Traffic Data for Network Level Pavement Structural Assessment and Performance Grade Asphalt Selection

Details

• Creators:
  Zhong, Jingtao ; Gu, Yangsong ; Huang, Kai ; Han, Lee D. ; Huang, Baoshan
• Corporate Creators:
  University of Tennessee, Knoxville. Department of Civil and Environmental Engineering
• Corporate Contributors:
  Tennessee. Department of Transportation ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  Asphalt Databases Grading Mechanistic-empirical Pavement Design Pavement Performance Performance Tests Rehabilitation Structural Analysis Technology Assessment Traffic
• Publication/ Report Number:
  RES2024-09
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Tennessee
• Corporate Publisher:
  Tennessee. Department of Transportation
• Abstract:
  Truck traffic is an important input for pavement design and analysis. Proper characterization of traffic patterns contributes to the design of reliable and cost-effective pavement structures. Axle load spectra obtained from the Long-Term Pavement Performance (LTPP) database, together with local Annual Average Daily Traffic (AADT) data from Tennessee, were analyzed to establish regional Level-2 traffic inputs for the Mechanistic-Empirical Pavement Design in Tennessee. Hierarchical clustering was performed to characterize the traffic patterns among the analyzed Weight-in-Motion (WIM) sites, followed by the sensitivity analysis to evaluate the impact of generated traffic inputs. Truck factors derived from National Cooperative Highway Research Program (NCHRP), LTPP Typical, LTPP Global, and cluster-based datasets were compared with TDOT default values. Results demonstrate that cluster-based level 2 provides the closest performance predictions compared with those from the site-specific level 1 data. TDOT’s truck factors generally underestimate the structural number (SN) compared with the national datasets, whereas cluster-based local calibration tends to yield more conservative SN estimates for new pavement design. Additionally, the distribution of ESALs estimated from TDOT’s default data was employed to classify traffic into four loading levels (standard, heavy, very heavy, and extreme) to support network-level binder selection. This classification attempts to provide a more rational basis for selecting Performance-graded (PG) asphalt binder consistent with expected loading conditions. The backcalculated SN values, derived by the estimated ESALs, serve as a valuable benchmark for evaluating required pavement structural capacity and supporting both new design and rehabilitation planning at the network level.
• Format:
  PDF
• Funding:
  RES2024-09
• Collection(s):
  Federal Aviation Administration
• Main Document Checksum:
  urn:sha-512:47447dac3831cf50cb24a7ada9b4ba383287868bb1bf31ebebcd8fc624db77ec5db95f5b5bd57e5ddf2d9b490ec426cc94986b4da6fd50fd18a1a613c6da30b4
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/89785/dot_89785_DS1.pdf
• File Type:
  [PDF - 4.39 MB ]