# Benchmarking Highways England

2018 Progress Report 31 January 2019 ORR logo

## Contents

| Executive Summary                            |     |   3 |
|----------------------------------------------|-----|-----|
|                                              |     |     |
| Introduction                                 |     |   4 |
|                                              |     |     |
| Regional comparisons                         | 5   |     |
|                                              |     |     |
| International and cross-sectoral comparisons | 20  |     |
|                                              |     |     |
| Annex A - Regional dashboards                |     |  25 |
|                                              |     |     |

## Executive Summary

Benchmarking Highways England's performance and efficiency is an important element of our role. It helps us monitor Highways England's delivery of the Road Investment Strategy; 
will inform our advice on future strategies; and increases transparency around Highways England's performance. 

We have been collecting consistent regional performance and spending data for three years and can now begin to see trends and patterns. Differences in performance across regions are generally relatively small. The main exception to this is average delay, where there are bigger differences between regions. Where there are regional differences in performance, we have seen evidence of Highways England taking action to address the issue. For example, it has improved incident clearance in what were the worst performing regions. And, while satisfaction is considerably lower in the North West than elsewhere, Highways England is implementing plans aimed at improving user satisfaction in the region. Highways England's average maintenance costs per lane mile, and the differences in maintenance spending between regions, both increased in 2017-18. On its own, this does not necessarily indicate falling efficiency. A fuller understanding of what is happening with Highways England's maintenance efficiency requires good top-down and bottom-up evidence. We are working with Highways England to develop more detailed data and analysis to improve its understanding of its maintenance costs and efficiency. This forms part of Highways England's own programme of benchmarking work. It is also developing its own programme of regional operational benchmarking and has completed a benchmarking exercise of its corporate services. 

Over the last year, we have looked in detail at Highways England's pavement condition and incident clearance key performance indicators, comparing its approach with other road operators. In both areas, Highways England's processes compare well with others. But there are limited opportunities for direct benchmarking of outcomes as different road operators tend to use different indicators or metrics to monitor performance. 

We expect evidence from both of our benchmarking programmes to inform Highways England's plans for the second road period that we will be reviewing in our Efficiency Review in 2019. This will be a significant focus of our work next year. We also plan to build on our work on pavement condition, looking at the feasibility of directly comparing the condition of roads across networks. And to continue our longer-term work with Highways England to establish wider comparators for benchmarking its efficiency.  
Benchmarking Highways England  

## Introduction

1.
This is the third in our series of annual progress reports on our work benchmarking Highways England. This work is important for a number of reasons. Our benchmarking work:

will form part of our evidence when we review Highways England's plans for the second road period;

can identify improvements that Highways England can implement to deliver better outcomes for the strategic road network's (SRN's) users and funders; and

improves the transparency around Highways England's performance and how this varies across the SRN.
2.
We now have three years of consistent regional performance and spending data. This means we can begin to see trends and patterns. But we expect the value of this analysis to increase over time as a longer time-series gives us greater insight.
3.
We have continued to explore opportunities to benchmark Highways England's processes and performance in certain areas against other countries and sectors. It is important for us to work with colleagues in Highways England on these projects, as they are in the position to deliver improvements that benefit road users. Alongside this report we have published two such studies, looking at how pavement (road surface) condition is measured and how incidents and unplanned disruption are managed.
4.
Highways England is also leading its own benchmarking work. It is developing a programme of regional operational benchmarking; collecting a more detailed dataset of maintenance spending in its areas; and has completed a benchmarking exercise of its corporate services.
5.
We expect evidence from both of our benchmarking programmes to inform Highways England's plans for the second road period that we will be reviewing in our Efficiency Review in 2019.

## Regional Comparisons Regional Performance Against Key Performance Indicators

6.
In this section we show how Highways England's performance against a subset of its key performance indicators (KPIs) varied across its regions in 2017-18. Figure 1
shows the five KPIs included in this analysis.
7.
The maps in figure 2 show how performance against these five KPIs varied in 2017-18. The colour-coding is based around Highways England's national targets for these KPIs as there are no regional targets. So it is expected that there might be
some regional variation as Highways England delivers against its national targets.
8.
The differences between regions are generally relatively small and reflect Highways England's national performance. This is particularly the case for network availability and incident clearance, with all regions exceeding the national target. There are two main exceptions:

average delay, where there is generally more variation across the regions; and

user satisfaction, where the North West was an outlier from the other regions in 2017-18.

## Network Availability And Incident Clearance

9.
Every region has exceeded the target to keep at least 97% of the network available for traffic in each of the first three years of the road period. By contrast, figure 3 shows that the percentage of incidents cleared in less than an hour was below the national target of 85% in the East and South West in the first year of the road period. But they are now two of the top performing regions in this area, and this has helped drive stronger performance at the national level.
10.
The next section of this report includes a summary of a project that looked at how Highways England manages incidents and unplanned disruption. In that section, we discuss in more detail some of the actions that Highways England has taken to improve performance in this area.

## User Satisfaction

11.
There is more regional variation in the other three KPIs. User satisfaction in most
regions in 2017-18 was at, or relatively close to, the national target of 90%. But it was
substantially lower in the North West, at 78%.
12.
It is difficult to identify drivers of satisfaction from the National Road Users' Satisfaction Survey (NRUSS), particularly at a regional level. But Highways England identified the following factors as contributing to lower satisfaction in the North West:

a high concentration of, and delays to, major schemes;

above average levels of commuters (who tend to be less satisfied); and

fewer variable message signs.
13.
Highways England is implementing plans aimed at improving user satisfaction in this region. And we have seen evidence from mid-year NRUSS results and the new
Strategic Roads User Survey (SRUS) that the gap in user satisfaction is narrowing in 2018-19.

## Pavement Condition

14.
2017-18 was the first year that Highways England met the national target for its
pavement (road surface) condition KPI. Figure 5 shows the regional breakdown of this KPI from 2015-16 to 2017-18, highlighting changes between the first and third years of the road period. Pavement condition has improved in all regions and differences between them are relatively small. But, pavement condition was below
the national target in the East and Midlands in 2017-18. As noted above, we might expect this sort of regional variation when Highways England is delivering against national targets.

## Average Delay

15.
With three years of data we can begin to see trends in regional average delay levels,
as well as the simple differences between regions. Figure 6 shows a mixed picture,
with delays appearing to increase in three of Highways England's regions, and falling in the other three.
16.
Highways England does not have a target for average delay and a range of factors
likely affect this KPI, including: traffic levels, roadworks, incidents (and incident
clearance), and the make-up of the network. Taken together, this makes regional performance difficult to assess.
17.
There are likely to be links between the different elements of Highways England's performance specification. For example, the North West has the highest levels of delay and the lowest levels of user satisfaction. And the reductions in average delay in Yorkshire and North East have coincided with increased network availability in that region. However, we have not found robust statistical relationships between these KPIs when looking across all six regions and three years of data.

## Regional Dashboards

18.
The following pages show dashboards that combine performance data from the first three years of the road period with data on the make-up of the network, traffic levels
and Highways England's maintenance spending in each region. The data for the
performance radar charts have been adjusted so that a larger shape represents 'good' performance for all of the KPIs.
19.
Maintenance spending levels and possible implications for efficiency are discussed
more below the dashboards. Annex A provides more detail on data sources; how we present the data in the performance 'radar charts'; and how we have treated parts of the network managed under DBFO contracts.
20.
Highways England has put in place its own regional operational benchmarking programme. This covers a similar set of measures to the dashboards on the following pages. We plan to review how this is developing as part of a set of regular meetings with Highways England.

## Regional Stats East Traffic Density Regional Stats Midlands Traffic Density Regional Stats North West Traffic Density South East Traffic Density Regional Stats South West Traffic Density Yorkshire And North East Highways England'S Regional Maintenance Spending

21.
In last year's report we noted that average maintenance and renewal spending per
lane mile had fallen, and the differences between regions had narrowed. Figure 7 shows that this trend has reversed in 2017-18 - the average maintenance spend per
lane mile has increased and the differences between regions has widened. Capital renewal spending caused most of the increase in average spending and variation between regions.
22.
This analysis includes (resource) maintenance and (capital) renewal spending but does not include spending on major projects or operations. There is likely to be some
interaction between these different categories of spend. For example, Highways England might undertake additional renewal activity alongside major projects to reduce disruption for users.
23.
A high-level unit cost like this masks a lot of complexity. It does not take account of the amount of activity that is delivered for the spending, or the nature (or cost) of that
work. For example, the increase in spending could simply be the result of Highways England planning to undertake more renewals, or more expensive sorts of renewal
activity, this year than last. Forecasts for 2018-19 suggest that the average spending and regional differences will reduce to a similar to level to that seen in 2016-17. And year-on-year variation of this sort could simply reflect Highways England's approach to managing its maintenance and renewal activity within its five-year funding.
24.
Just as we could not conclude that last year's data definitively showed an improvement in efficiency, we cannot definitively conclude that this is evidence of
falling maintenance efficiency. A fuller understanding of what is happening with Highways England's maintenance efficiency requires good top-down and bottom-up evidence. It is important that Highways England can demonstrate that its plans are based around asset need, particularly in its planning for the second road period. 

## Developing Our Understanding Of Maintenance Efficiency

25.
To understand efficiency we need a deeper understanding of what drives
maintenance spending. This is very complex, with potential drivers including:

the types or roads and structures making up the network;

traffic levels (including by vehicle type);

the age and underlying condition of the network; weather, climate and topography;

local labour, plant and materials costs; and

the factors that matter most might be different for (resource) maintenance and (capital) renewal.
26.
Regulators, to varying degrees, have assessed the relative efficiency of regulated companies with econometric models of efficiency. Such models use data on outputs or cost drivers to explain the average relationship between these drivers and cost (of, for example, maintaining their network). This average relationship can be used to say what it 'should cost' for each firm (to maintain their network), and various statistical
methods can then be used to split the difference between the 'should' and actual costs into model 'noise' and efficiency.
27.
We used an aggregate measure of Highways England's regional maintenance and renewal spending to develop initial models of this sort. While this gave largely plausible results, the analysis was limited by the number of regions and potential cost drivers. And similar analysis in other sectors has typically used larger and longerestablished data sets.
28.
Highways England is now building up a more detailed data set that has several
advantages:

it is at area, rather than regional, level, which means there are more data that
can better support more detailed analysis;

spending can be split between (resource) maintenance and (capital) renewal, so that both separate and joint models can be developed; and

there is a wider range of potential cost drivers.
29.
We will continue to work with Highways England to develop its data collection and analysis. We expect developing robust efficiency models to be a long-term
undertaking. There will be more short-term benefits as Highways England collects better data and improves its understanding of the costs and drivers of its maintenance activities.

## International And Cross-Sectoral Comparisons

30.
In this section we provide a brief overview of several projects that have looked at
different activities and compared Highways England with other countries or sectors.
Reports from these projects are available on our website.1

## Measuring Pavement Condition

31.
We commissioned a study to consider how Highways England measures the condition of its pavement (road surface) assets and how this compares with other
road authorities in the UK and abroad. The objectives were to:

compare how Highways England measures and monitors its pavement condition with practice elsewhere; and

assess the extent to which we can benchmark Highways England's KPI and target for pavement condition against other networks.

Does the road authority have a similar metric / target to Highways England? 
Road authority 
Does the road authority measure the same pavement characteristics as Highways England? To a considerable degree 
Somewhat 
Local Authorities (Cornwall, South Lanarkshire) TfL 
Yes 
Somewhat 
Australia 
To a considerable degree 
No metric in place 
Austria 
Yes 
Two indicators but not similar 
Denmark 
Yes 
No metric in place 
Netherlands 
Yes 
Yes 
Scotland 
Yes 
Different metric, no target 
Wales 
To a considerable degree 
Different metric, no target 

32.
The project's broad conclusion is that, as shown in the middle column of table 1, most road authorities use similar methods and equipment to measure similar pavement characteristics. There are some differences in the detail of how they do
this but these are relatively minor. For example, Highways England's surveys are more frequent and comprehensive than most other road authorities. 

33.
Most road authorities use indicators to monitor pavement condition for internal management and planning purposes. But, as shown in the right-hand column of table
1, different road authorities have tended to develop different indicators or metrics. And only a few of the comparators have developed these into formal performance metrics.
34.
The Netherlands appears to be the best candidate as an external benchmark of
pavement condition. Local Authorities and the Devolved Administrations also offer potential benchmarks. The former use more similar data and metrics, while the latter manage networks that are more similar to the SRN. We are considering whether to
take forward a second project in this area, to assess in detail the feasibility of comparing pavement condition across these authorities.

## Highways England And Incident Management

35.
Following on from work we published last year looking at roadworks and planned disruption, we commissioned a study looking at how Highways England manages incidents and unplanned disruption on its network. The objectives of the study were to understand:

how Highways England, comparable road authorities and other relevant organisations manage, measure, target and incentivise clearing incidents from their network; and

the comparability of incident management performance measures used by Highways England and its comparators.
36.
The study found that Highways England is performing well in this area and employs many elements of good practice seen in the comparators. The incident clearance KPI is well understood across the company and its experienced and competent staff underpin its strong performance in this area.
37.
The study also identified opportunities for further improvement. Some of these relate to initiatives that are already underway, such as rolling out new incident management guidance across its regions. Other recommendations relate to stakeholder liaison - such as developing more consistent processes and relationships with local highways authorities - and to ensure that local good practice and innovation is harnessed
nationally.
38.
Highways England already has processes in place to disseminate best practice. For example, individual incidents that miss the target are reviewed routinely and regional
directors receive a comparison of performance across the network. Along with Highways England introducing an internal stretch target, these steps are likely to have contributed to the strong performance against the KPI target in this area. 

39.
As with pavement condition, there are common factors that many road authorities measure to monitor incident management. But table 2 shows that most comparators that have targets for specific metrics, target incident response times, rather than clearance times. This makes direct benchmarking of Highways England's performance challenging.

| Comparator                     | Target description           | Target level / range          |
|--------------------------------|------------------------------|-------------------------------|
| Midland Expressway (M6Toll)    | Response and clear up times  |                               |
| South Wales Trunk Road         |                              |                               |
| Agent                          |                              |                               |
| Response time                  | 80% within 20 minute         |                               |
| response between               |                              |                               |
| 0700-1900hrs                   |                              |                               |
| TfL                            | Incident resolution          | 90 mins to return network to  |
| normal operation               |                              |                               |
| ASFINAG (Austria)              | Average duration, number and |                               |
| average duration of complete   |                              |                               |
| closures, response time        |                              |                               |
| Vejdirektoratet (Denmark)      |                              |                               |
| KPIs include response times    |                              |                               |
| and average clearance times    |                              |                               |
| for major incidents            |                              |                               |
| Response times vary by road    |                              |                               |
| type and time of day: 20-45    |                              |                               |
| minutes on motorways and       |                              |                               |
| specifically defined roads     |                              |                               |
| Clearance times                | Queensland Department of     |                               |
| Transport and Main Roads       |                              |                               |
| (Australia)                    |                              |                               |
| MidLink M7M8 (Ireland)         | Response time                | 60 / 90 mins during / outside |
| working hours                  |                              |                               |
| Rijkswaterstaat (Netherlands)  | Arrival time after detection | 80% within 15 / 30 mins       |
| during / outside of rush hours |                              |                               |

## Scoping Options For Benchmarking Capital Efficiency

40.
To better understand our options ahead of our Efficiency Review, we commissioned a scoping exercise to assess options for benchmarking the efficiency of Highways England's capital spending. Based on case studies of approaches used in other regulated sectors, each option was considered for its feasibility (for RIS2 and in the
longer-term), robustness, data requirements and effort / cost. The options considered
and their assessment against feasibility, robustness and effort criteria are summarised in table 3. 

41.
We are using this work to develop our plans for our Efficiency Review of Highways England's proposals for the second road period.

| Option                               | Description    | Feasibility    |
|--------------------------------------|----------------|----------------|
| for RIS2                             |                |                |
| Robustness                           |                |                |
| for RIS2                             |                |                |
| Effort for                           |                |                |
| RIS2                                 |                |                |
| Medium                               | Medium*        | High           |
| benchmarking                         |                |                |
| Top-down benchmarking, including     |                |                |
| econometric and unit cost analysis,  |                |                |
| with:                                |                |                |
| Medium                               | Medium*        | High           |
|                                     |                |                |
| overseas National Road               |                |                |
| Authorities,                         |                |                |
| National                             |                |                |
| benchmarking                         |                |                |
|                                     |                |                |
| Local Road Authorities, and          |                |                |
| High                                 | Medium*        | Medium         |
| benchmarking                         |                |                |
|                                     |                |                |
| Highways England's regions /         |                |                |
| areas.                               |                |                |
| High                                 | Medium         | Medium         |
| Bottom up unit                       |                |                |
| cost analysis                        |                |                |
| Comparison of unit costs for         |                |                |
| specific activities against other    |                |                |
| construction, engineering or         |                |                |
| highways organisations.              |                |                |
| Unclear**                            | Medium         | Medium         |
| composition                          |                |                |
| Benchmarking proportion of project   |                |                |
| costs in categories such as design,  |                |                |
| project management, overhead,        |                |                |
| contingency etc.                     |                |                |
| High                                 | Medium         | Low            |
| trend analysis                       |                |                |
| Analysis of changes in productivity  |                |                |
| over time in firms or sectors of the |                |                |
| economy engaged in similar           |                |                |
| activities to Highways England       |                |                |
| High                                 | Medium         | Low            |
| costs versus project budgets.        |                |                |

* The robustness of international, national and internal benchmarking may be constrained for RIS2 by limitations in data availability/consistency, but robustness for these methods is more likely to be 'high' for RIS3. ** Despite uncertainties, experience from other sectors suggests this option may be feasible for RIS2.

## Corporate Services Benchmarking

42.
Highways England has completed a benchmarking exercise of its corporate services,
like its legal, finance and HR functions. It used a leading benchmarking provider and focused the comparators on companies in similar sectors.
43.
We expect this work to form part of the evidence informing Highways England's plans for the second road period. We will assess it as part of our Efficiency Review of those plans.

## International Road Construction Database

44.
Over the past year we have continued to engage with the OECD-ITF2 on its plans to
establish an international database of road construction costs. Such initiatives typically take a long time and considerable effort to set up. But it would offer potentially useful information on how and why road construction costs vary between countries. So the importance of taking this project forward will depend in part on the evidence in Highways England's plans for the second road period and whether future plans would benefit from more international comparison.

## Annex A - Regional Dashboards Calculating The Performance Radar Charts

The 'radar charts' on each dashboard show regional performance relative to Highways England's overall target. Performance has been normalised to the target level and is shown with the red line. If the blue line is outside the red target, then performance exceeded the target for that KPI in that region in that year. The exception is average delay, which has no target. For this KPI the red line represents average delay across the SRN as a whole in 2015-16, with regional performance presented relative to the national average. 

The table below sets out the outcome areas, metrics and targets for each of the five KPIs: 

| Outcome area                    | KPI metric                  | Target                         |
|---------------------------------|-----------------------------|--------------------------------|
| >90% NRUSS score by 31          |                             |                                |
| March 2017                      |                             |                                |
| Improving user satisfaction     | Percentage of NRUSS         |                                |
| respondents fairly or very      |                             |                                |
| satisfied                       |                             |                                |
| Supporting the smooth flow of   |                             |                                |
| traffic                         |                             |                                |
| >97% of the network available   |                             |                                |
| to traffic                      |                             |                                |
| Percentage of the network       |                             |                                |
| (measured in lane miles) open   |                             |                                |
| to traffic                      |                             |                                |
| >85% of motorway incidents      |                             |                                |
| cleared within 1 hour           |                             |                                |
| Percentage of incidents on      |                             |                                |
| motorways cleared within 1      |                             |                                |
| hour                            |                             |                                |
| No target                       | Encouraging economic growth | Average delay - the difference |
| (in seconds per mile) between   |                             |                                |
| actual and free-flow speeds     |                             |                                |
| Keeping the network in good     |                             |                                |
| condition                       |                             |                                |
| >95% of pavement not            |                             |                                |
| requiring further investigation |                             |                                |
| Percentage of the pavement      |                             |                                |
| not requiring further           |                             |                                |
| investigation for maintenance   |                             |                                |

## Average Delay

As discussed above, performance against this KPI is represented against the average for the SRN, as there is no target. Lower delay represents better performance so the data are transformed in the following way:  

$$A v e r a g e\,d e l a y=1+\left(1-\frac{a v e r a g e\,d e l a y_{r e g i o n}}{a v e r a g e\,d e l a y_{S R N}}\right)$$
Network availability, incident clearance, user satisfaction and pavement condition These four KPIs are all measured in percentage terms, with a higher number representing better performance. However, the targets for all four KPIs are relatively close to 100%, making it difficult to demonstrate variation between the regions. Therefore each metric, and its respective target was transformed as shown in the table below: 

| KPI              | Target            | Transformed    |
|------------------|-------------------|----------------|
| KPI              |                   |                |
| Transformed      |                   |                |
| target           |                   |                |
| <3%              | Network           |                |
| availability     |                   |                |
| % lane           |                   |                |
| availability     |                   |                |
| >97%             | % lane            |                |
| un               | availability      |                |
| <15%             | Incident          |                |
| clearance        |                   |                |
| % incidents      |                   |                |
| cleared within 1 |                   |                |
| hour             |                   |                |
| >85%             | % incidents       | not            |
| cleared within 1 |                   |                |
| hour             |                   |                |
| <10%             | User satisfaction |                |
| % fairly or very |                   |                |
| satisfied        |                   |                |
| >90%             |                   |                |
| %                | not               | fairly or      |
| very satisfied   |                   |                |
| <5%              |                   |                |
| Pavement         |                   |                |
| condition        |                   |                |
| % of pavement    |                   |                |
| not requiring    |                   |                |
| further          |                   |                |
| investigation    |                   |                |
| >95%             |                   |                |
| % of pavement    |                   |                |
| not              | requiring         |                |
| further          |                   |                |
| investigation    |                   |                |

These transformations produce metrics where a lower score is better. The transformation used for average delay is then applied for presentation in the radar charts. If performance were more than double the target level (for example, if >6% of the network were unavailable), this would give a score of 0. Any such scores are adjusted to 0.05, so as not to appear as 'zero performance' in the radar charts. The 2015-16 regional pavement condition data are based on a pro-rata adjustment to the performance reported that year, to reflect the revised figure for the network as a whole in that year. 

## Treatment Of Dbfo-Managed Sections Of The Network

Management of the SRN is split into a series of areas and regions. There are thirteen areas, one of which (the M25) is managed by a private contractor under a Design, Build, Finance, Operate (DBFO) contract. The other twelve areas are combined together into six regions, with two areas in each region. 

Including the M25, there are eleven sections of the network managed under DBFO contracts. Private operators are appointed to design, build and finance major improvements to the network, and to operate (maintain and renew) it over a 30-year period. The regional dashboards, including the network and traffic data, relate only to those parts of the network managed by Highways England's regions - DBFO-managed roads are excluded. The user satisfaction KPI in the radar charts is the exception, as it is not possible to differentiate between DBFO and non-DBFO sections of the network. 

The maps on the dashboards show the SRN but do not differentiate between sections that are directly managed by Highways England's regions.3  More detail on which parts of the network fall into each region, and which are managed by DBFO operators, can be found here: https://www.gov.uk/government/publications/roads-managed-by-the-highwaysagency 

## Regional Stats, Road Length, Spending And Traffic Population

Regional population estimates for mid-2017 were sourced from the ONS and are rounded to nearest 100,000 in the dashboards: 
https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populatio nestimates/datasets/populationestimatesforukenglandandwalesscotlandandnorthernirelan d 

## Gva Per Head

Gross value added (GVA) data for 2017 were sourced from ONS; divided by regional population to give GVA per head; and are rounded to the nearest £250 in the dashboards: https://www.ons.gov.uk/economy/grossvalueaddedgva/datasets/regionalgrossvalueaddedi ncomeapproach 

## Structures

The number of structures on each region of the SRN is sourced from Highways England's Structures Management Information System (SMIS)). The main categories of structures included are: 


bridges and large culverts,

masts,

retaining walls,

road tunnels, and

signs and / or signal gantries.

## Road Length

Two measures of the length of the SRN are presented in the dashboards: 


route length, split by road type - the sum of the main carriageway lengths only (e.g.
excluding slip roads) with a factor of 0.5 applied to dual carriageways; and

lane length - the sum of the carriageway sections multiplied by the number of
permanent running lanes (i.e. hard shoulders are excluded).
Data were sourced from Highways England's pavement management information system (HAPMS) and represent a snapshot for 31 March 2018. 

## Spending

Maintenance and renewal spending data were sourced from statements F2.1 and F3.1 of Highways England's 2017-18 performance monitoring statements. The spending figures are divided by the lane length data described above to give a figure per lane mile, and are compared with the average across the six regions: 
https://www.gov.uk/government/publications/highways-englands-2017-to-2018- performance-monitoring-statements 

## Traffic

Traffic data are for 2017 and were sourced from DfT Road Traffic Statistics. Traffic on DBFO-managed roads was separately identified but the regional boundaries do not exactly match the boundaries of Highways England's regions. The source data gives vehicle kilometres in 2017 by road and vehicle type. We have converted this to annual average daily traffic flow by dividing annual vehicle miles (for all vehicle types) by route length (as defined above) and then by 365 days to give the daily average. Flow refers to the number of vehicles passing a point on a road over a given period of the time. The annual average daily traffic flow represents the number of vehicles (travelling in both directions) that would pass a point on the network during an average 24 hour period in 2017. The percentage of HGV traffic is the proportion of HGV miles in total vehicle miles. 

## © Crown Copyright 2019

This publication is licensed under the terms of the Open Government Licence v3.0 except where otherwise stated. To view this licence, visit nationalarchives.gov.uk/doc/open-governmentlicence/version/3. Where we have identified any third party copyright information you will need to obtain permission from the copyright holders concerned. This publication is available at orr.gov.uk Any enquiries regarding this publication should be sent to us at orr.gov.uk OGL logo