Traffic Loading Spectra Characterization for Pavement ME Design in A Production Environment

Axle loading spectrum inputs obtained from existing Weight-In-Motion (WIM) stations are one of the key data elements required in the Pavement ME Design. Due to the limited number of WIM stations within a state agency, for Level 2 inputs for a particular design it is critical to implement clustering approaches to identifying similar traffic patterns and developing cluster average. Prep-ME, the final product of transportation pooled-fund study TPF-5(242), integrates four clustering approaches to generating traffic inputs for Pavement ME Design in a production environment. In this paper, the approaches are introduced and software capability to prepare traffic data inputs for a specific pavement design is provided. The approaches include the discriminant analysis based method developed in Michigan, decision tree based method in North Carolina, the Truck Traffic Classification (TTC) method, and the simplified TTC approach. The first two methods were developed under separate research efforts and are implemented in software for production purposes in Prep-ME. The third method meets the needs for state DOTs that do not have a comprehensive clustering approach but have short term classification data for a design site. The last method can be used for less important secondary roads based on engineering judgment without the need of any site-specific traffic data. It is anticipated that the Prep-ME tool can substantially reduce state DOTs' efforts in calibrating and implementing Pavement ME Design in the coming years.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: pp 197-207
  • Monograph Title: T&DI Congress 2014: Planes, Trains, and Automobiles

Subject/Index Terms

Filing Info

  • Accession Number: 01531685
  • Record Type: Publication
  • ISBN: 9780784413586
  • Files: TRIS, ASCE
  • Created Date: Jun 2 2014 3:01PM