Traffic inputs for pavement ME design using Oklahoma data
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Mechanistic-empirical pavement design guide (MEPDG) or Pavement ME Design requires traffic inputs in three levels based on the availability of data and the importance of the project. The availability of site-specific (Level 1) traffic data is generally limited due to expensive cost and the accuracy of Level 3 average is unsatisfactory, and therefore the intermedium level (Level 2) traffic data need to be developed. Using multiple years of weigh-in-motion (WIM) and vehicle classification data from Oklahoma, this paper starts with a comprehensive check of the traffic data quality, and generates site-specific traffic inputs that are required in Pavement ME Design. Cluster analysis is then applied to develop homogeneous groups for each traffic input. Subsequently, decision tree model and multinomial regression model are developed for the selection of appropriate traffic clusters under given site design conditions. In addition, a case study is included to evaluate the variations of pavement performance at various levels of traffic inputs.
KeywordsPavement ME design Weigh-in-motion Cluster analysis Decision tree Multinomial logit regression
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