LAUSR

Abstract The purpose of this study is to assess the effect of traffic data source (estimated vs. actual) on predicted progression rates of roughness and rutting for heavy-duty flexible pavements of rural freeways. Progression rates are predicted using calibrated HDM-4 models. The assessment is performed in terms of variations in maintenance intervention timing associated with the variations in progression rates. Time series pavement condition data (covering 3–5 years) have been collected for 7 sections of rural freeways for use in calibrating HDM-4 deterioration models. They range in length from 10 to 60.8 km and cover different traffic volumes, climate zones and subgrade soil types. For these sections, estimated annual average daily traffic (AADT), growth factors and assumed loading have been extracted from relevant database. Only six segments of these sections have Weigh-in-Motion (WIM) sites so relevant actual AADT, growth factors and axle load distributions have been extracted from WIM reports. The results of running the calibrated HDM-4 deterioration models using different traffic data show that actual traffic data from WIM sites result in higher rates of deterioration to that of estimated data for four sites, resulting in earlier intervention timing and higher present value agency cost. The other two sites have lower rates with actual data due to lower traffic loading than estimated.