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In the United Kingdom (UK), calibration of HDM-4 has been performed relatively recently (University of Birmingham, 2011, Odoki et al., 2013). However, before the model can be used with confidence it must be calibrated to local conditions (Bennett and Paterson, 2000). In particular, the model is commonly adopted by engineers in the field of road asset management for conducting road pavement life-cycle cost analyses as it allows an estimate of the socio-economic and environmental impacts that poor condition of the road surface can generate (e.g.
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Rakha et al., 2011, Chen et al., 2017, Wang and Rakha, 2017, Wang et al., 2017, Zhou and Jin, 2017), HDM-4 is still the most widely used in practice. This represents an opportunity for the road agencies to evaluate the performance of their assets in the use-phase and can support decision making in regards to maintenance and rehabilitation (M&R) of the infrastructure.Īlthough many tools have been proposed recently to estimate the fuel consumption of road vehicles (e.g. Sandberg et al., 2011, Chatti and Zaabar, 2012, Haider and Conter, 2012, Benbow et al., 2013, Ejsmont et al., 2017). However, recent studies highlighted that the fuel consumption of road vehicles can also be affected by the conditions of the road infrastructure due to the effect of rolling resistance related parameters such as unevenness and macrotexture of the road surface (e.g.
#Hdm 4 model emissions drivers#
Recently, reductions in emissions have been achieved through new engine technologies (NAS, 2015), eco-routing (Zhou et al., 2016) and training of drivers (Ferreira et al., 2015, Walnum and Simonsen, 2015, Figueredo et al., 2017). A better understanding of the phenomena and the ability to estimate the fuel required by road vehicles is essential for optimising operational costs and emissions. Zhou and Jin, 2017, Rakha et al., 2011, Wang and Rakha, 2017, Chen et al., 2017) focused on modelling the fuel consumption of road vehicles, as this represents an important source of greenhouse gas emissions from the road transport industry (EEA, 2017, EPA, 2017a, EPA, 2017). One of the models implemented in HDM-4 aims to estimate the fuel consumption of road vehicles. HDM-4 includes dedicated tools that cover the management processes of highway infrastructures including planning, evaluation of investments, work programming, etc., that helps engineers in decision making at a strategic level.
#Hdm 4 model emissions software#
The Highway Development and Management software (HDM-4) (Kerali et al., 2006) is a powerful decision support tool developed by the World Bank and used by road agencies and managers of road infrastructure in various applications worldwide (e.g. The use of HGV fleet and network condition data as described in this paper provides an opportunity to verify HDM-4 continuously. The quality of the model estimates can be improved significantly by updating vehicle weight and frontal area in HDM-4.
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The paper shows that the current calibration of HDM-4 for the United Kingdom already requires recalibration.
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Then, the model was updated to take into account vehicle weight and frontal area specific to the considered vehicles. First, the HDM-4 model calibrated for the UK has been used. These conditions have been simulated in HDM-4 by computing fuel consumption for each truck type driving at a constant speed of 85 km/h on a flat and straight road segment in good condition.Įstimates are compared to real measurements under two separate sets of assumptions. These represent records of trucks driving at constant speed along part of the M1 and the M18, two motorways in England. Some 19,991 records from 1645 trucks are available in total. The data was obtained from the telematic database of truck fleet managers (SAE J1939) and includes three types of HGVs: light, medium and heavy trucks. The study focuses on HGVs and compares estimates made by HDM-4 to measurements from a large fleet of vehicles driving on motorways in England. This paper presents an assessment of the accuracy of the HDM-4 fuel consumption model calibrated for the United Kingdom and evaluates the need for further calibration of the model.