intended to observe performance on homogeneous and heterogeneous data. The initial analysis used length-based data from 61 sites in Wisconsin. The research team compared performance of the methods estimating axle factors and vehicle class proportions. Performance was comparable and consistent between homogeneous and heterogeneous subsets of data. The research team selected two methods for a final round of analysis based on their accuracy and robustness to heterogeneity. For the final round of analysis, the research team assembled a multistate dataset using data from Wisconsin and from 14 other states represented in a dataset from the Long Term Pavement Performance program. The final round of analysis compared performance under different seasons, facility type, and road character (urban vs. rural). Performance of the two identified methods was deemed appropriate and they are recommended for implementation.