Calibration of HDM4 Models for Estimating the Effect of Pavement Roughness on Fuel Consumption for U.S. Conditions.

Fuel consumption costs are an essential part of the costs that highway agencies must consider when evaluating pavement-investment strategies. Thesecosts depend on the vehicle class and are influenced by vehicle technology, pavement-surface type, pavement condition, roadway geometrics, environment, speed, and other factors. The objective of the study described in this paper is to recommend models for estimating the effects of pavement condition on fuel consumption. These models should be able to take into account pavement, traffic and environmental conditions encountered in the US, and capable of addressing the full range of vehicle types. A large body of research is available on the effects of pavement condition on fuel consumption and on models used to estimate these effects. Much of this informationand many of the models were developed on the basis of data generated years ago in other countries for vehicle fleets that vary substantially from those used currently in the US. However, some relevant information was collected in the US and Canada in recent years that could help in refining these models or developing models that would better apply to current and future US conditions. In this paper, mechanistic models are recommended to compute fuel consumption. The most recent fuel consumption model is the one developed as part of the Highway Development and Management software (HDM 4). Several developing and developed countries have adopted HDM 4 and are trying to calibrate and validate its models to their own conditions (e.g. Australia, France, Sweden, Canada, etc.). This paper presents the results of a calibration exercise of the HDM 4 fuel consumption model to US conditions using field data collected as part of the NCHRP project 1-45. Statistical analysis showed that there is no difference between the observed and the estimated fuel consumption at 95 percent confidence level. The calibrated HDM 4 model was able to predict fuel consumption with an error rangingfrom 2.5 percent for articulated trucks to 8 percent for medium cars.