Economic, Environmental and Social Impacts of Changes in Maintenance Spend on Local Roads in Scotland
Appendix F Network Pavement Model and Vehicle Operating Cost Analyses
F.1 Background and pre-analysis steps
WDM Ltd was contracted by Transport Scotland to run the pavement model to predict the condition of the road networks in 8 sample Authorities (Aberdeenshire, Dumfries and Galloway, City of Edinburgh, Fife, Glasgow City, Highland, North Lanarkshire and South Ayrshire) under the 3 different budget Scenarios. WDM provided outputs for the years 2010, 2013, 2017, 2020, 2025 and 2030 and included:
- Predicted carriageway condition parameters including longitudinal profile variance and rutting for each 10m carriageway length in the asset database for the 8 Authorities
- An accumulated depreciation for each of the 8 Local Authorities
- Treatment types, lengths and areas for each of the time intervals used in the model (i.e. 2010 to 2012, 2013 to 2016, 2017 to 2019, 2020 to 2024 and 2025 to 2029).
The economic analysis based on the condition of the carriageway was driven by the longitudinal profile variance of the network and the traffic flow over the network. The following methodology was used to process the projected network condition data, provided by WDM, and the network traffic data.
- Using the projected 3m Longitudinal Profile Variance (LPV) data the International Roughness Index (IRI) was calculated for each 10m surveyed length using the same relationship that was used for the trunk road analysis (Alonso, 2001).
- The calculated IRI values were then rounded into 0.5 IRI increments (maximum of IRI 10, minimum of 1). Sections with an IRI > 10 were set to 10 and those with an IRI < 1 were set to 1.
- The proportion of the surveyed network by road type, road environment and Local Authority by the rounded IRI condition established in step 2 was calculated.
- The traffic flows for each Local Authority was taken from the traffic count vehicle kilometre travelled data in 2009. The proportion of traffic by road class, road environment and vehicle type was then used to determine a traffic flow for each of the sample Authorities by road class (i.e. A, B, C or Unclassified), road environment (i.e. rural or urban) and vehicle type (car, van, bus and HGV).
- For each Local Authority, vehicle type, road class and road environment the base traffic data calculated in step 5 was grown using traffic growth forecasts found in the National Road Traffic Forecast, to calculate the traffic in each of the analysis time-steps (2010, 2013, 2017, 2020, 2025 and 2030).
- Using the network proportions by condition calculated in step 3 together with the appropriate traffic flow for each time-step, Authority, road type and road environment, calculated in step 5, the number of vehicle kilometres travelled over different network condition in each time step for each vehicle type, road type and road environment was calculated.
The resulting vehicle proportions were used as the basis of the calculation of vehicle operating cost; travel time delay; and CO2 emissions from vehicles under normal running conditions.
F.2 Predicted maintenance and condition
The results of the analyses of wider economic impacts are dependent on the predicted outcomes from the condition prediction modelling. The details of the rules and algorithms are not known but some observed characteristics of the modelling may impact on the results of the analyses.
After treatment the treated lengths are placed in a pavement category with very long life. These lengths have, typically, 50 years life so do not get considered again in the analysis period but the long life may affect the depreciation calculation.
There is no consideration of unexpected performance of urban roads due to utility works so this is likely to over-estimate the lives of maintenance treatments in urban areas.
Similarly winter maintenance is not included and sudden cold winters can also reduce the carriageway lives.
Plots of the projected pavement condition from the WDM condition projection outputs of all 8 sample authorities are shown in Figure F.1 to Figure F.3 for 3mLPV and Figure F.4 to Figure F.6 for rutting. The projected 3mLPV condition is a key parameter in the economic analysis carried out in this study.
Figure F.1 Distribution of 3mLPV for the 8 Sample Authorities and Scenario 1
Figure F.2 Distribution of 3mLPV for the 8 Sample Authorities and Scenario 2
Figure F.3 Distribution of 3mLPV for the 8 Sample Authorities and Scenario 3
Figure F.4 Distribution of rut depth for the 8 Sample Authorities and Scenario 1
Figure F.5 Distribution of rut depth for the 8 Sample Authorities and Scenario 2
Figure F.6 Distribution of rut depth for the 8 Sample Authorities and Scenario 3
F.3 Vehicle operating costs
The HDM-4[9] 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 VOCs 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 road network that will have any impact on VOCs.
Using the HDM-4 model 19 notional 1km road lengths were defined with different levels of roughness ranging from IRI 1 to 10 in 0.5 IRI increments. Four vehicle types (car, van, bus and truck) with appropriate economic values were defined in the HDM-4 model to represent the different types of vehicle on the Local Authority network based on published data and consultation with TRL experts and the outputs from the literature review. The parameters used for each vehicle type are shown in Table F.1.
Speeds appropriate for the vehicles travelling on Local Authority roads, shown in Table G.1, were also set in the HDM-4 model. The crew cost parameter was set to zero for the purposes of this analysis to avoid double counting with the travel time delay analysis, which was carried out separately.
All the modelled road sections were of asphalt construction, had a width of 7.5m, a Rise and Fall of 10m/km and a curvature of 15 degrees/km. A further set of 3 lengths were modelled with a surface dressing wearing course to investigate the sensitivity of the model outputs to the different material type. No significant changes in the results of the economic analysis were found due to the change in surfacing type and as a result one surfacing type has been used in this analysis.
| Parameter | Car | Light Goods Vehicle (LGV) | HGV | Public Service Vehicle (PSV) |
|---|---|---|---|---|
| Passenger car equivalent | 1 | 1 | 1.8 | 1.5 |
| No. of wheels | 4 | 4 | 12 | 6 |
| No. of axles | 2 | 2 | 5 | 2 |
| Tyre type | Radial ply | Radial ply | Radial ply | Radial ply |
| Tyre Retread cost (%) | 20 | 30 | 50 | 50 |
| Annual kilometres | 10000 | 20500 | 60000 | 45000 |
| Working hours | 700 | 2250 | 3500 | 4000 |
| Average life (yrs) | 14 | 9 | 10 | 10 |
| Private use (%) | 100 | 0 | 0 | 0 |
| Passengers (persons) | 1 | 0 | 0 | 50 |
| Work related passenger trips (%) | 75 | 0 | 0 | 75 |
| Operating weight (t) | 1 | 2.5 | 44 | 7.5 |
| New vehicle cost (£) | 13600 | 18000 | 56000 | 36000 |
| Replacement tyre cost (£) | 36 | 64 | 336 | 200 |
| Fuel cost (£ per litre) | 0.46 | 0.48 | 0.48 | 0.48 |
| Lubricating oil cost (£ per litre) | 4 | 12 | 4 | 4 |
Note: Costs are economic costs
HDM-4 was run for each of the vehicles travelling over each of the 1km lengths to evaluate the economic costs associated with each vehicle type.
To take account of vehicle engine efficiency improvements and predicted growth in the resource cost of fuel, the fuel costs in the HDM-4 outputs were replaced with values calculated from:
- The amount of fuel used given in the outputs from the HDM-4 modelling, modified for each year of the analysis based on the assumed vehicle fuel efficiency improvements given in webTAG unit 3.5.6 (Department for Transport, 2011a).
- The resource cost of fuel based on the vehicle type, taking into account the proportion of the vehicle type using petrol or diesel and the growth forecast for the resource costs of petrol and diesel given in webTAG unit 3.5.6. (Department for Transport, 2011a)
Using the calculated vehicle operating costs per vehicle kilometre together with the vehicle kilometre proportions calculated using the methodology in Section F.1 the total vehicle operating costs for each of the 8 sample Local Authority road networks was calculated for 2010, 2013, 2017, 2020, 2025 and 2030 under the 3 budget scenarios.
The vehicle operating costs for each of the modelled years under the 20% and 40% budget cut Scenarios were then linearly interpolated and extrapolated to represent a 35% and 69% budget cut to account for the fact that the WDM model runs were carried out using a 20% and 40% budget cut, but the subjective budget analysis identified that these would be equivalent to a 35% and 69% budget cut in carriageway maintenance respectively.
To calculate VOCs for the intermediate years between the modelled years the results of the analysis were linearly interpolated.
F.4 Results of vehicle operating cost analysis
The results from the VOC analysis are shown in Figure F.7 to Figure F.14. They show the vehicle operating costs increasing with time, which is consistent with the growth in traffic. There is little difference between the VOCs for the different budget scenarios, but in every case the vehicle operating costs for Scenario 1 (no budget cut) are less than those for budget Scenario 2, which are less than those for budget Scenario 3.
Figure F.7 Aberdeenshire vehicle operating costs
(2002 prices undiscounted)
Figure F.8 Dumfries and Galloway vehicle operating costs
(2002 prices undiscounted)
Figure F.9 City of Edinburgh vehicle operating costs
(2002 prices undiscounted)
Figure F.10 Fife vehicle operating costs
(2002 prices undiscounted)
Figure F.11 Glasgow City vehicle operating costs
(2002 prices undiscounted)
Figure F.12 Highland vehicle operating costs
(2002 prices undiscounted)
Figure F.13 North Lanarkshire vehicle operating costs
(2002 prices undiscounted)
Figure F.14 South Ayrshire vehicle operating costs
(2002 prices undiscounted)
F.5 Effect of discounting
The effect of discounting the vehicle operating costs can be seen in Figure F.15 for Fife. The discounted values show the Net Present Value of the vehicle operating costs decreasing with time. This behaviour was common to all 8 sample Authorities.
Figure F.15 Fife vehicle operating costs
(2002 prices discounted)
