Economic, Environmental and Social Impacts of Changes in Maintenance Spend on Local Roads in Scotland

6 Impacts of Carriageway Conditions

6.1 Overview of methodology

This Section provides an overview of the methodologies adopted for evaluating the economic impacts arising from changes in the Local Authority carriageway condition under the different budget scenarios.

If carriageway conditions deteriorate due to reduced funding, the following impacts may be experienced by the user:

  • Vehicle operating costs increase. For example, cars consume more fuel on rougher roads and might need more repairs due to increased damage (e.g. from potholes). The effect has been well documented and is the basis of such models as the World Bank's HDM-4 model (Watanada, Harral, Paterson, Dhareshwar, Bhandari, & Tsunokawa, 1987) and standard UK cost benefit analysis as defined by STAG (Transport Scotland, 2011a) (Transport Scotland, 2011a) and DfT (Department for Transport, 2011b).
  • Travel times increase. As the road surface condition deteriorates, it has been noted that vehicle speeds reduce (Cooper, Jordan, & Young, 1980). The effect will vary depending on the original design speed of a road. Evidence from the UK has been used to determine the significance of the effect on the Scottish local road network.
  • Accidents increase if the skid resistance of road surfaces deteriorates Most national highway Authorities in the developed world, including Transport Scotland and the other UK national road agencies, and some local road Authorities monitor skid resistance and implement skid resistance management strategies. The justification of such strategies is documented evidence of increased skidding accidents on lower quality road surfaces in wet conditions (Wilde & Viner, 2001). Evidence from the UK has been used to assess if this effect might apply an issue on the Scottish local road network.

Further details of the literature and the detailed background to the above effects are provided in the detailed report on the associated trunk road study (Transport Scotland, 2011b) (Transport Scotland, 2011b).

SCOTS has previously commissioned analyses for predicting road condition based on different budget scenarios for the Scottish local road network (SCOTS, 2010). These predictions were used with other information (e.g. traffic volumes across the network) to assess the impact on vehicle operating costs and travel time.

The analysis used data from 8 of the 32 Scottish Local Authorities. Detailed results from the analysis of these 8 Authorities can be found in Appendix F, Appendix G and Appendix H. To illustrate the analysis methodology are given and issues of interpretation, results from a single Local Authority (Fife) are described in this Section. Where appropriate, comments are also provided on key differences between the results for Fife and the results for any of the other Authorities in the sample.

The impact of accidents due to skid resistance was assessed using a separate methodology as described in Section 6.4 using skid resistance information from three Local Authorities (not included in the sample of 8 Authorities used for the economic analyses).

The final stage of the analysis included the scaling-up of the results from the 8 Local Authorities to provide results representative of the entire Scottish Local Authority network. The scaling-up methodology is described in Section 12 and Appendix L.

6.2 Surface conditions and vehicle operating costs

Road surface roughness has an impact on vehicle operating costs. Increasing roughness causes additional wear and tear to vehicle suspensions and tyres and affects vehicle fuel consumption and vehicle depreciation. HDM-4 (Watanada, Harral, Paterson, Dhareshwar, Bhandari, & Tsunokawa, 1987) models the relationships between road roughness, vehicle operating costs (VOCs), fuel consumption and vehicle emissions.

In most studies, the cost of vehicle depreciation is subsumed within the overall vehicle operating costs as it is usually considered a relatively small contribution to total vehicle operating costs. In the ISOHDM study on road user effects (Bennett & Greenwood, 2004) included the development of models specifically for depreciation, based on various international studies and supplementing these with realistic assumptions where the appropriate research data did not exist. The models showed that up to roughness levels of 5 IRI (equivalent to 5.5 mm2 3m LPV) there is no significant change in vehicle depreciation for any vehicle type.

Based on the projected road conditions predicted in this study for all the proposed budget levels, it was concluded that any changes in vehicle depreciation due to changes in investment levels on the network are negligible. The analysis was therefore concerned mainly with the change in fuel consumption of vehicles as road condition changes.

For this study HDM-4 was used to determine the economic costs of individual vehicle travelling over 1km of road in different conditions. This data was used together with the traffic data for the road networks (in vehicle kilometres travelled) and the distribution of the network in different roughness conditions for each of the modelled years in the analysis under the 3 budget scenarios, to determine the total VOCs for each of the 8 sample Authorities.

The HDM-4 analyses took no account of improvements in engine efficiency through the analysis period. The efficiency improvement factors given by the Department for Transport (Department for Transport, 2010) have been applied to the results from the HDM-4 analyses.

Full details of this analysis are given in Appendix F, which includes results from all 8 sample Authorities. The results from the analysis for Fife are shown in Figure 6.1.

Figure 6.1 shows how the increase in road surface roughness due to bigger reductions in funding (e.g. 40% reduction) leads to increased VOCs and as maintenance funding increases after 2020, the differences between the Scenarios reduces. The growth in traffic flow is a major contributor to the overall increase in VOCs through the analysis period for all 3 Scenarios.

Figure 6.1 Vehicle Operating Costs - Fife
(2002 prices undiscounted)

Figure 6.1 Vehicle Operating Costs - Fife

6.3 Surface conditions and travel time costs

In the analysis of the Scottish trunk road network one of the economic impacts evaluated was the travel time costs incurred due to small changes in vehicle speed as a consequence of differences in carriageway condition.

At the 1987 World Road Congress, the World Road Association (PIARC) (World Road Association, 1987) reported the effect of pavement surface condition on vehicle speeds as:

  • An increase in macrotexture and the lower orders of megatexture generally induces the driver to reduce speed; and
  • Increases in megatexture and greater roughness, or the incidence of loose gravel or deep snow or mud, frequently have the effect of inducing the driver to reduce speed to below 50 km/h.

Studies in Sweden by (Linderoth, 1981), (Wretling, 1996) and (Anund, 1992) investigated the relationship between road surface condition and travel speed using a sample of resurfaced roads and a control group. Linderoth and Wretling concluded that there was no evidence of reduced speed due to roughness. Anund showed that there was a statistically significant speed reduction of 1.6 km/h for passenger cars travelling in the evening and at night if the rut depth increased by 10 mm, and a reduction of 2.2 km/h for an increase of 1 IRI. The corresponding figures during day time 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.

For both the trunk and local roads analyses the results of research carried out by TRL was used (Cooper, Jordan, & Young, 1980). That study concluded that following the resurfacing of a trunk road, under free flowing traffic conditions, there is a small, but measurable, increase in the speed of vehicles of between 2 and 2.6km/h depending on vehicle type.

In extending this analysis to the Local Authority road network where road alignment, the number of junctions and lower speed limits have increased influences on vehicle speeds compared to trunk roads, the application of the methodology used for the trunk road analysis has been considered to be inappropriate for B, C and U class roads. Therefore the methodology is restricted in scope to consideration of A class local roads only.

The HDM-4 model also includes a vehicle speed road condition relationship and allows for the input of crew cost data to calculate the costs arising from speed changes due to changes in road condition. To avoid double counting the speed effects within the HDM-4 analysis the crew costs for all vehicle types used in HDM-4 were set to zero.

Full details of this analysis can be found in Appendix G, which includes results from all 8 sample Authorities. The results from Fife are shown in Figure 6.2.

Comparing the results from the 8 Authorities it is evident that Fife and Edinburgh show a bigger change in travel time for budget Scenarios 2 and 3 compared to the base scenario (Scenario 1).

Figure 6.2 Travel time costs for Fife
(2002 prices undiscounted)

Figure 6.2 Travel time costs for Fife

The shapes of the curves for the 3 Scenarios again show how the bigger budget reductions have a clear impact up to 2020 but in the period 2020-2030 when funding recovers to the 2010/11 level and above, the differences in cost due to increased travel time are reduced. Although funding levels in Scenarios 2 and 3 return to the level of Scenario 1 by 2025 and exceed Scenario 1 funding for the last 5 years of the analysis period, the travel time costs for Scenarios 2 and 3 remain significantly higher than those for Scenario 1.

6.4 Skid resistance and accident costs

The relationship between skid resistance, site accident risk rating and skidding accident rates has been well established in the UK and shows lower skid resistance tends to correlate with an increased accident rates. 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).

Comparative friction data over a wide range of surfaces, with a range of skid resistance and texture characteristics shows that higher risk sites have higher proportions of accidents above a skid friction coefficient (SFC) of 0.35 than is the case for risk category 1 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 Standardised Mean 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 the trunk road network in England (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.

Earlier studies, (Parry & Viner, 2005) and (Viner, Sinhal, & Parry, 2005), summarised the current position for Motorways in which 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 lengths, 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 lengths showed a continuous increase in accident risk with decreasing skid resistance. For local roads, the network is predominantly single carriageway roads.

6.4.1 Summary of trunk road methodology for skid resistance

Transport Scotland has monitored skid resistance on a routine basis for a number of years on trunk roads. In the earlier trunk road study, data analysis showed that:

  • Skid resistance significantly improved between 2001 and 2005, since when it has shown little change and no further significant trends
  • Average annual road surfacing budgets (in real terms) were higher in the early half of the 2000s than the latter half

Using the available evidence on the risk of accident occurrence on roads with differing skid conditions (Coyle & Viner, 2009) and data on the total number of wet road accidents in recent years on the trunk road network, a broad estimate of potential accident costs under different future funding scenarios was produced. A similar approach was sought for the local road network.

6.4.2 Assessment of local road skid resistance data

The measurement of skidding resistance by SCRIM is not a routine survey on the Local Authority network. After canvassing the Local Authorities for available data, SCRIM data was provided from three Scottish Local Authorities (Aberdeenshire, Fife and Angus) and used to assess the effects of changes in skid resistance. A summary of the data available is given in Table 6.1.

Table 6.1 Summary of available SCRIM data
Year Available SCRIM data by road class
Aberdeenshire Fife Angus
2005 A, B, C and U
2006 A, B, C and U A, B and U
2007 A, B, C and U A and U
2008 A, B and C A and U
2009 A, B, C and U A, B C and U A, B and U
2010 A, B, C and U A, B C and U A, B and U

The data for all road types was a sample of each network only. Although data was provided for U roads, the coverage was low and prevented drawing reliable conclusions. It was therefore decided that focusing on the data for the A, B and C roads was appropriate for this study. Figure 6.3 shows the trends for the three networks.

Trends in the data are less pronounced than for the trunk road network. There is some evidence of an underlying trend affecting all three networks in a similar way, which is reasonable given that skid resistance data is affected by seasonal variation and the data is from Local Authorities all on the east coast of Scotland with similar climatic conditions. However, overall the pattern of behaviour is not robust and it is not considered that any significant conclusions can be drawn on any increasing or decreasing trends.

It was therefore concluded that no quantitative analysis or conclusions can be drawn from the limited skid resistance data available for this study.

In terms of qualitative conclusions, it is still reasonable to assume that experience of other Authorities (that targeting poor skid resistance reduces the number of accidents due to wet road skid resistance) will be the same for the local road network. Reductions in maintenance budgets will therefore strain the ability of Authorities to manage skid resistance (even if the reduced maintenance funding is targeted to address safety as the highest priority) and, therefore, the reductions represent a lost opportunity for improvements in road safety.

Figure 6.3 Trends in deficient skid resistance for 3 Local Authorities

Figure 6.3 Trends in deficient skid resistance for 3 Local Authorities

6.5 Ride quality

As road condition deteriorates, the vehicle ride quality may also reduce. Poor ride quality is not currently a major factor with road users for current levels of network condition. However, studies have been undertaken into the effect of poor ride quality on professional drivers.

In addition to road condition and ride quality, these studies have considered other factors that may affect drivers such as prolonged use of poor quality seats and exposure to noise. It is recognised that vibrations affect drivers but no information could be found on the incremental effect of surface unevenness (e.g. potholes) on drivers.

The European Agency for Safety and Health at Work (EU-OSHA) (European Agency for Safety and Health at Work (EU-OSHA), 2011) recognises the potential effects on long-distance professional drivers and includes the effect of exposure to vibrations (ride quality) in the list of main physical hazards and risks. It was considered that the levels of road condition predicted for the scenarios in this study will not lead to severe changes in ride quality, particularly for HGVs, and therefore little change in risk to drivers from overall levels of condition. With reductions in maintenance funding it is, however, likely that there will be an increase in the occurrence of small areas of rapid deterioration (e.g. potholes) and these may increase the levels of vibration for drivers if the defects are not repaired quickly.