Hdm-4 Software Price

[Griselda Humbarger]

Ingeneral, for countries where roads have historically been built and maintained to deliver smooth travel over the life of the pavement, maintenance decision making has focused on parameters other than road roughness (e.g. travel time impact of roadworks, skid resistance). For countries where rougher roads have been more common, the effects of road roughness have been pivotal in the maintenance decision making framework. Countries where roughness has a more significant role in decision making tend to have networks with a significant proportion of the network with roughness greater than 5 or 6 IRI (International Roughness Index).

The World Bank HDM model (Watanatada, Harral, Paterson, Dhareshwar, Bhandari, & Tsunokawa, 1987) (Watanatada, Harral, Paterson, Dhareshwar, Bhandari, & Tsunokawa, 1987) and other models such as the TRL RTIM (Robinson, Hide, Hodges, Rolt, & Abaynayaka, 1975) were originally developed for roads in developing countries and had a key focus on road roughness impacts on the road user and vehicle operating costs. They have since been upgraded and extended (e.g. the HDM-4 model is used in Eastern Europe and preliminary analyses have been carried out for local roads in England) but the basic conceptual frameworks remain similar. The logic of these models is a link from road condition (predominantly summarised by road roughness) through to road user costs in terms of vehicle operating costs and travel time. These effects are derived albeit at higher levels of roughness than currently experienced on the strategic road networks in the UK using established relationships that, in practice, would be calibrated for the road network under consideration.

Vehicle operating costs are a summation of fuel and engine oil consumption, tyre use, vehicle depreciation and maintenance and repair costs. In terms of modelling impacts of road maintenance, the models depend primarily on road roughness changes as other road conditions (e.g. curvature, rise and fall) will not be affected by changes in maintenance policy, or their impact is second order.

Road roughness does have an effect on vehicle travel speeds in so far as road users will travel at lower speeds on roads which are in a worse condition. The HDM model identified this effect from the studies in the 1970s on experimental road sections in very poor condition and HDM-4 updated the relationships in the 1990s. The model shows variations of between 0.62 and 2.57 km/h reduction in speed per 1 IRI increase in roughness (this is equivalent to changes of around 1.5mm2 in 3m wavelength Longitudinal Profile Variance, LPV, at base ride quality levels of 4 mm2 LPV).

Little has been reported on how surface conditions affect travel time. Vehicle drivers may choose to drive more slowly over a surface that has deteriorated than they would over a more even surface. However, it has been postulated that with modern vehicles the effects are reduced and vehicle speeds are maintained but with higher operating costs.

An early study (Cooper, Jordan, & Young, 1980) gathered vehicle speed data for three sites in England due to be resurfaced. At two of the three sites the surface unevenness showed little change before and after resurfacing. At the third site a statistically significantly increase in the traffic mean speed levels was seen following reconstruction of the road. The observed increases in the mean speed after resurfacing were 2 km/h for private cars, 2.3 km/h for light goods vehicles, 2 km/h for medium goods vehicles, and 2.6 km/h for heavy goods vehicles.

Studies in Sweden by (Linderoth, 1981) and (Wretling, 1996) investigated the relationship between road surface condition and travel speed using a sample of resurfaced roads and a control group. They concluded that there was no evidence of reduced speed due to roughness. (Wretling, 1996) described another Swedish study by (Anund, 1992) that investigated the relationship between surface quality (measured in IRI) and vehicle speed. The results showed that there was a statistically significant speed reduction of 1.6 km/h for passenger cars between 3.00 p.m. and 9.00 a.m. if the rut depth increased by 10 mm, and a reduction of 2.2 km/h for an increase of 1 IRI. The corresponding values during 9.00 a.m. and 3.00 p.m. were 1.9 km/h and 3.0 km/h. For trucks with and without trailers, no significant speed reduction with increased roughness or rut depth was found. The results of those studies support a significant reduction in vehicle speed only when road condition deteriorates beyond some critical level that is rougher than the general level of condition of the trunk road network in Scotland.

The relationship between skid resistance, site accident risk rating and skidding accident rates is well established in the UK. Many factors influence the rate or risk of accidents, including skid resistance/texture depth, and other road condition factors such as unevenness and ruts (Wilde & Viner, 2001).

An investigation by (Viner, Sinhal, & Parry, 2005) provided comparative friction data over a wide range of surfaces, with a range of skid resistance and texture characteristics. The data also showed that higher risk sites have higher proportions of accidents above a Sideway-Force Coefficient (SFC) of 0.35 than is the case for low risk category sites.

The research also confirmed the necessity of maintaining an adequate level of texture depth to ensure good high-speed friction and the data showed that a texture of at least 0.7mm Sensor Measured Texture Depth (SMTD) was desirable. The results also demonstrated the declining benefits of continuing to increase the texture depth above an adequate level of approximately 1.25mm SMTD.

A large-scale study of the link between skid resistance and personal injury accidents, based on 1000km of road network (Rogers & Gargett, 1991), confirmed the different levels of accident risk for different types of road site and the increase in risk for sites with lower skid resistance.

In general, summarised by (Viner, Sinhal, & Parry, 2005), it has been found that for Motorways, the overall trend with skid resistance is very flat except for the lowest levels of skid resistance. For dual carriageways the results showed there is a statistically significant trend for accident risk to increase at locations with lower skid resistance. For single carriageway non-event sections, the trend was both stronger and more significant and the trend was stronger when considering only wet or skidding accidents. The trend for single carriageway non-event sections showed a continuous increase in accident risk with decreasing skid resistance.

To derive vehicle operating costs from the HDM-4 relationships it is necessary to translate pavement condition (remaining life) into IRI. This was achieved by translating remaining life into 3m LPV and then, a further conversion from 3m LPV to IRI.

Many of the economic impacts (i.e. carbon emissions, vehicle operating costs and travel time) are driven by pavement roughness. As noted earlier, the projected pavement condition data for various pavement budget scenarios was given as the length of network for each road type (Motorway, dual APTR and single APTR) by remaining life: ranging from 0 to 50 years. Figure 7.1 shows the steps taken to translate the projected remaining life data to 3m LPV.

In order to develop a relationship between remaining life and 3m LPV, condition data for 300 sections, 10m in length, of each road type (Motorway, dual APTR, single APTR) were extracted from the Transport Scotland database. Using the methodology for calculating RCI from 10m condition data (Transport Scotland, 2007a) the RCI for each section was calculated. A more recent RCI calculation methodology was available for Transport Scotland but an earlier version used for the Transport Scotland pavement model was considered sufficiently reliable for this study.

From the RCI value for each 10m length a linear regression between RCI and 3m LPV data provided relationships (for each road type independently) suitable for use in this study. It should be noted that since RCI and 3m LPV are not independent variables this methodology was not adopted to prove a statistical correlation, but did yield the simple linear conversion shown in Equation 2 to translate RCI data for each of the different road types to 3m LPV. Some outlier data points were removed from the analysis (i.e. 3m LPV > 40mm2) to improve the correlation.

The remaining life data from the projected network condition was converted to RCI using the relationship shown in Equation 1. The RCI data was further converted to 3m LPV using the relationships shown in Equation 2. Note that the R2 value from the analysis for only single carriageways was particularly poor so the relationship derived for dual carriageway APTRs was used for all APTRs.

To assign the traffic to the network it was assumed that the traffic is distributed evenly over the different lengths of the network in different levels of 3m LPV (i.e. there is no significant avoidance by road users of roads in poor condition or attraction of roads in good condition). This assumption was necessary as the projected condition data does not represent the actual' network (i.e. the projected condition data does not show route information for the lengths of the network in each remaining life condition band). To undertake a more detailed analysis the condition would need to be projected using the actual road network and associated condition data.

To use IRI in the economic relationships in HDM-4, a further conversion was required to convert the 3m LPV data to IRI. A provisional transformation has been derived in the European FILTER study (Alonso, 2001) and is provided in Appendix D with other default parameters used in this study.

The HDM-4 [5] model includes modules to calculate vehicle operating costs (VOCs) and vehicle emissions and was considered to be an appropriate tool for this analysis. Typically HDM-4 is not used in the UK as the road network is, by international standards, relatively smooth and vehicle operating costs are not sensitive to roughness until the pavement has an IRI of around 4 or 5. Based on this threshold it is only the worst parts, in terms of longitudinal profile variance, of the Scottish trunk road network that will have any impact on vehicle operating costs.

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