General reports on road user effects.
A dissertation presenting how EVs could be modelled with HDM-4. This shows the potential for major distortion of results as the capital costs are twice those with ICE vehicles.
Detailed analysis of the total cost of ownership (TCO) consisting of all costs related to both purchasing and operating the vehicle. This TCO analysis builds on previous work to provide a comprehensive perspective of all relevant vehicle costs of ownership. In this report, we present what we believe to be the most comprehensive explicit financial analysis of the costs that will be incurred by a vehicle owner. This study considers vehicle cost and depreciation, financing, fuel costs, insurance costs, maintenance and repair costs, taxes and fees, and other operational costs to formulate a holistic total cost of ownership and operation of multiple different vehicles. For each of these cost parameters that together constitute a comprehensive TCO, extensive literature review and data analysis were performed to find representative values in order to build a holistic TCO for vehicles of all size classes. The light- and heavy-duty vehicles selected for analysis in this report are representative of those that are on the road today and expected to be available in the future. Table ES-1 summarizes the main parameters in this study, including the cost components which comprise TCO, the sizes and vocations of vehicles which are analyzed, the powertrains of these vehicles, and the model year for analysis of both current and future vehicles.
The HDM-4 Road User Costs Model (HDM-4 RUC) is an Excel based model designed to compute, for different vehicle types and road conditions, vehicle speeds, fuel consumption, vehicle operating costs, passenger time costs, emission and accident costs based on the Highway Development and Management Model (HDM-4) relationships. The model computes unit road user costs, performs sensitivity analysis, computes network road user costs and performs a simplified economic evaluation of a road project. The current version of HDM-4 RUC (Version 5.0) computes the vehicle operating costs and speeds, function of roughness, cubic polynomials that can be used together with the RED and RONET models.
Report on quantifying the input parameters, and indexes, for updating vehicle operating costs.
This paper presents a summary of findings on the effect of pavement roughness [international roughness index (IRI)] and texture [mean profile depth (MPD)] on vehicle operating costs. The most important cost affected by roughness was fuel consumption, followed by repair and maintenance, then tire wear. An increase in IRI of 1 m/km (63.4 in./mi) increased fuel consumption of passenger cars by 2% to 3%, regardless of speed. For heavy trucks, this increase was 1% to 2% at 70 mph and 2% to 3% at 35 mph. Surface texture and pavement type had no effect on fuel consumption for vehicle classes except heavy trucks. An increase in MPD of 1 mm (0.039 in.) increased fuel consumption by 1.5% at 55 mph and 2% at 35 mph. The effect of pavement type on fuel consumption was statistically not significant for all light vehicles and was statistically significant for heavy trucks only at 35 mph in summer conditions (308C). No data were available for heavy trucks in winter. For repair and maintenance, there was no effect of roughness up to an IRI of 3 m/km (190 in./mi). Beyond this range, an increase in IRI up to 4 m/km (254 in./mi) increased repair and maintenance costs by 10% for passenger cars and heavy trucks. At an IRI of 5 m/km (317 in./mi), the increase was up to 40% for passenger cars and 50% for heavy trucks. An increase in IRI of 1 m/km (63.4 in./ mi) increased tire wear of passenger cars and heavy trucks by 1% at 55 mph.
Measurement of pavement permanent deformation is critical to highway agencies for both pavement design and rehabilitation. Since the AASHO Road Test in the late 1950s and early 1960s, field rut condition is monitored by agencies on a regular basis. Over the decades, rut depth has been the solely dominating pavement permanent deformation indicator extensively used, though it faces many criticisms for being incomplete to characterize rut. The premature data collection technology, lack of uniform practice standard, and unrealistic expectations have hindered the improvement of rut measurement.
Recently, two AASHTO draft standard documents PP70-10 and PP69-10 are published specifying data requirements and procedures for deriving new technical parameters, respectively. It is envisioned that the mature application of the 1 mm 3D pavement surface data collected by PaveVision3D Ultra system in companion with the new standards poses a significant opportunity to change the landscape of current rut measurement practice.
This research described in the thesis accomplishes the following tasks to provide substantial insights into the new rut measurement requirements. First, thirteen technical parameters covering multiple aspects of pavement ruts are derived based on PP69-10. The rut depth measures documented in PP69-10 are verified with ground truth values. Second, a thorough study of these rut attributes is conducted with 8,960 transverse profiles collected from National Highway Systems (NHS) in Arkansas. The interrelationships among different technical parameters are explored, and inferences regarding pavement performance are developed. Third, a comprehensive hierarchical system is constructed for overall permanent deformation evaluation. The standardized index provided by the proposed system can help highway agencies manage pavement performance in a more comprehensive and reasonable manner. Fourth, the impact of vehicle wandering on the accuracy of rut measurements is assessed. A methodology is developed and verified to reduce the adverse effect of unknown lane locations.
Overall, this thesis demonstrates findings of a timely study in rut measurement and characterization based on the latest standard protocols and data collection technology. The research provides insights and useful supplements to both practitioners and researchers in the transition to apply the most advanced 1 mm 3D laser imaging technology to comprehensive pavement survey.
The economic prosperity of a country is strongly associated with the relative size and physical condition of its road network, which is the most important component of its transportation infrastructure. The rate at which a country’s economy grows is very closely linked to the rate at which the transport sector grows. In the process of economic analysis of highways, the road user cost plays a significant role. Large research has been carried out in India, from half century. Amongst the Road User Cost (RUC) models, Vehicle Operating Cost (VOC) models are the major component and are analytically complex in nature. The determination of VOC is a key element in evaluating the highway projects. The updation of VOC models are required for highway project appraisals, particularly for road maintenance. The determination of VOC is complicated and time consuming, as lots of data collection is required. With time, these VOC models become outdated. In the current paper, another method, Whole price index was used to update the vehicle operating cost. This method is very simple, easy and time saving. This updated VOC models can be used by various Highway engineers to evaluate the road projects on monetary basis.
Paper from India listing RUE models and how they can be annually updated.
Engineering economic analysis applies economic concepts and methods to engineering problems to support decisions on a best course of action. Economic analysis provides a way of comparing the economic gains expected from an investment with the cost of that investment; providing an objective understanding of value to be expected for cost incurred. The Highway Development and Management Model (HDM-4) system is seen as the international standard decision support tool for road management. This article focuses on the Road User Cost Modelling in HDM-4 and validation of the model using Road User Costs Knowledge System (RUCKS).
Presentation summarizing current situation with regard to predicting vehicle operating costs
NHCRP 720. This report presents models for estimating the effects of pavement condition on vehicle operating costs. These models address fuel consumption, tire wear, and repair and maintenance costs and are presented as computational software on the accompanying CD-ROM
to facilitate use. The material contained in the report should be of immediate interest to state pavement, construction, and maintenance engineers; vehicle fleet managers; and those involved in pavement-investment decision processes and financial aspects of highway transportation.
Report describing the effects of vehicle-generated dust.
Excel workbook for calculating the HDM-4 vehicle operating costs. Also includes a worksheet with values for HDM-4 user parameters applied in different countries.
[In Spanish] This paper aims to analyze the structure of the sub model Social and Environmental Effects (SEE), included in the version 1.3 of the Highway Development and Management System (HDM-4). Several studies have been carried out describing other sub models within the HDM-4 and regional experiences using this tool to manage road networks as well. Recently the Mexican Institute for Transportation (IMT) published a study concerning sensibility analysis of asphalt pavement deterioration model for Mexican conditions. However, this is the first attempt to analyse the potential use of the SEE in Mexico. Even though the SEE includes the analysis of the energy balance and the vehicle emissions, only the latter will be considered in this paper due to the lack of information concerning energy expenditures for relevant activities in Mexico. In spite of the fact that the model doesn?t provide accurate results because of the use of aggregated data and several simplifications in the model when representing the emission formation processes, it is concluded that the potential in the use of the SEE as an integral part of the HDM-4 is large, even more when the mechanisms and tools to calculate emissions from road transport are either too expensive or too complex to implement. It is also desirable to conduct other studies in order to calibrate the model, activity which requires a concerting effort from society, government and researchers due to the costs involved. Nevertheless, the benefits obtained could match or even surpass the costs, especially if externalities are taken into account.
The NZVOC Model is used by Transfund New Zealand to prepare tables of vehicle operating costs for their Project Evaluation Manual. Two projects were conducted to update the costs and develop a new version of the NZVOC Model, one in 1999 and a second in 2001-2003. The NZVOC model contains enhanced VOC relationships and formed the basis for the HDM-4 RUE model.
ARRB testing of HDM-4's RUE modelling.
Article highlighting difference between the two sets of VOC models
Memo on issues with HDM emissions modelling
Briefing paper describing revised RUE model
Briefing paper describing RUE model
A review of how HDM-III was used in studies in different countries
Original report describing HDM-4 RUE models. Much of the work was subsequently updated but still provides good background information
How the recommendations from the workshop were implemented
The World Bank's Vehicle Operating Costs Model (HDM-VOC) is a stand alone program that estimates vehicle operating costs, using the HDM-III relationships, for ten vehicle types as a function of the vehicle characteristics, vehicle utilization, vehicle unit costs, and the road characteristics. The HDM-VOC software is available for downloading at this site, together with Technical Paper 234 'Estimating Vehicle Operating Costs' (PDF 3,336 KB) by Rodrigo S. Archondo-Callao and Asif Faiz..
Early paper on the impact of roughness on vehicle operating costs.
One of the very early papers putting forward the integrated framework of considering road user costs in the planning of road investments.
Written by Jan de Weille, this is the foundational report for the World Bank\'s future efforts to develop HDM and other management systems. It describes the basis for quantifying road user costs as well as the early research that was available.