Fuel

Paper describing the validation of the HDM-4 fuel consumption model.

The thesis presents a novel approach for estimating the impact of road roughness and macro-texture on truck fleet fuel consumption based on Big Data.

Results show that, although the present study confirms that road surface characteristics, such as roughness and macrotexture, can affect the fuel consumption of trucks, due to the low quality of the data available, that is currently difficult to quantify. A comparison of the results obtained with the findings of studies conducted in the past, shows that there is some match in the order of magnitude of the estimates made, but this is not always the case.

This paper presents an assessment of the accuracy of the HDM-4 fuel consumption model. 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. 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. Some 19,991 records from 1645 trucks are available in total. These represent records of trucks driving at constant speed along part of the M1 and the M18, two motorways in England.

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.

Estimates are compared to real measurements under two separate sets of assumptions. First, the HDM-4 model calibrated for the UK has been used. Then, the model was updated to take into account vehicle weight and frontal area specific to the considered vehicles.

The paper shows that the current calibration of HDM-4 for the United Kingdom already requires recalibration. The quality of the model estimates can be improved significantly by updating vehicle weight and frontal area in HDM-4. The use of HGV fleet and network condition data as described in this paper provides an opportunity to verify HDM-4 continuously.

In this study, consumption of energy due to pavement structural response through viscoelastic deformation of asphalt pavement materials under vehicle loading was predicted for 17 field sections in California by using three different models. Calculated dissipated energy values were converted to excess fuel consumption (EFC) to facilitate comparisons under different traffic loads (car, SUV, and truck) and speeds and different temperature conditions. The goal of the study was to compare the different modeling approaches and provide first level estimates of EFC in preparation for simulations of annual EFC for different traffic and climate scenarios as well as different types of pavement structures on the California state highway network. Comparison of the predicted EFC for all test sections showed that all three models produced different results which can be attributed to the differences in the three modeling approaches. However, predictions from the three models are generally of same order of magnitude or an order of magnitude different indicating that overall these models can be calibrated using data from field measurements, which is the next step in the research program.

Road transport is responsible for 76% of cargo movement in South Africa; at the same time transport cost in SubSahara Africa forms a much higher fraction of the total cost of landed goods compared to the rest of the world.  Fuel represents the single biggest operational cost for road transport operators; efforts towards improved fuel efficiency are therefore a priority within this sector.  As fuel usage depends on many factors, including engine size, vehicle fabrication, driver behaviour, payload, traffic conditions and route inclinations, it is not a trivial exercise to create accurate consumption benchmarks for a specific operation.  This paper investigates various factors that are known to impact fuel utilization with the aim of quantifying the relative importance of the contribution of each.  Fuel usage data was collected for a representative set of trucks covering all major routes in South Africa and for various cargo categories over a 3 year period.  This data was filtered based on different criteria, including driver identity, route and vehicle model.  Comparisons were drawn between consumption figures derived from manually recorded refuel events and figures derived from measurements that are automatically performed by on-board vehicle sensors.  It was concluded that driver behaviour and the possible siphoning of fuel from vehicles seem to be a major factor and would justify further actions towards curbing fuel losses.  At the same time route inclination, payload and vehicle model also play an important role and should be incorporated into costing models used to determine how different routes and trips are priced.

The objective of the study described in this paper is to investigate a mechanistic relationship between roughness and Fuel consumption (FC). First, simulations of the response of a 5‐axle tractor‐semitrailer (5A‐Semi) to real profiles with different roughness levels were performed to estimate the dynamic axle loads induced by each profile. Then, the Dynamic Load Coefficient (DLC) was computed every 0.03 km (0.02 miles). Finally, the FC of the truck was calculated and the recent HDM 4 model from the NCHRP 1‐45 project was re‐calibrated using DLC instead of the International Roughness Index (IRI) for each 0.03 km (0.02 miles) subsection. The analysis shows that the new model, after appropriate calibration, adequately predicted the effect of roughness on FC of the 5‐axle Semi. Statistical analysis showed that there is no difference between the observed and the estimated FC at 95 percent confidence level.

Reducing fuel consumption on roadway networks can have a huge impact on the nation’s economy and environment. Existing ad-hoc transportation planning efforts that allocate limited funding on need-based criteria are insufficient for providing a significant reduction in fuel consumption. Therefore, there is an urgent need for new research to analyze the impact of planning effort on fuel consumption to support transportation’s decision making. This paper presents the development of a new model for estimating fuel consumption in transportation networks under budget constraints by taking into consideration the effect of pavement deterioration on fuel consumption. The model is composed of three main modules to (1) estimate vehicle fuel consumption of transportation networks; (2) allocate limited funding to competing highway rehabilitation projects; and (3) evaluate the impact of pavement roughness and deterioration on fuel consumption. An application example is analyzed to evaluate the developed model and illustrate capabilities of the model. The application result demonstrates the significant impact of highway rehabilitation planning on fuel consumption on roadway networks. This study should prove useful to planners and decision makers in evaluating the impact of highway rehabilitation efforts on fuel consumption.

Vehicle fuel consumption and emission are two important effectiveness
measurements of sustainable transportation development. Pavement plays an essential
role in goals of fuel economy improvement and greenhouse gas (GHG) emission
reduction. The main objective of this dissertation study is to experimentally investigate
the effect of pavement-vehicle interaction (PVI) on vehicle fuel consumption under
highway driving conditions. The goal is to provide a better understanding on the role of
pavement in the green transportation initiates.

A novel mechanistic model based on an infinite beam on elastic foundation is developed to
quantify the impact of pavement structural and material properties on pavement deflection and consequently on vehicle fuel consumption. The model can also account for the effect of temperature and vehicle speed on fuel consumption. A simplified expression for evaluating the energy dissipation for practical purposes is proposed and used to investigate the impact of various pavement design systems on fuel consumption. GPS (General Pavement Studies) sections from the FHWA’s Long Term Pavement Performance program (FHWA 2011) are used for this study. These sections consist of asphalt concrete (AC), portland cement concrete (PCC) and composite pavements. The model quantifies the impact of temperature and vehicle speed on the fuel consumption and confirms that those impacts are negligible for PCC and significant for AC pavements due to their viscoelasticity.

Nothing monumental...

MIRAVEC Report D5.1.  This is a report of the findings in Work Package 4 (WP4) in MIRAVEC. The objectives of this WP are to: - Identify the current role of road vehicle energy consumption and CO2 emissions in existing pavement/asset management systems and opportunities for its improvement, - Analyse potential implications of optimizing for low energy consumption for other objectives, - Give recommendations on implementation of road vehicle energy consumption (CO2 emissions) in existing pavement/asset management systems. 

MIRAVEC Report D3.1.  This report details the spreadsheet tool developed to aid NRAs in the assessment of fuel consumption. The tool has brought together a number of different models and studies of fuel consumption and incorporates:  The effect of road roughness on fuel consumption (measured using IRI)  The effect of macro texture depth on fuel consumption (measured using MPD)  The effect of road geometry on fuel consumption (measured using the degree of curvature and rise and fall/gradient)  The effect of vehicle speed on fuel consumption. The tool estimates the average vehicle speed from the road geometry, the level of traffic and the split of heavy to light vehicles. In addition, a simple method for estimating the effect of idle time due to traffic congestion has been developed and implemented. 

MIRAVEC Report D2.1.  This is a report of the findings in Work Package 2 (WP2) in MIRAVEC. The objective of this WP is to describe existing modelling tools and evaluate their capabilities with respect to analysing the effects identified in WP1 “Road infrastructure influence effects on vehicle energy consumption and associated parameters”. The variables identified in WP1 and considered to be the most important to take into consideration when estimating the impact of road infrastructure on road traffic energy use are texture (MPD), IRI (unevenness), rut depth (RUT), gradient (RF), crossfall, horizontal curvature (ADC), road width, traffic volume (AADT) and speed (v). In this report, a selection of projects that have evaluated road characteristics and the effect on energy use are described and analysed. The results of these project shows that there can be benefits energy wise in taking the energy aspect into consideration when planning a new road or choosing rehabilitation measure of the pavement. 

MIRAVEC Report D5.3.  The objective of MIRAVEC was to build on existing knowledge and models in order to achieve a more holistic view considering a broad variety of effects. The project results are compiled in this final report of MIRAVEC project. The first part of this final report is a short summary on the findings and outputs of all Work Packages (WP), while the second part is a summary of all recommendations to National Road Administrations (NRAs) on how to implement the findings, models and tools in pavement and asset management systems. The main findings and recommendations of the project can be summarised as follows:  Five major groups of parameters influencing road vehicle energy and fuel consumption were identified, of which a subset was selected based on impact, potential for influence by National Roads Administrations and integration into existing fuel consumption models. Further analysis showed that while currently monitored parameters can be used for modelling several effects of the infrastructure influence, knowledge gaps remain with respect to other parameters and the correct modelling of associated effects.  There is no current model which takes all infrastructure-related effects into account. Most models for fuel consumption and CO2 emission of road vehicles focus on vehicle and traffic flow characteristics and tend to neglect details of the infrastructure. The Swedish VETO model is one of the most advanced models in this respect and was the basis of many analyses. As the knowledge about the infrastructure influence increases, these models offer the possibility to integrate this knowledge into decision making.  The spreadsheet tool developed in WP3 allows the comparison of the effects of different infrastructure-related measures on fuel consumption and CO2 emission. It requires data about the most widely available pavement and road layout parameters and uses information about traffic flow and vehicles as background information. While the tool can be applied even with limited data, the strong influence of these background data found in the analysis may supersede the infrastructure effects in some cases.  The investigation of the current situation with regard to the occurrence of this topic in pavement and asset management found a growing awareness of its importance with road managers, but so far very limited implementation in the actual systems. While future models based on the more commonly monitored infrastructure parameters will make the integration of vehicle CO2 emission feasible, acceptance and weight in decision making in the view of limited financial resources for maintenance still remain to be achieved. 

Various reports and presentations from the EU MIRAVEC looking at the impact of infrastructure on vehicle energy.

Very interesting report which looks at the impact of rigid vs flexible pavements. Discusses the recent work done with the HDM-4 fuel consumption model as well. Useful for updating parameters from the original HDM-4 work.

MIRAVEC Report D1.1: This document describes the different road infrastructure parameters which can contribute to the overall road vehicle energy consumption and highlights those which can be influenced by infrastructure design. It is a report on the effects and parameters that need be considered in order to determine the influence of road infrastructure on road vehicle energy consumption by modelling. The effects and properties were divided into the following five groups: A. Effects of pavement surface characteristics (rolling resistance, texture, longitudinal and transversal unevenness, cracking, rutting, other surface imperfections) B. Effects of road design and layout (e.g. road curvature, gradient and crossfall, lane provision) C. Traffic properties and interaction with the traffic flow (e.g. free flowing traffic vs. stop-and-go, speed limits, access restrictions) D. Vehicle and tyre characteristics including the potential effect of technological changes in this area E. Meteorological effects (e.g. temperature, wind, water, snow, ice)

The higher investment cost for Portland cement concrete over asphalt concrete pavements is balanced by lower maintenance costs and a longer technical life. It is also claimed that the truck roll-ing resistance is lower on concrete pavements. An important actuality as the world continues to grow and emissions must be kept at bay. A number of studies have been made where a truck is driven over different pavement structures and the fuel consumption is carefully measured. They show that the rolling resistance does vary, but it is difficult to assess exactly how much can be at-tributed to the pavement structure. The present paper deals with assessing pavement hysteresis by evaluating falling weight deflectometer time histories. It was found that the visco-elastic proper-ties of the asphalt had a great influence on the curve. Water present and the subgrade material al-so affected the curve due to the material damping properties. At a field site, a motorway consisted of asphalt concrete and PCC. The difference in energy losses between the two is significant and can be accounted for when comparing the two materials for life cost analysis purposes. The results support choosing Portland cement concrete for high volume truck roads.

Detailed reference book on the different types of driving cycles used for modelling emissions.

This paper describes the methodology used for determining on-road truck tyre rolling resistance and fuel consumption along with a preliminary analysis of the acquired data.

TRB Conference paper presenting results of calibrating HDM fuel model to Canada and Chile

Calibrating the free speed and congestion fuel models to Thailand

Results of testing HDM-4 congestion model in Indonesia

How congestion effects were to be modelled in HDM-4

Describes the development of the HDM-4 congestion-fuel model

Describes development of HDM-4 fuel model