1 Executive Summary

This Area Profile presents a systematic overview of resident and road risk in Wokingham. The insight derived from this report can inform the design and development of road safety interventions, underpin local road safety strategies and support local authorities and their stakeholders to secure safer roads and healthier communities across the area. Area Profiles are compiled using analytical techniques which, not only compare long term trends but also use rate-based measures derived from a range of datasets.

Wokingham’s overall resident casualty rate, at 133 casualties per year, per 100,000 population, is slightly lower than previous 5-year analysis periods and remains more than 35% below the national rate and 40% below the South-East regional rate. Having been steadily declining since 2015 resident casualty numbers have increased in 2021 and 2022 to return to levels comparable to 2017 and 2018. Half of these resident casualties are injured on roads outside of Wokingham Borough, with many injured on roads in neighbouring authorities such as Reading, Hampshire, Surrey and Bracknell Forest.

The highest number of Wokingham’s resident casualties are from Acorn type D8: affluent, older homeowners, although resident casualties from Acorn types M37: restricted resident socially renting and Q49: socially renting single adult households despite being small in number are significantly over-represented in the casualty figures. Wokingham’s resident casualties are most likely to come from the least deprived 10% of the population, however communities in the more deprived 40% and the less deprived 40% are over-represented as resident casualties, despite having lower numbers of resident casualties.

Resident casualties have been broken down into the following cohorts:

1.      Resident child casualty numbers have seen a fluctuating downward trend over the last ten years but in 2022 have fallen by 31% from the previous year. This represents a 42% reduction since 2013 and the lowest number of resident child casualty numbers in a decade. Three quarters of Wokingham’s resident child casualties are injured in Wokingham.

2.      Resident older casualty numbers were on a downward trajectory from 2013 to 2019 however since 2020 resident older casualty numbers have risen year-on year to increase by nearly a third in the last 2 years. Killed and seriously injured casualties account for approximately 17% of all resident older casualties.

3.      Resident pedestrian casualty numbers have seen year on year fluctuations since 2016. Having risen in 2021 immediately following the pandemic, in 2022 they have fallen again to below pandemic levels. This latest fall in resident pedestrian casualty numbers represents a decrease by just over a third since 2013. Sixty-six percent of Wokingham’s resident pedestrian casualties are injured on Wokingham’s roads.

4.      Resident pedal cyclist casualty numbers have been decreasing consistently over the last ten years and this trend has continued since 2020. With just 26 resident pedal cyclist casualties in 2022 this is a 50% reduction from the start of the decade in 2013. Like resident pedestrians, two thirds of all resident pedal cyclist casualties are injured in Wokingham.

The number of collision-involved resident drivers from Wokingham have been decreasing from 2015 to 2020 although numbers have increased by an average of 14% in each of the last two years. Despite this the resident driver collision involvement numbers are 30% lower than at the start of the decade and as a rate per 100,000 population remains 45% below the national rate, 41% below the South-East regional rate, and 16% below the rate for Berkshire as a whole. The largest number of resident collision-involved drivers are aged 25 to 44. Collision-involved resident drivers aged 17 to 24 are over-represented relative to their local population and to a greater extent than the over-representation for collision-involvement by this age group seen nationally. Wokingham’s resident drivers aged 55+ years are under-represented in their collision-involvement.

Again, the majority of resident drivers involved in collisions are from the Acorn type D8: affluent, older homeowners. Collision-involved resident drivers from communities of restricted residents, socially renting (M37), although smaller in number, are over-represented compared to their population.

Focusing more closely on specific driver groups, Wokingham has the second lowest resident young driver collision-involvement rate across Berkshire at 244 young drivers per year, per 100,000 population. This rate is 27% less the national rate and 38% below the regional rate for the South-East. In terms of absolute numbers there has been a 47% reduction in the number of resident young drivers involved in collisions since 2013. Whilst 60% of the casualties were the young drivers themselves, 31% of casualties in young driver involved collisions are their passengers. This is the highest percentage of passenger casualties of any driver group.

Resident older driver involvement in collisions in Wokingham has been in decline since 2014 through to 2021, with a 3-year average of just 30 collisions (2019 – 2021).  However, in 2022 there has been a 100% increase in the number of resident older drivers involved in collisions on 2021 figures. There has also been more than a 300% increase in the number of fatal or serious injury collisions that resident older drivers have been involved in.

The rate of resident motorcycle riders involved in collisions per population is just 16 riders per year. However following a declining trend from 2016, collision-involvement has risen again in 2021 and 2022 with a 36% increase on the pre-pandemic 3-year average (2017 - 2019).

In addition to considering risk to Wokingham’s residents, this Area Profile also considers collision rates on the local road network. Following an upturn in collisions in 2021, there was a marginal fall in the number of collisions across the Wokingham network in 2022. The collision rate per 100km of road is still below the national rate and is the second lowest collision rate of all authorities in Berkshire.

Just above half of all collisions on Wokingham’s network occur on roads considered within the urban area with the remaining forty-three percent on rural class roads. Despite the slightly higher number on urban roads the collision rate for urban and rural roads in Wokingham is very similar at 22 and 23 collisions per year, per 100km of road respectively.

Collision numbers on urban roads in Wokingham saw a downward trend over the last decade from 2015 onwards. However as with all roads, numbers rose in 2021 and again in 2022 following the reduction in 2020 that coincided with pandemic-related travel restrictions. The collision rate between 2018 and 2022 continues to be less than half of both the national and South-East regional urban collision rates. Wokingham’s urban collision rate was 38% lower than the overall rate for Berkshire on urban roads. Analysis of the collision dynamics at the time of the collision shows patterns from previous years have continued with 26% of collisions on urban roads involving no vehicle-to-vehicle impact. Where multiple vehicles were involved, whilst 16% continue to involve rear vehicle impacts; 16% also now involve side impacts (up from 9%); and 12% involved head-on impacts. The driver actions at the time of the collision show that the highest percentage of collisions on urban roads were when making a right turn, followed by a slow manoeuvre such as stopping. On urban roads alone the number of motorcyclists seriously injured has halved between 2021 to 2022 while the number of child casualties seriously injured although still small in absolute numbers, has increased year-on-year since 2019.

Collision numbers on rural roads in Wokingham have been steadily falling over the last decade since 2014. After marginal increases in between 2019 and 2021, collision numbers on rural roads have fallen once again. The collision rate between 2018 and 2022 was 62% higher than the national rate, but 15% lower the South-East regional rate. As with the rate for collisions on all roads, Wokingham’s collision rate on rural roads was the second lowest in Berkshire amongst comparator authorities, after West Berkshire. Analysis of the collision dynamics at the time of the collision show that closer to a third of collisions on rural roads involved no vehicle-to-vehicle impact. Where multiple vehicles were involved, 20% involved rear vehicle impacts; 12% involved side impacts; and 10% involved head-on impacts. The driver actions at the time of the collision show that the highest percentage of collisions on urban roads involved run-off incidents, particularly run-offs to the nearside of the carriageway.

The factors that contribute towards collisions on Wokingham’s road network (CFs) are also measured. It is entirely possible that a combination of factors led to a collision taking place, and the results do not produce figures that represent the number of incidents ‘caused’ by a single factor.  Speeding, as measured by the factors ‘exceeding the speed limit’ or ‘travelling too fast for conditions’, has increased by more than a quarter between 2021 and 2022. Together, these factors still play a role in 9% of officer attended collisions in Wokingham, a percentage that is below the national and South-East percentages for speeding contributory factors.The number of impairment CFs attributed, ‘impaired by alcohol’ or ‘impaired by drugs (illicit or medicinal)’, has seen a rising trend since 2016 with numbers 75% higher than at the start of the decade. The number of impairment related collisions resulting in serious injury have reduced by more than 50% in 2022 from the previous two years with slight collisions accounting for the overall increase. Impairment CFs were attributed in 9.7% of officer attended collisions on Wokingham’s roads, a percentage that is notably higher than the national and South-East Regional percentages. Road surface contributory factors show a marked increase of 86% in 2022 on 2021 numbers. Despite this increase the rate of road surface CFs attributed in officer attended collisions is still below the national and South-East regional percentages. Control error contributory factors also show a declining trend across the decade, although collision numbers have increased in the last 3 years. Attributed in 17.4% of officer attended collisions, this broadly in line with the national and South-East percentages. Whilst the number of unsafe behaviour contributory factors attributed, ‘aggressive driving’ or ‘careless, reckless or in a hurry’, has decreased moderately since the start of decade; 19.8% of officer-attended collisions were attributed an unsafe behaviour CF. This is higher than the national percentage but in line with the South-East regional percentage. Close following contributory factors have decreased dramatically, in particular after 2015, and were only allocated in 3.5% of officer attended collisions, a slightly lower proportion than those seen at the national and South-East regional levels. Medically unfit contributory factor numbers have fluctuated during the course of the last decade, having shown a steep increase in 2021, numbers have fallen again in 2022, below pre-pandemic levels. 4% of officer-attended collisions received a medically unfit CF, higher than both the national and South-East regional percentages. Distraction contributory factor numbers were falling up to 2020 and despite an increase in 2021 have continued the declining trend in 2022. Attributed to 7% of collisions attended by an officer, this is a markedly higher proportion than those seen nationally and in the South East Region.

In summary the road safety risk rates for Wokingham residents are, for the most part, lower than the national and regional norms and have decreased over the last ten years. Resident drivers have a lower risk rate than most of the comparator authorities.

2 Introduction

2.1 Overview

2.1.1 Background

Area Profiles from Agilysis provide overviews of road safety performance within specific local areas. This profile delivers detailed analysis and insight on all injury collisions reported to the police in Wokingham, as well as casualties and drivers involved in collisions anywhere in Britain who reside in Wokingham.

Area Profile formats are modular, which affords the flexibility to select topics for inclusion to reflect local needs and allows each section of the report to be used independently if required. Profile design allows authorities to understand general casualty and collision trends affecting their residents and roads, as well as selecting particular topics based on local issues. Experts from Agilysis work with commissioning authorities to ensure that selected topics provide an accurate and relevant assessment. After production of a first Area Profile, updates can be produced in future years covering the entire document or selected existing sections, whilst new topics can also be introduced in response to latest trends and concerns.

2.1.2 Aims and Objectives

The aim of this document is to provide a comprehensive profile of road safety issues affecting Wokingham’s road network and Wokingham’s residents, primarily using STATS19 collision data1 and Acorn socio-demographic classification. Annual trends are presented and analysed for key road user groups, predominantly based on data from the last five full years of available statistics but referring to older figures where appropriate.

The Road Safety Analysis (RSA) analysis tool MAST Online has also been used to investigate trends for Wokingham’s residents involved in road collisions anywhere in the country, including socio-demographic profiling of casualties and drivers. MAST has been used to allow comparison of Wokingham’s key road safety issues with those of comparator regions and national figures. The aim is to allow Wokingham to assess its progress alongside other areas, and work together with neighbours to address common issues.

2.1.3 Analytical Techniques

The analytical techniques employed throughout this Area Profile are detailed in Section 5.1 on Analytical Techniques. Please refer to this section for information on the terminology and data sources used as well to understand methodologies utilised and the structure and scope of the report.

2.2 Profile Configuration

2.2.1 Structure

The Area Profile has been divided into separate analysis of key road user groups. The aim is to allow each section to be used independently if required. This will also allow Wokingham to update selected sections when appropriate, without a requirement to update the entire document.

Section 3 explores Resident Risk. Resident risk analysis includes examining all of Wokingham’s resident casualties and resident motor vehicle users in terms of rates, comparisons with other relevant police forces and authorities; residency by small area; trends and socio-demographic analysis. Specific road user groups will also be analysed against these measures. The focus of this section is on how the people of Wokingham are involved in collisions, rather than what happens on local roads.

Section 4 provides analysis of Road Network Risk. It also examines rates; comparisons; location by small area; and trends on Wokingham’s roads. Breakdowns by rurality classification of road are also included in this section.

Section 5 includes Appendices detailing all Acorn Types and the profile and distribution of specific Acorn Types relevant to Wokingham. It also contains data tables for all analysis referred to in this Area Profile.

2.2.2 Scope

All figures included in this report are based on STATS19 collision data. The residents section covers casualties and motor vehicle users involved in collisions who are residents of Wokingham, regardless of where in Britain the collision occurred. Resident analysis in this profile is based on the national STATS19 dataset as provided to Road Safety Analysis by the Department for Transport for publication in MAST Online over the five-year period between 2018 and 2022 inclusive. For a more complete explanation, please refer to 5.1.1 on methodology for calculating resident risk.

In contrast, the road network section covers collisions which occurred on Wokingham’s roads, regardless of where those involved reside. Network analysis is also based on the national STATS19 dataset over the five-year period between 2018 and 2022 inclusive. For a more complete explanation, please refer to 5.1.1 on methodology for calculating network collision risk.

3 Wokingham Resident Risk

For information about the provenance and scope of data included in this section, please refer to section 2.2.2. For an explanation of the methodologies employed throughout this section, please refer to 5.1.1.

3.1 Wokingham Resident Casualties

This section examines all casualties who were residents of Wokingham at the time of injury. For information about Wokingham’s resident motor vehicle users involved in collisions on all roads, please refer to section ??.

3.1.1 All Resident Casualties

3.1.1.1 Rates

Figure 3.1 shows the resident casualty rates for Wokingham compared to the national and regional rates, as well as the most similar comparators.

For the 5-year period 2018 to 2022 Wokingham had a resident casualty rate of 133 casualties per year per 100,000 population. There has been a small reduction in the casualty rate since the previous 5-year period.

Figure 3.1: Annual average Wokingham resident casualties per 100,000 population (2018 - 2022)

Annual average Wokingham resident casualties per 100,000 population (2018 - 2022)

3.1.1.2 Comparisons

Wokingham’s resident casualty rate remains more than 35% below the national rate and 40% below the regional rate. Compared to Berkshire as a whole Wokingham’s resident casualty rate is 17% lower continuing to sit below that of Bracknell Forest, Windsor & Maidenhead and Reading and similar to West Berkshire. Wokingham’s resident casualty rate is below the resident casualty rate of the comparator authorities, with the exception of South Oxfordshire.

3.1.1.2.1 Residency by Small Area

Figure 3.2 shows the home location of Wokingham’s resident casualties by lower layer super output area (LSOA). The thematic map is coloured by resident casualties per year per population of LSOA.

The highest resident casualty rates can be found around Finchampstead, Wokingham town and Shinfield. There are also high resident casualty rates around Earley, Woodley East and Winnersh.

Figure 3.2: Wokingham resident casualties home location by LSOA, casualties per year per 100,000 population (2018-2022)

Wokingham resident casualties home location by LSOA, casualties per year per 100,000 population (2018-2022)

3.1.1.4 Socio Demographic Analysis

3.1.1.4.1 Age

Figure ?? (or 3.4) shows the numbers of resident casualties by ten specified age groups.

Over two thirds of all casualties are aged between 17 and 54 years although it is the age category 25-34 that accounts for the largest proportion of all casualties. Just 9% of all casualties are aged 65 years and over.

It is more informative to consider Figure ?? (or 3.5) which shows resident casualty numbers by age group indexed by the population of those age groups in Wokingham. There is also a national index value for comparison.

Casualties aged 17-34 are over-represented when taking relative population size into account and to a greater extent in Wokingham than nationally. However the difference between Wokingham and Great Britain has reduced between 2017 - 2021 and 2018 - 2022. Casualties aged 45-54 have increased relative to the population size and are now over-represented in Wokingham and moreso than the nationally observed over-representation. Casualties aged under 17 and over 55 are under-represented compared to relative population size.

Figure 3.4: Wokingham resident casualties, by age group (2018-2022)

Wokingham resident casualties, by age group (2018-2022)

Figure 3.5: Wokingham resident casualties, by age group and indexed by population (2018-2022)

Wokingham resident casualties, by age group and indexed by population (2018-2022)

Figure 3.6 illustrates the overall trend for the four age groups over the last ten years.

Casualty trends for the under 17 and over 60 years age groups have remained largely the same whilst casualties aged 17-24 years have fallen over the decade. Casualties aged 25-59 years have also fallen since 2013 but have been rising again since 2020.

Figure 3.6: Wokingham resident casualty trend by age group (2013-2022)

Wokingham resident casualty trend by age group (2013-2022)
3.1.1.4.2 Segmentation

Analysis of the Acorn communities in which Wokingham’s resident casualties live provides an insight into those injured in collisions. For an explanation of Acorn and how to understand the following chart, please refer to section 5.1.1.1.

The largest number of resident casualties belong to the group Affluent, older homeowners (D8) although these communities are under-represented compared to the relative population.

Communities of Restricted residents socially renting (M37) and Socially renting single adult households (Q49) have small numbers of casualties but are significantly over-represented with approximately twice the proportion of casualties expected relative to population size.

Figure 3.7: Wokingham resident casualties, by Acorn Type (2018-2022)

Wokingham resident casualties, by Acorn Type (2018-2022)
3.1.1.4.3 Deprivation

Figure 3.8 shows resident casualties by the IMD of the LSOA (Lower Super Output Area) in which they reside.

The largest number of Wokingham’s resident casualties come from the least deprived 10% decile. Communities in the more deprived 40% and less deprived 40% have far fewer casualties but are noticeably over-represented in casualty numbers.

Figure 3.8: Wokingham resident casualties, by Index of Multiple Deprivation (2018-2022)

Wokingham resident casualties, by Index of Multiple Deprivation (2018-2022)

3.1.2 Resident Child Casualties

This section examines child casualties who are residents of Wokingham. For an explanation of the methodologies employed throughout this section, please refer to 5.1.1.

3.1.2.1 Rates

Figure 3.9 shows Wokingham resident child casualty rate compared to the national and regional rates, and to the most similar comparators.

Wokingham has a resident child casualty rate of 62.6 casualties per year, per 100,000 child population.

Figure 3.9: Annual average Wokingham resident child casualties per 100,000 population (2018-2022)

Annual average Wokingham resident child casualties per 100,000 population (2018-2022)

3.1.2.2 Comparisons

Wokingham’s resident child casualty rate is 41% lower than the national rate for Great Britain and 39% below the rate for the South-east region. Whilst it is 12% below the rate for Berkshire as a whole Wokingham has a higher resident child casualty rate than its neighbouring authorities Bracknell Forest, Windsor & Maidenhead and West Berkshire. Against the comparator authorities Wokingham’s resident child casualty rate is higher than South Oxfordshire but lower than Hart, South Cambridgeshire and Surrey Heath.

3.1.2.2.1 Residency by Small Area

Figure 3.10 shows the home location of Wokingham’s resident child casualties by lower layer super output area (LSOA). The thematic map is coloured by resident casualties per year per population of LSOA.

The highest concentrations of child casualties are amongst residents of South Lake and Nores Hill in the west of Shinfield. The lowest rates of child casualties are found amongst residents to the south of the Borough, in Spencers Wood & Swallowfield, Arborfield & Garrison, Crowthorne North & Finchampstead.

Figure 3.10: Wokingham resident child casualties home location by LSOA, casualties per year per 100,000 population (2018-2022)

Wokingham resident child casualties home location by LSOA, casualties per year per 100,000 population (2018-2022)

3.1.3 Resident Older Casualties

This section examines older casualties who are residents of Wokingham. For an explanation of the methodologies employed throughout this section, please refer to section 5.1.1.

3.1.3.1 Rates

Figure 3.12 shows the resident older casualty rates for Wokingham compared to the national and regional rates, as well as the most similar comparators.

Wokingham’s resident older casualty (those 60 years and older) rate is 86 casualties per year, per 100,000 population.

Figure 3.12: Annual average Wokingham resident older casualties per 100,000 population (2018-2022)

Annual average Wokingham resident older casualties per 100,000 population (2018-2022)

3.1.3.2 Comparisons

Wokingham’s older casualty rate is more than a third lower than the same rate for Great Britain and the South East region, it is also 9% lower than the older casualty rate for Berkshire as a whole. The Wokingham rate is lower by a difference of 10% or less than the older casualty rates for Bracknell Forest, West Berkshire and Reading, whilst the other neighbouring authorities have older casualty rates that are more than 10% higher. Of the comparator authorities, South Oxfordshire’s older casualty rate is lower while the rate for Hart, Surrey Heath and South Cambridgeshire are all higher.

3.1.3.2.1 Residency by Small Area

Figure 3.13 shows the home location of Wokingham’s resident older casualties by lower layer super output area (LSOA). The thematic map is coloured by resident older casualties per year per older population of LSOA.

The highest older casualty rate is found in the region to the east of Finchampstead with high rates also found in Lower Earley North and Twyford West & Charvil.

Figure 3.13: Wokingham resident older casualties home location by LSOA, casualties per year per 100,000 population (2018-2022)

Wokingham resident older casualties home location by LSOA, casualties per year per 100,000 population (2018-2022)

3.1.3.4 Socio Demographic Analysis

3.1.3.4.1 Segmentation

Analysis of the Acorn communities in which Wokingham’s resident older casualties live provides an insight into those injured in collisions. For an explanation of Acorn and how to understand the following chart, please refer to section 5.1.1.1.

Again Wokingham’s resident older casualties follow the trend of all casualties with the largest proportion, nearly a quarter, of all older casualties from communities of Acorn Type D8 - affluent, older homeowners. This community type is over-represented in casualty terms by approximately 20% compared to local population size.

Figure 3.15: Wokingham resident older casualties, by Acorn Type (2018-2022)

Wokingham resident older casualties, by Acorn Type (2018-2022)
3.1.3.4.2 Deprivation

Figure 3.16 shows resident older casualties by the IMD of the LSOA (Lower Super Output Area) in which they reside.

Aligned with the characteristics of the predominant socio-demographic group, nearly all older casualties are from less deprived communities and specifically just under two thirds from the least deprived 10% decile.

Figure 3.16: Wokingham resident older casualties, by Index of Multiple Deprivation (2018-2022)

Wokingham resident older casualties, by Index of Multiple Deprivation (2018-2022)

3.1.4 All Wokingham Resident Pedestrian Casualties

This section examines pedestrian casualties who are residents of Wokingham. For an explanation of the methodologies employed throughout this section, please refer to section 5.1.1.

3.1.4.1 Rates

Figure 3.17 shows the resident pedestrian casualty rates for Wokingham compared to the national and regional rates, as well as the most similar comparators.

Between 2018 and 2022, Wokingham had a resident pedestrian casualty rate of 14 casualties per year, per 100,000 population.

Figure 3.17: Annual average Wokingham resident pedestrian casualties per 100,000 population (2018-2022)

Annual average Wokingham resident pedestrian casualties per 100,000 population (2018-2022)

3.1.4.2 Comparisons

The resident pedestrian casualty rate for Wokingham is just over half the national rate, 43% below the regional rate, and 26% below the overall Berkshire rate. Within Berkshire, Wokingham’s pedestrian casualty rate is higher than those of Bracknell Forest & West Berkshire, but lower than that of Reading, Slough and Windsor & Maidenhead. Of the most similar comparator authorities, Wokingham’s pedestrian casualty rate is higher than that of South Cambridgeshire and South Oxfordshire, but lower than that of Hart and Surrey Heath.

3.1.4.2.1 Residency by Small Area

Figure 3.18 shows the home location of Wokingham’s resident pedestrian casualties by lower layer super output area (LSOA). The thematic map is coloured by resident casualties per year per population of LSOA.

Resident pedestrian casualty rates are highest around Shinfield, and Barkham & Woose Hill. There are also high rates in parts of Winnersh, Charvil, Earley and Southlake.

Figure 3.18: Wokingham resident pedestrian casualties home location by LSOA, casualties per year per 100,000 population (2018-2022)

Wokingham resident pedestrian casualties home location by LSOA, casualties per year per 100,000 population (2018-2022)

3.1.5 All Wokingham Resident Pedal Cyclist Casualties

This section examines pedal cyclist casualties who are residents of Wokingham. For an explanation of the methodologies employed throughout this section, please refer to 5.1.1.

3.1.5.1 Rates

Figure 3.20 shows the resident pedal cyclist casualty rates for Wokingham compared to the national and regional rates, as well as the most similar comparators.

Wokingham had a resident pedal cyclist casualty rate of 19 casualties per year, per 100,000 population.

Figure 3.20: Annual average Wokingham resident pedal cyclist casualties per 100,000 population (2018-2022)

Annual average Wokingham resident pedal cyclist casualties per 100,000 population (2018-2022)

3.1.5.2 Comparisons

Wokingham’s resident pedal cyclist casualty rate is 23% below the national rate, 24% below the regional rate for the South East, and 13% below the overall rate for Berkshire. Within Berkshire, Wokingham’s rate is above the rates of Bracknell Forest and West Berkshire, but below the rates of Reading, Slough, and Windsor & Maidenhead. Wokingham’s rate is below that of all similar comparator authorities.

3.1.5.2.1 Residency by Small Area

Figure 3.21 shows the home location of Wokingham’s resident pedal cyclist casualties by lower layer super output area (LSOA). The thematic map is coloured by resident pedal cyclist casualties per year per population of LSOA.

The highest resident pedal cyclist casualty rates can be found around West Twyford & Charvil and Wokingham West. There are also high rates in Wokingham North & Hurst.

Figure 3.21: Wokingham resident pedal cyclist casualties home location by LSOA, casualties per year per 100,000 population (2018-2022)

Wokingham resident pedal cyclist casualties home location by LSOA, casualties per year per 100,000 population (2018-2022)

3.2 Wokingham Resident Drivers involved in Collisions

This section refers to all drivers of motor vehicles and motorcycles involved in collisions and who are residents of Wokingham.

3.2.1 All Resident Motor Vehicle Driver Involvement (excluding motorcycle riders)

This section analyses all persons recorded as being [a] Wokingham resident in charge of a motor vehicle (other than a motorcycle or moped) involved in a collision, regardless of age. Therefore, it includes a small number of drivers recorded as being under the age of seventeen.

3.2.1.1 Rates

Figure 3.23 shows the resident driver involvement rates for Wokingham compared to the national and regional rates, as well as the most similar comparators.

Wokingham had a resident driver involvement rate of 139 drivers per year, per 100,000 population

Figure 3.23: Annual average Wokingham resident involved drivers per 100,000 population (2018-2022)

Annual average Wokingham resident involved drivers per 100,000 population (2018-2022)

3.2.1.2 Comparisons

The resident driver collision involvement rate for Wokingham was 45% below the national rate, 41% below the regional rate, and 16% below the rate for Berkshire as a whole. Within Berkshire, Wokingham’s rate is slightly lower than that of West Berkshire, Windsor & Maidenhead, Reading and Bracknell Forest, and significantly below that of Slough. Wokingham’s rate was below that of all the most similar comparator authorities apart from South Oxfordshire.

3.2.1.2.1 Residency by Small Area

Figure 3.24 shows the home location of Wokingham’s collision-involved resident drivers by lower layer super output area (LSOA). The thematic map is coloured by resident involved drivers per year per population of LSOA.

The highest resident driver involvement rates can be found in Winnersh and the north of Shinfield. There are also high involved drivers rates around Twyford, Wokingham Town (north), Earley and the north of Finchampstead.

Figure 3.24: Wokingham resident involved drivers home location by LSOA, drivers per year per 100,000 population (2018-2022)

Wokingham resident involved drivers home location by LSOA, drivers per year per 100,000 population (2018-2022)

3.2.1.4 Socio Demographic Analysis

3.2.1.4.1 Age

Figure 3.26 shows the numbers of resident involved drivers by ten specified age groups.

The largest number of resident involved drivers are in the 25-34 and 35-44 age group. These are followed by the 45-54 and 17-24 age groups.

It is more informative to consider Figure 3.27 which shows resident involved driver numbers by age group indexed by the population of those age groups in Wokingham. There is also a national index value for comparison.

When taking into account the relative population of each age group, the 17-24 age group is over-represented in driver numbers and to a greater extent than the over-representation seen nationally. This is also true, and to a greater extent in the 25-34 age group. Resident involved drivers in the 35-44 and 45-54 age groups are only slightly over-represented in driver numbers, and this is less than the nationally observed over-representation. Resident drivers in the age bands 55 and over are under-represented in driver numbers based on their share of the population.

Figure 3.26: Wokingham resident involved drivers, by age group (2018-2022)

Wokingham resident involved drivers, by age group (2018-2022)

Figure 3.27: Wokingham resident involved drivers, by age group and indexed by population (2018-2022)

Wokingham resident involved drivers, by age group and indexed by population (2018-2022)

Figure 3.28 illustrates the overall trend for the four age groups over the last ten years.

Involvement trends by all Wokingham resident driver age groups have decreased over the last ten years. Following an increase from 2020 to 2021, the rise in 25-59 year old involved drivers has slowed whilst the rise of involved drivers aged 60+ has continued.

Figure 3.28: Wokingham resident involved drivers trend by age group (2013-2022)

Wokingham resident involved drivers trend by age group (2013-2022)
3.2.1.4.2 Segmentation

Analysis of the Acorn communities in which Wokingham’s resident drivers live provides an insight into those injured in collisions. For an explanation of Acorn and how to understand the following chart, please refer to section 5.1.1.1.

The largest number of resident involved drivers come from communities of Affluent, older homeowners (D8). When taking into account the relative population of this type, these communities are under-represented in collision involvement. The next largest numbers of involved drivers are from communities of Mixed life stages in semi-detached homes (G20).

Communities of Restricted residents, socially renting (M37) represent lower levels of collision involved drivers, but are significantly over-represented in collisions given their share of the population.

Figure 3.29: Wokingham resident involved drivers, by Acorn Type (2018-2022)

Wokingham resident involved drivers, by Acorn Type (2018-2022)
3.2.1.4.3 Deprivation

Figure 3.30 shows resident involved drivers by the IMD of the LSOA (Lower Super Output Area) in which they reside.

The highest numbers of resident involved drivers come from communities in the least deprived 10% decile and when considering their share of the population, they are slightly over-represented in collision involvement. The next largest number of resident involved drivers come from communities in the less deprived 20% decile, and these communities are also slightly over-represented in collisions. Communities in the less deprived and more deprived 40% deciles and the less deprived 30% deciles represent a much lower number of involved drivers but are over-represented when accounting for their relative population.

Figure 3.30: Wokingham resident involved drivers, by Index of Multiple Deprivation (2018-2022)

Wokingham resident involved drivers, by Index of Multiple Deprivation (2018-2022)

3.2.3 Resident Young Driver Involvement (aged 17 to 24)

This section analyses all young Wokingham resident drivers involved in a collision.

3.2.3.1 Rates

Figure 3.32 shows the resident young driver involvement rates for Wokingham compared to the national and regional rates, as well as the most similar comparators.

Wokingham had a collision involvement rate for resident young drivers of 244 drivers per year, per 100,000 population.

Figure 3.32: Annual average Wokingham resident young involved drivers per 100,000 population (2018-2022)

Annual average Wokingham resident young involved drivers per 100,000 population (2018-2022)

3.2.3.2 Comparisons

Wokingham’s young driver collision involvement rate between 2018 and 2022 was 27% less the national rate. This is 38% below the regional rate for the South East and 5% below the overall Berkshire rate. Within Berkshire, Reading has the lowest young driver collision involvement rate, followed by Wokingham. Wokingham’s young driver involvement rate is below that of all the most similar comparator authorities.

3.2.3.2.1 Residency by Small Area

Figure 3.33 shows the home location of Wokingham’s collision-involved resident young drivers by lower layer super output area (LSOA). The thematic map is coloured by resident involved young drivers per year per young adult population of LSOA.

The highest rates of young driver collision involvement can be found among residents living in the south of Twyford West. There are also high collision involvement rates amongst young drivers from Crowthorne North, Finchampstead and Southlake.

Figure 3.33: Wokingham resident young involved drivers home location by LSOA, young drivers per year per 100,000 population (2018-2022)

Wokingham resident young involved drivers home location by LSOA, young drivers per year per 100,000 population (2018-2022)

3.2.3.4 Socio Demographic Analysis

3.2.3.4.1 Segmentation

Analysis of the Acorn communities in which Wokingham’s resident young drivers live provides an insight into those injured in collisions. For an explanation of Acorn and how to understand the following chart, please refer to section 5.1.1.1.

Figure 30 shows resident collision-involved young drivers by the Acorn Group of the community in which they reside. The majority of collision involved young drivers are from communities of Affluent, older homeowners (D8). The number of young drivers from Acorn type D8 involved in collisions is proportional to their share of the population of Wokingham as indicated by an index of 101 (shown in red).

Figure 3.35: Wokingham resident young involved drivers, by Acorn Type (2018-2022)

Wokingham resident young involved drivers, by Acorn Type (2018-2022)
3.2.3.4.2 Deprivation

Figure 3.36 shows resident involved young drivers by the IMD of the LSOA (Lower Super Output Area) in which they reside.

The largest number of resident involved young drivers come from communities in the least deprived 10% decile and when taking into account the relative population of these communities within Wokingham, they are slightly over-represented in collision involvement. There is also a number of involved young drivers from communities in the less deprived 20% decile.

Figure 3.36: Wokingham resident young involved drivers, by Index of Multiple Deprivation (2018-2022)

Wokingham resident young involved drivers, by Index of Multiple Deprivation (2018-2022)

3.2.5 Resident Older Driver Involvement

This section analyses all older Wokingham resident drivers involved in a collision.

3.2.5.1 Rates

Figure 3.38 shows the resident older driver involvement rates for Wokingham compared to the national and regional rates, as well as the most similar comparators.

Wokingham’s resident older driver involvement rate is 88 older drivers per year, per 100,000 population.

Figure 3.38: Annual average Wokingham resident involved older drivers per 100,000 population (2018-2022)

Annual average Wokingham resident involved older drivers per 100,000 population (2018-2022)

3.2.5.2 Comparisons

Wokngham’s resident older driver involvement rate is more than half that of the south east region and a little under half that of GB as a whole. Wokingham’s resident older driver involvement rate is the lowest of the Berkshire authorities and lower than all of the comparator authorities with the exception of South Oxfordshire.

3.2.5.2.1 Residency by Small Area

Figure 3.39 shows the home location of Wokingham’s collision-involved resident older drivers by lower layer super output area (LSOA). The thematic map is coloured by resident involved older drivers per year per older population of LSOA.

Like resident older casualties, resident driver collision-involvement rates are highest in the region east of Finchampstead as well as parts of Crowthorne North.

Figure 3.39: Wokingham resident involved older drivers home location by LSOA, older drivers per year per 100,000 population (2018-2022)

Wokingham resident involved older drivers home location by LSOA, older drivers per year per 100,000 population (2018-2022)

3.3 Wokingham resident motorcycle riders involved in collisions

3.3.1 Resident Motorcyclist Involvement

This section refers to motorcyclists involved in collisions and who are residents of Wokingham.

3.3.1.1 Rates

Figure 3.42 shows the resident motorcyclist involvement rates for Wokingham compared to the national and regional rates, as well as the most similar comparators.

Wokingham had a resident motorcyclist collision involvement rate of 16 motorcyclists per year, per 100,000 population between 2018 and 2022.

Figure 3.42: Annual average Wokingham resident involved motorcyclist per 100,000 population (2018-2022)

Annual average Wokingham resident involved motorcyclist per 100,000 population (2018-2022)

3.3.1.2 Comparisons

Wokingham’s resident motorcyclist collision involvement rate was 39% lower than the national rate. This is 42% below the regional rate for the South East, and 23% below the overall Berkshire rate. Within Berkshire, Wokingham had the lowest resident motorcyclist involvement rate. Wokingham’s resident motorcyclist involvement rate was above that of South Oxfordshire, and lower than all the other most similar comparator authorities.

3.3.1.2.1 Residency by Small Area

Figure 3.43 shows the home location of Wokingham’s collision-involved resident motorcyclists by lower layer super output area (LSOA). The thematic map is coloured by resident involved motorcyclists per year per population of LSOA.

The highest motorcyclist involvement rates are amongst residents of Woodley South. There are also high resident motorcyclist involvement rates amongst residents living in the residential area of Wokingham Town.

Figure 3.43: Wokingham resident involved motorcyclist home location by LSOA, motorcyclists per year per 100,000 population (2018-2022)

Wokingham resident involved motorcyclist home location by LSOA, motorcyclists per year per 100,000 population (2018-2022)

4 Wokingham Road Network Risk

For information about the provenance and scope of data included in this section, please refer to section 2.2.2. For an explanation of the methodologies employed throughout this section, please refer to section 5.1.2.

4.1 Collisions in Wokingham

This section refers to all collisions which occurred on Wokingham’s roads. For an explanation of the methodologies employed throughout this section, please refer to section 5.1.2.

4.1.1 Rates

4.1.1.1 Collisions per 100km of road

Figure 4.1 below shows the rate of average annual collisions between 2018 and 2022 per 100km of road in Wokingham compared to the national and regional rates, and those of the most similar comparators.

Between 2018 and 2022, Wokingham had a collision rate of 22.4 collisions per year, per 100km road on its road network.

Figure 4.1: Annual average collisions per 100km of road (2018-2022)

Annual average collisions per 100km of road (2018-2022)

4.1.1.2 Comparisons

The collision rate in Wokingham was 17% below the national collision rate. This is 36% below the regional rate for the South East, and 22% below the overall Berkshire collision rate. Within Berkshire, Wokingham has the second lowest collision rate behind West Berkshire.

4.1.1.2.1 Collisions by Small Area

Figure 4.2 shows collisions on all roads in Wokingham by LSOA. The thematic map is colour coded by the rate of annual average collisions per 100km of road.

The highest collision rates in Wokingham can be found in Wokingham town centre.

Figure 4.2: Annual average collisions per 100km of road (2018-2022)

Annual average collisions per 100km of road (2018-2022)

4.1.1.4 Collisions by day of the week

Figure 4.4 shows collision in Wokingham by day of the week and severity. More collisions occur on weekdays in Wokingham than at weekends.

Figure 4.4: Wokingham collisions, by day of the week and severity (2018-2022)

Wokingham collisions, by day of the week and severity (2018-2022)

4.1.1.5 Collisions by hour of the day

4.1.1.5.1 Collisions by hour of the day on weekdays

Figure 4.5 shows collisions on weekdays by the hour of the day in which they occurred. There are clear peaks around both the morning commute (7am to 9am) and the evening commute (3pm to 7pm), with very few collisions before 7am or after 10pm.

Figure 4.5: Wokingham collisions, by hour of the day during weekdays (2018-2022)

Wokingham collisions, by hour of the day during weekdays (2018-2022)
4.1.1.5.2 Collisions by hour of the day on weekends

Figure 4.6 shows collisions on a weekend by the hour of the day in which they occurred. Compared to weekdays, collision numbers are more evenly spread throughout the day, with the majority occurring after 9am and before 10pm.

Figure 4.6: Wokingham collisions, by hour of the day during weekends (2018-2022)

Wokingham collisions, by hour of the day during weekends (2018-2022)

4.1.1.6 Collisions by light conditions

Figure 4.7 shows collisions in Wokingham by the light conditions at the time of the collision. Three quarters (76%) of Wokingham’s collisions occurred during daylight. Of the remaining 24%, the majority took place in the presence of lit street lighting (16%).

Figure 4.7: Wokingham collisions by light conditions (2018-2022)

Wokingham collisions by light conditions (2018-2022)

4.1.1.7 Collisions by weather conditions

Figure 4.8 shows collisions in Wokingham by the weather conditions present at the time of the collision. Over four in five collisions (88%) in Wokingham took place during fine weather, without high winds. Of the remaining 12% that took place during adverse weather conditions, most were during rain or snow without high winds (10%).

Figure 4.8: Wokingham collisions by weather conditions (2018-2022)

Wokingham collisions by weather conditions (2018-2022)
4.1.1.7.1 Collision-involved drivers who reside in other areas

Of the drivers involved in collisions in Wokingham for whom home location was recorded, 45% were Wokingham residents. Of the remaining 55%, the majority were residents of Reading (13%), Surrey (8%), Hampshire (7%) and Bracknell Forest (6%).

4.1.1.8 Collision dynamics and driver actions

4.1.1.8.1 Collision dynamics

Figure 4.9 shows collisions in Wokingham by the dynamics resulting in the collision. A description of collision dynamics and the derivation using STATS19 data is outlined in section 5.1.4 of this report.

Almost a third (29%) of collisions in Wokingham resulted in no impact between vehicles. Of the remaining 71% of collisions, 18% involved a rear impact, 14% involved a side impact and 11% involved head-on impact. The rest either involved another type of conflict (16%) or had insufficient data to determine the type of impact.

Figure 4.9: Wokingham collisions by collision dynamics (2018-2022)

Wokingham collisions by collision dynamics (2018-2022)
4.1.1.8.2 Driver actions

Figure 4.10 shows collisions in Wokingham by the presence of different driver actions. An explanation of the derivation of driver actions and the definitions are included in section 5.1.5 of this report. Note that collisions can have multiple driver behaviours present, so there may be some overlap in numbers.

Right turns were the most prevalent driver action in collisions in Wokingham, followed by run-offs. Most of these were nearside run-offs. Slow vehicle maneouvres, such as being parked, waiting to proceed, slowing down or stopping were also present in a high number of collisions.

Figure 4.10: Wokingham collisions by driver actions (2018-2022)

Wokingham collisions by driver actions (2018-2022)

4.1.1.9 Road environment

4.1.1.9.1 Road class

Figure 4.11 shows collisions in Wokingham by class of road. Forty percent of collisions in Wokingham were on A roads. Unclassified roads featured over a third (34.5%) of collisions whilst 15% took place on B roads and 10% took place on motorways.

Figure 4.11: Wokingham collisions by road class (2018-2022)

Wokingham collisions by road class (2018-2022)
4.1.1.9.2 Carriageway type

Figure 4.12 shows collisions in Wokingham by carriageway type of road. Nearly three quarters (74%) of collisions were on single carriageway roads, whilst 13% were on dual carriageways. Around 10% were on roundabouts, 2% on one-way streets and 2% on slip roads.

Figure 4.12: Wokingham collisions by road carriageway type (2018-2022)

Wokingham collisions by road carriageway type (2018-2022)
4.1.1.9.3 Junction type

Figure 4.13 shows collisions in Wokingham by the presence and type of junction. Over half (55%) of collisions in Wokingham took place at a junction. Of these, most were at a normal junction (34%), whilst 20% were at a roundabout. Six percent of collisions occurred at a private driveway.

Figure 4.13: Wokingham collisions by junction type (2018-2022)

Wokingham collisions by junction type (2018-2022)
4.1.1.9.4 Junction control

Figure 4.14 shows collisions in Wokingham by the type of junction control (if the collision took place at a junction). Of those collisions that took place at a junction, over three quarters (82%) were at a give way or uncontrolled junction. Sixteen percent of collisions occurred at traffic signal controlled junctions. Very few collisions, just 2% occurred at junctions with stop signs.

Figure 4.14: Wokingham collisions by junction control (2018-2022)

Wokingham collisions by junction control (2018-2022)

4.2 Collisions on Urban Roads in Wokingham

The following section investigates collisions in Wokingham which occurred on urban roads. For an explanation of how urban roads have been identified in Wokingham, please refer to Section 5.1.2.1.1.

4.2.1 Rates

4.2.1.1 Collisions on urban roads per 100km of urban road

Figure 4.20 below shows the rate of average annual collisions on urban roads between 2018 and 2022 per 100km of urban road in Wokingham compared to the national and regional rates, and those of the most similar comparators.

On Wokingham’s urban roads between 2018 and 2022 there was a collision rate of 22 collisions per year, per 100km of urban road.

Figure 4.20: Annual average collisions on urban roads per 100km of urban road (2018-2022)

Annual average collisions on urban roads per 100km of urban road (2018-2022)

4.2.1.2 Comparisons

Wokingham’s urban road collisions rate was less than half the national urban road collision rate and the regional rate. This was also 38% below the overall Berkshire rate. Within Berkshire, West Berkshire has the lowest urban roads collision rate, followed by Wokingham. The urban road collision rate for Windsor & Maidenhead is slightly higher at 25 collisions per year, per 100km urban road while Reading & Slough’s rates are much higher at 63 and 70 respectively.

4.2.1.4 Collision dynamics and driver actions on urban roads

4.2.1.4.1 Collision dynamics

Figure 4.22 shows collisions on urban roads in Wokingham by the dynamics resulting in the collision. A description of collision dynamics and the derivation using STATS19 data is outlined in section 5.1.4 of this report.

The breakdown of collisions by collision dynamic is similar on urban roads to all roads. Over a quarter of collisions (26%) involved no impact between vehicles. Thirty-two percent involved either rear impact or side impact with 12% of collisions on urban roads involving head-on impact between vehicles.

Figure 4.22: Wokingham collisions on urban roads by collision dynamics (2018-2022)

Wokingham collisions on urban roads by collision dynamics (2018-2022)
4.2.1.4.2 Driver actions

Figure 4.23 shows collisions on urban roads in Wokingham by the presence of different driver actions. An explanation of the derivation of driver actions and the definitions are included in section 5.1.5 of this report. Note that collisions can have multiple driver behaviours present, so there may be some overlap in numbers.

Drivers making right turns were involved in the largest number of collisions on Wokingham’s urban roads, followed by slow maneouvres, such as slowing down, stopping or waiting to proceed. Drivers leaving the carriageway in run-off collisions on urban roads accounted for just over 10% of urban road collisions.

Figure 4.23: Wokingham collisions on urban roads by driver actions (2018-2022)

Wokingham collisions on urban roads by driver actions (2018-2022)

4.2.1.5 Urban road environment

4.2.1.5.1 Road class

Figure 4.24 shows collisions on urban roads in Wokingham by class of road. Compared to all roads, more urban road collisions take place on unclassified roads (44% compared to 35%) and fewer take place on motorways (2% compared to 10%).

Figure 4.24: Wokingham collisions on urban roads by road class (2018-2022)

Wokingham collisions on urban roads by road class (2018-2022)
4.2.1.5.2 Carriageway type

Figure 4.25 shows collisions on urban roads in Wokingham by carriageway type of road. When compared to all roads, a lower proportion of urban collisions occur on dual carriageways (6% compared to 13%) whilst a higher proportion occur on single carriageways.

Figure 4.25: Wokingham collisions on urban roads by road carriageway type (2018-2022)

Wokingham collisions on urban roads by road carriageway type (2018-2022)
4.2.1.5.3 Junction type

Figure 4.26 shows collisions on urban roads in Wokingham by the presence and type of junction. Just under a third of urban collisions took place away from a junction. Of the remaining 70%, the majority (40%) took place at a normal junction and 22% at a roundabout.

Figure 4.26: Wokingham collisions on urban roads by junction type (2018-2022)

Wokingham collisions on urban roads by junction type (2018-2022)
4.2.1.5.4 Junction control

Figure 4.27 shows collisions on urban roads in Wokingham by the type of junction control (if the collision took place at a junction). Like all roads in Wokingham, for those collisions that took place at junctions, the vast majority (84%) took place at give way or uncontrolled junctions. Fifteen percent of urban collisions at junctions, occurred at traffic signal controlled junctions and only 1% at junctions with stop signs.

Figure 4.27: Wokingham collisions on urban roads by junction control (2018-2022)

Wokingham collisions on urban roads by junction control (2018-2022)

4.3 Collisions on Rural Roads in Wokingham

The following section investigates collisions in Wokingham which occurred on rural roads. For an explanation of how rural roads have been identified in Wokingham, please refer to Section 5.1.2.1.1.

4.3.1 Rates

4.3.1.1 Collisions on rural roads per 100km of rural road

Figure 4.33 below shows the rate of average annual collisions on rural roads between 2018 and 2022 per 100km of rural road in Wokingham compared to the national and regional rates, and those of the most similar comparators.

Wokingham’s rural road collision rate was 23 collisions per year, per 100km of rural road.

Figure 4.33: Annual average collisions on rural roads per 100km of rural road (2018-2022)

Annual average collisions on rural roads per 100km of rural road (2018-2022)

4.3.1.2 Comparisons

Wokingham’s rural road collision rate is 62% higher than the national rate and 10% higher than the overall rate for Berkshire. This is 15% lower than the South East’s regional rate. Wokingham’s rate continues to be the second lowest rate in Berkshire, above West Berkshire.

4.3.1.4 Collision dynamics and driver actions on rural roads

4.3.1.4.1 Collision dynamics

Figure 4.35 shows collisions on rural roads in Wokingham by the dynamics resulting in the collision. A description of collision dynamics and the derivation using STATS19 data is outlined in section 5.1.4 of this report.

The breakdown of collisions by the dynamics of the collision is broadly similar on rural roads to all roads although the percentage of vehicles involved in collisions with no impact is slightly higher at 32% (compared to 29%). Head-on collisions accounted for the smallest proportion of collisions on rural roads at 11%, followed by side-impact collisions at 12%. Rear impact collisions accounted for the largest proportion of those collisions on rural roads involving impact between vehicles, accounting for 20%.

Figure 4.35: Wokingham collisions on rural roads by collision dynamics (2018-2022)

Wokingham collisions on rural roads by collision dynamics (2018-2022)
4.3.1.4.2 Driver actions

Figure 4.36 shows collisions on rural roads in Wokingham by the presence of different driver actions. An explanation of the derivation of driver actions and the definitions are included in section 5.1.5 of this report. Note that collisions can have multiple driver behaviours present, so there may be some overlap in numbers. Aligning with typical collision dynamic trends on non-built-up roads, run-off was the most prevalent driver action for collisions on rural roads, particularly near-side run-offs.

Figure 4.36: Wokingham collisions on rural roads by driver actions (2018-2022)

Wokingham collisions on rural roads by driver actions (2018-2022)

4.3.1.5 Rural road environment

4.3.1.5.1 Road class

Figure 4.37 shows collisions on rural roads in Wokingham by class of road. Compared to all roads, more rural road collisions take place on the classified road network collectively - Motorways (17%), A class (42%) and B class (17%) roads than the unclassified road network (24%).

Figure 4.37: Wokingham collisions on rural roads by road class (2018-2022)

Wokingham collisions on rural roads by road class (2018-2022)
4.3.1.5.2 Carriageway type

Figure 4.38 shows collisions on rural roads in Wokingham by carriageway type of road. When compared to all roads, a higher proportion of rural collisions take place on dual carriageways (20% compared to 13%) whilst a lower proportion take place on single carriageways (68% compared to 74%).

Figure 4.38: Wokingham collisions on rural roads by road carriageway type (2018-2022)

Wokingham collisions on rural roads by road carriageway type (2018-2022)
4.3.1.5.3 Junction type

Figure 4.39 shows collisions on rural roads in Wokingham by the presence and type of junction.

Again aligning with collision dynamics and drive behaviour trends, almost half (49%) of all rural road collisions occurred away from a junction. Of those collisions that did take place at a junction, 26% occurred at a normal junction and 18% at a roundabout.

Figure 4.39: Wokingham collisions on rural roads by junction type (2018-2022)

Wokingham collisions on rural roads by junction type (2018-2022)
4.3.1.5.4 Junction control

Figure 4.40 shows collisions on rural roads in Wokingham by the type of junction control (if the collision took place at a junction). Like all roads and urban roads the majority of collisions that occurred at a junction took place at a give way or uncontrolled junction (79%) although the percentage of these on rural roads is the smallest proportion across each of the 3 road groups. As a result, more collisions on rural roads took place at traffic signal controlled junctions (17%) and 4% took place at junctions with stop signs (compared to 1% on all roads).

Figure 4.40: Wokingham collisions on rural roads by junction control (2018-2022)

Wokingham collisions on rural roads by junction control (2018-2022)

4.4 Contributory Factors

Each section below examines trends in reported collisions on Wokingham’s roads involving groups of related contributory factors (CFs). For each group, the total number of collisions in which any CF in the group was recorded has been determined. To provide comparative context, each chart also shows the three-year average of all police attended collisions with recorded CFs.
For more information about CFs and the techniques used to analyse them see section 5.1.6. For a complete list of all CFs and CF groupings used by Agilysis, see section 5.4.

4.4.1 Speed Related

This section examines collisions, by severity, where at least one of the contributory factors 306 Exceeding speed limit and/or 307 Travelling too fast for conditions was attributed to one or more vehicles. This may include some instances where these factors were applied more than once in the same collision.

4.4.1.2 Comparisons

Figure 4.48 shows collisions on Wokingham’s roads where at least one of the speed choice CFs was recorded, as a percentage of all officer attended collisions where any CF was recorded. Also shown are the national, regional and comparator authorities’ percentages.

Nine per cent of officer attended collisions in Wokingham were attributed a speed choice CF. That is lower than the proportions seen nationally, regionally and across Berkshire as a whole. Within Berkshire, Reading continues to have a lower proportion of speed related collisions. Of the comparator authorities, Wokingham’s percentage of speed related collisions is higher than that of Surrey Heath (7.7%), but lower than Hart, South Cambridgeshire and South Oxfordshire.

Figure 4.48: Percentage of collisions in Wokingham and comparators where CF306 and/or CF307 were recorded (2018-2022)

Percentage of collisions in Wokingham and comparators where CF306 and/or CF307 were recorded (2018-2022)

4.4.2 Impairment

This section examines collisions, by severity, where at least one of the contributory factors 501 Impaired by alcohol and/or 502 Impaired by drugs (illicit or medicinal) was attributed to one or more drivers. This may include some instances where these factors were applied more than once in the same collision.

4.4.2.2 Comparisons

Figure 4.51 shows collisions on Wokingham’s roads where at least one of the impairment CFs was recorded, as a percentage of all officer attended collisions where any CF was recorded. Also shown are the national, regional and comparator authorities’ percentages.

Of Wokingham’s officer attended collisions, 9.7% were attributed an impairment related CF. this is higher than the national and regional levels and that for Berkshire as a whole. Slough continues to have the lowest proportions of impairment related collisions with West Berkshire second highest, just below Wokingham. Compared to other similar authorities, Wokingham has a higher proportion of impairment attributed collisions.

Figure 4.51: Percentage of collisions in Wokingham and comparators where CF501 and/or CF502 were recorded (2018-2022)

Percentage of collisions in Wokingham and comparators where CF501 and/or CF502 were recorded (2018-2022)

4.4.3 Road Surface Conditions

This section examines collisions, by severity, where at least one of the CFs 101 Poor or defective road surface, 102 Deposit on road (e.g. oil, mud, chippings) and/or 103 Slippery road (due to weather) was attributed. This may include some instances where more than one of these factors were applied in the same collision.

4.4.3.2 Comparisons

Figure 4.54 shows collisions on Wokingham’s roads where at least one of the road surface CFs was recorded, as a percentage of all officer attended collisions where any CF was recorded. Also shown are the national, regional and comparator authorities’ percentages.

Although the percentage of Wokingham’s officer attended collisions that were attributed a road surface related CF has increased from 5.9% (2017 - 2021) to 6.5% (2018 - 2022) this rate is still below the national and regional rates. Within Berkshire, Reading and Slough have lower rates while West Berkshire and Windsor & Maidenhead have higher rates. Beyond the neighbouring authorities, Wokingham’s road surface related collisions rate is higher than Surrey Heath but lower than Hart, South Oxfordshire and South Cambridgeshire.

Figure 4.54: Percentage of collisions in Wokingham and comparators where CF101 and/or CF102 and/or CF103 were recorded (2018-2022)

Percentage of collisions in Wokingham and comparators where CF101 and/or CF102 and/or CF103 were recorded (2018-2022)

4.4.4 Control Errors

This section examines collisions, by severity, where at least one of the CFs 408 Sudden braking, 409 Swerved and/or 410 Loss of Control was attributed. This may include some instances where more than one of these factors were applied in the same collision.

4.4.4.2 Comparisons

Figure 4.57 shows collisions on Wokingham’s roads where at least one of the control error CFs was recorded, as a percentage of all officer attended collisions where any CF was recorded. Also shown are the national, regional and comparator authorities’ percentages.

In Wokingham, 17.4% of collisions attended by a police officer were attributed a control error CF. This is in line with both the GB and South East percentages. Of all the comparators, Wokingham’s percentage is in line with Windsor & Maidenhead. These are higher than the other Berkshire authorities of Slough and Reading and the external comparator of Surrey Heath.

Figure 4.57: Percentage of collisions in Wokingham and comparators where CF408 and/or CF409 and/or CF410 were recorded (2018-2022)

Percentage of collisions in Wokingham and comparators where CF408 and/or CF409 and/or CF410 were recorded (2018-2022)

4.4.5 Unsafe Behaviour

This section examines collisions, by severity, where at least one of the CFs 601 Aggressive driving, and/or 602 Careless, reckless or in a hurry was attributed. This may include some instances where more than one of these factors were applied in the same collision.

4.4.5.2 Comparisons

Figure 4.60 shows collisions on Wokingham’s roads where at least one of the unsafe behaviour CFs was recorded, as a percentage of all officer attended collisions where any CF was recorded. Also shown are the national, regional and comparator authorities’ percentages.

In Wokingham, 19.8% of collisions attended by a police officer were attributed an unsafe behaviour CF. This is higher than the national rate but lower than the regional rate for the South East. It is also lower than the rate for Berkshire as a whole. Compared to neighbouring authorities Wokingham has a similar rate to Windsor & Maidenhead but slightly higher than Reading and lower than Slough. Of the comparator authorities the proportion of collisions attributed an unsafe behaviour CF in Hart is the lowest and Surrey Heath the highest.

Figure 4.60: Percentage of collisions in Wokingham and comparators where CF601 and/or CF602 were recorded (2018-2022)

Percentage of collisions in Wokingham and comparators where CF601 and/or CF602 were recorded (2018-2022)

4.4.6 Distraction

This section examines collisions, by severity, where at least one of the CFs 508 Driver using mobile phone, 509 Distraction in vehicle and/or 510 Distraction outside vehicle was attributed. This may include some instances where more than one of these factors were applied in the same collision.

4.4.6.2 Comparisons

Figure 4.63 shows collisions on Wokingham’s roads where at least one of the distraction CFs was recorded, as a percentage of all officer attended collisions where any CF was recorded. Also shown are the national, regional and comparator authorities’ percentages.

Between 2018 and 2022, 7% of collisions in Wokingham were attributed a distraction related CF. This is higher than the national rate, the rate for the South East region and all neighbouring authorities. Wokingham’s proportion of distraction related collisions is comparable with South Cambridgeshire, South Oxfordshire and Surrey Heath.

Figure 4.63: Percentage of collisions in Wokingham and comparators where CF508 and/or CF509 and/or CF510 were recorded (2018-2022)

Percentage of collisions in Wokingham and comparators where CF508 and/or CF509 and/or CF510 were recorded (2018-2022)

4.4.7 Medically Unfit

This section examines collisions, by severity, where at least one of the CFs 504 Uncorrected, defective eyesight and/or 505 Illness or disability, mental or physical was attributed. This may include some instances where more than one of these factors were applied in the same collision.

4.4.7.2 Comparisons

Figure 4.66 shows collisions on Wokingham’s roads where at least one of the medically unfit CFs was recorded, as a percentage of all officer attended collisions where any CF was recorded. Also shown are the national, regional and comparator authorities’ percentages.

Four per cent of collisions in Wokingham attended by a police officer were attributed a medically unfit CF. This is higher than the national and regional rates and comparable to the rate for Berkshire as a whole. Within Berkshire, Wokingham’s rate is higher than Reading, Slough and Windsor & Maidenhead but lower than Bracknell Forest & West Berkshire. Of the comparator authorities just South Oxfordshire has a higher rate of medically unfit related collisions.

Figure 4.66: Percentage of collisions in Wokingham and comparators where CF504 and/or CF505 were recorded (2018-2022)

Percentage of collisions in Wokingham and comparators where CF504 and/or CF505 were recorded (2018-2022)

4.4.8 Close Following

This section examines collisions, by severity, where the CF 308 Following too close was attributed.

4.4.8.2 Comparisons

Figure 4.69 shows collisions on Wokingham’s roads where the close following CF was recorded, as a percentage of all officer attended collisions where any CF was recorded. Also shown are the national, regional and comparator authorities’ percentages.

The percentage of close following related collisions in Wokingham at just 3.5% is lower than the national (4.2%) and regional (4.5%) rates. It is also lower than all other authorities within Berkshire with the exception of Reading (2.6%). Likewise Wokingham’s rate, aside from Surrey Heath, is lower than all comparator authorities.

Figure 4.69: Percentage of collisions in Wokingham and comparators where CF308 was recorded (2018-2022)

Percentage of collisions in Wokingham and comparators where CF308 was recorded (2018-2022)

5 Appendices

5.1 Analytical Techniques

5.1.1 Resident road users

Casualty and driver postcodes in STATS19 make it possible to identify where casualties from Wokingham reside. Thematic maps are used to illustrate the number of casualties per head of population from each small area in Wokingham. Areas on maps are progressively coloured, indicating annual average rates relative to the population of that area.

The geographical units used for this analysis are based on similar populations, which enables meaningful comparative analysis within and between authorities. In England and Wales the areas typically used are super output areas as defined by the Office for National Statistics (ONS). Where appropriate, lower level small areas are employed: for England and Wales these are lower layer super output areas (LSOAs) of around 1,600 residents on average. In some cases, larger groupings are used, as is the case in MAST Online: for England and Wales these are middle layer super output areas (MSOAs) with an average of nearly 8,000 residents each.

MAST Online has been used to determine the casualty figures for Wokingham’s residents injured in road collisions anywhere in Britain. Using national population figures (by age where appropriate), casualty and driver/rider involvement rates per head of population have been calculated. Charts have been devised which compare the local rates with the equivalent figures for Great Britain and for selected comparators. Trend analysis examines resident road user collision involvement over time and by severity, and additional trends are explored depending on road user type.

Where appropriate, socio-demographic analysis is conducted to provide insight into the backgrounds of people from Wokingham who are involved in collisions, either as casualties or motor vehicle users. Socio-demographic profiling examines age breakdowns, and for some road user groups includes analysis using Acorn segmentation, deprivation and/or rurality. More information on Acorn is provided later in this section.

5.1.1.1 Acorn

Insight into the lifestyles of Wokingham resident road casualties and motor vehicle users can be provided through socio demographic analysis. RSA Acorn profiling uses CACI’s Acorn cross-channel classification system2, which is assigned uniquely for each casualty and vehicle user based on individual postcodes in STATS19 records. Typically, nearly 85% of casualty and driver STATS19 records can be matched to Acorn Types, so residency analysis is based on about five out of six Wokingham residents involved in reported injury collisions.

Acorn is intended to provide an accurate and comprehensive view of citizens and their needs by describing them in terms of demographics, lifestyle, culture and behaviour. By analysing data from hundreds of different sources, and segmenting UK postcodes by common characteristics, Acorn provides a detailed understanding of the various types of people who make up customer bases and catchment areas.

Acorn presently classifies the community represented by each UK postcode into one of 7 categories, 22 Groups and 65 Types. Each Group embraces between 3 and 6 Types. A complete list of Acorn Types is provided in 5.2.1 whilst profiles and distribution for the Acorn Types identified in this Area Profile as providing insight on Wokingham’s residents are detailed in 5.2.2.

This profile displays Acorn analysis as dual series column charts, to facilitate quick and easy insight into residents and relative risk. In these charts, the wider background columns denote the absolute number of Wokingham resident casualties or drivers in each Acorn Type or Group, corresponding to the value axis to the left of the chart. The columns in the foreground provide an index for each Acorn Type or Group. These indices are 100 based, where a value of 100 indicates the number of casualties or drivers shown by the corresponding background column is exactly in proportion to the population of communities in Wokingham where that Type or Group predominates. Indices over 100 indicate over representation of that Type among casualties or motor vehicle users relative to the population: for example, a value of 200 would signify that people resident in communities of that Type were involved in collisions at twice the expected rate. Conversely, indices below 100 suggest under representation, so an index of 50 would imply half the expected rate. Inevitably, index values become less significant as numbers of involved residents decrease, because increased random fluctuations tend to decrease levels of confidence.

Where appropriate, additional Acorn profiles for drivers may be provided with indices based on CACI’s estimate of the average annual mileage typically driven by each Group or Type. These profiles help to identify situations where exposure to road risk for some communities is out of proportion to their population due to unusually high or low levels of vehicle use.

5.1.1.2 Deprivation

Deprivation levels are examined using UK Index of Multiple Deprivation (IMD) values. IMD is calculated by the Office for National Statistics (ONS), the Scottish Government and the Welsh Government, and uses a range of economic, social and housing data to generate a single deprivation score for each small area in the country. This profile uses deciles, which are ten groups of equal frequency ranging from the 10% most deprived areas to the 10% least deprived. It should be remembered that indices of multiple deprivation include income, employment, health, education, access to services and living environment and are not merely about relative wealth.

In order to interpret deprivation more accurately at local level, this profile includes indexed IMD charts. Indices in these charts show risk relative to the predominance of each IMD decile in the population of Wokingham and can be interpreted in the same way as indices on Acorn charts as explained in the preceding section.

5.1.2 Collisions

MAST Online has been used to determine average annual road injury collision levels for Wokingham and relevant comparator areas. Dividing this annual rate by road length in each area generates an annual collision rate per kilometre of road, which allows direct comparisons to be made between authorities. Road length data have been taken from central government figures, and where required have been calculated separately for different road classes and environments. Charts have been devised which compare local rates with the equivalent figures for Great Britain and comparator highway authorities. District authorities cannot be included, as road length data is only available at highway authority level.

Trend analysis examines numbers of collisions on Wokingham’s roads over time and by severity, with additional trends explored, sometimes classified by kinds of road network. In order to determine the distribution of collisions within Wokingham, maps show the number of collisions in each small area, divided by the total road length (in kilometres) within that small area

5.1.2.1 Contrasting kinds of road network

Road networks vary considerably across the country. It is often useful to analyse and compare collision rates between authorities on certain kinds of road. Ideally such comparisons would take traffic flow into account, so collision rates per vehicle distance travelled could be calculated. However, traffic flow data for different kinds of road network is not available, so this profile can only calculate collision rates using road length. Road length data by kind of road network has been taken from DfT figures where possible. As with all collisions, trend charts are provided in addition to rate comparison charts.

5.1.2.1.1 Rurality

Within Wokingham, the road network has been split into either Urban and Rural or SRN and local roads. These types have been analysed separately under Sections 4.2 and 4.3 in the Area Profile. Routes were split into urban and rural in accordance with the ONS rural/urban classifications by LSOA (Lower Layer Super Output Area). Note that the term ‘urban’ both in the ONS classification and in this report denotes an area which forms part of a contiguous conurbation with a total population of more than 10,000.

5.1.3 Comparators

In order to put the figures for Wokingham into context, comparisons with other areas have been made.

On a regional level, all of the other Berkshire authorities have been analysed to show how resident road user and collision rates differ between authority areas within the county.

It is not always appropriate to compare an authority solely against it’s neighbours , especially when the authority has unique characteristics in terms of socio-demographic composition and/or road network. Because of the size of Wokingham , only district authorities have been selected for comparison. The chosen four districts are:

Local Authority District
Hart District
South Cambridgeshire District
South Oxfordshire District
Surrey Heath Borough

5.1.4 Collision Dynamics

Many collisions entail some (or all) of the vehicles involved coming into direct conflict with each other. To maximise insight into such incidents, Agilysis categorises all collisions by their Collision Dynamic, based on the nature of inter-vehicle conflicts they comprised. This assessment is based on the directions in which vehicles were travelling, and the points of impact at which they first made contact.

The Collision Dynamic categories (arranged in the hierarchical order in which they are applied) are as follows:

  • No Conflict
  • Head On
  • Shunt
  • Side Impact
  • Other Conflict
  • Conflict Unknown

A collision is defined as No Conflict if: it only involved one non-parked vehicle OR all involved non-parked vehicles had no impact OR all bar one of the involved non-parked vehicles had no impact.

A collision is defined as Head On if: any involved non-parked vehicle which had a front impact was travelling in a direction which differed by between 135⁰ and 225⁰ from the path of another involved non-parked vehicle which had a non-rear impact.

A collision is defined as a Shunt if: the collision was not a Head On AND; any involved non-parked vehicle which had a rear impact was travelling in a direction which only differed by up to 45⁰ either way from the path of another involved non-parked vehicle which had a non-rear impact.

A collision is defined as a Side Impact if: the collision was not a Head On or Shunt AND; any involved non-parked vehicle which had a side impact was travelling in a direction which differed by 45⁰ to 135⁰ either way from the path of another involved non-parked vehicle which had a non-rear impact.

A collision is defined as Other Conflict if: the collision was not a Head On, Shunt or Side Impact AND; at least two involved non-parked vehicles with known directions of travel had any impact.

A collision is defined as Conflict Unknown if: the collision was not a No Impact, Head On, Shunt, Side Impact or Other Impact (NOTE: this includes cases where data for first point of impact and/or direction of travel was missing or unknown, in a manner which precluded the application of any other definition).

5.1.4.1 Limitations

Certain vagaries inherent in STATS19 recording may confound this categorisation in some circumstances. These, along with the available mitigations, are listed below.

  1. Collisions involving more than two vehicles may comprise multiple types of conflict within the same incident, which STATS19 data by its nature cannot always distinguish with certainty. Collision Dynamics defines the primary dynamic of such collisions by using a ‘hierarchy’ of conflicts which gives certain types of conflict precedence over others.
    • In some circumstances it may be preferable to mitigate this uncertainty by analysing two vehicle collisions only.
  2. Recorded first points of impact may refer to contact with pedestrians or other objects, rather than with other vehicles. From STATS19 data, it is not always possible to ascertain with certainty to what counterpart any given impact refers.
    • For this reason, in some circumstances it may be preferable to mitigate this uncertainty by analysing collisions separately where injured pedestrians and/or impact with other objects were recorded.

5.1.5 Driver Actions

The derivation of ‘Driver Action’ from STATS19 data is carried out using a combination of two data collection fields, ‘Vehicle Manoeuvres’ and ‘Vehicle leaving carriageway’. The definitions of driver action used in this report are as follows:

Driver Action Definition
Involved Slow Manoeuvre Vehicle was stopping, stationary or moving off
Involved Right Turn Vehicle was turning right, or waiting to do so
Involved Left Turn Vehicle was turning left, or waiting to do so
Involved Runoff Combination of ‘Involved Runoff Other’ and ‘Involved Runoff Nearside’
Involved Runoff Other Vehicle left carriageway in any other fashion
Involved Runoff Nearside Vehicle left carriageway to the nearside (whether rebounded or not)

5.1.6 Contributory factors

Police officers who attended the scene of an injury collision may choose to record certain contributory factors (CFs) which in the officer’s view were likely to be related to the incident. Up to six CFs can be recorded for each collision. CFs reflect the officer’s opinion at the time of reporting, but may not be the result of extensive investigation. Consequently, CFs should be regarded only as a general guide for identifying factors as possible concerns.

In all CF analysis, only collisions which were both attended by a police officer and for which at least one factor was recorded are included. Since multiple CFs can be recorded for a single collision, the same incidents may be included in analysis of more than one CF.

In CF analysis specifically related to pedestrians, only CFs directly assigned either to pedestrian casualties or to drivers and riders who first hit a pedestrian casualty are analysed. For ease of analysis and interpretation RSA often organises CFs into groupings. A complete list of all CFs and their groupings may be found in section 5.4.

5.2 Acorn

This section provides information on all of the Acorn Types and more detailed analysis of the specific Types identified as being of interest to Wokingham. More information on what Acorn is can be found in section 5.1.1.1.

5.2.1 Complete list of Acorn Types

Below is a complete list of all the Acorn Types, with descriptions, shown in the Acorn Group to which they belong.

A - Exclusive Addresses
A1 High-flyers in luxury apartments and townhouses ###################
A2 Wealthy, gentrified areas #########################################
A3 Asset-rich, out-of-town older families ############################
B - Flourishing Capital
B4 High-end professionals in city flats ##############################
B5 Successful young families in smart urban areas ####################
C - Upmarket Families
C6 Executives in expensive suburban houses ###########################
C7 Prosperous families in green-belt areas with substantial homes ####
D - Commuter-Belt Wealth
D8 Affluent, older homeowners ########################################
D9 Families and couples in comfortable homes #########################
D10 Well-off families in larger semis #################################
D11 Mature and moneyed out-of-towners #################################
D12 Well-to-do empty nesters in detached houses #######################
E - Prosperous Professionals
E13 Families in leafy suburbs #########################################
E14 Upmarket young families in terraces ###############################
E15 Educated professionals renting flats ##############################
F - Mature Success
F16 Families and couples in detached houses ###########################
F17 Older, rural empty nesters and couples ############################
F18 Countryside retirees in spacious houses ###########################
F19 Sophisticated couples living comfortably in detached homes ########
G - Settled Suburbia
G20 Mixed lifestages in semi-detached homes ###########################
G21 Mid-life suburban living ##########################################
H - Metropolitan Surroundings
H22 Younger families and sharers in city terraces #####################
H23 Culturally diverse suburban families ##############################
I - Up-and-Coming Urbanites
I24 Young professionals renting city flats ############################
I25 Privately renting students and house sharers ######################
I26 Younger couples and singles in flats ##############################
J - Aspiring Communities
J27 Professional families and couples in suburban, owner-occupied areas
J28 Families and couples in terraces ##################################
K - Semi-Rural Maturity
K29 Senior home-owning couples ########################################
K30 Empty nesters in owner-occupied detached homes ####################
K31 Comfortable, home-owning families and empty nesters ###############
K32 Older comfortable families and couples in detached, rural properties
K33 Retirees in semi-detached and detached properties #################
L - Traditional Homeowners
L34 Older owner-occupier households in semis ##########################
L35 Settled communities, semi-detached properties #####################
M - Family Renters
M36 Cost-conscious families in terraces ###############################
M37 Restricted residents, socially renting ############################
N - Urban Diversity
N38 Younger families, multi-occupancy and rented households ###########
N39 Diverse communities in smaller semis and terraces #################
N40 Young families, limited means in terraced metropolitan areas ######
O - Stable Seniors
O41 Living on modest means in terraces ################################
O42 Retired homeowners in semi-detached and detached houses ###########
O43 Older couples living in detached houses, rural communities ########
P - Tenant Living
P44 Urban, aspiring flat dwellers #####################################
P45 Privately renting squeezed professionals in flats #################
P46 Sharers and students in private rentals ###########################
P47 Singles and couples in rented flats ###############################
Q - Limited Budgets
Q48 Routine occupations, socially renting families in semis ###########
Q49 Socially renting single adult households ##########################
R - Hard-Up Households
R50 Single-parent families in terraced housing ########################
R51 Older, single-person households on the outskirts of town ##########
R52 Socially renting families in terraces #############################
S - Cash-Strapped Families
S53 Diverse families and sharers in flats #############################
S54 Young families in socially rented semis ###########################
S55 Families in low-value terraced housing ############################
S56 Diverse young families in rented terraces and flats ###############
T - Constrained Pensioners
T57 Older renters in flats and tenements ##############################
T58 Poorer pensioners in semis ########################################
U - Challenging Circumstances
U59 Students and sharers in multi-occupancy flats #####################
U60 Socially renting single adult households in flats #################
U61 Socially rented flats, singles and pensioners #####################
V - Not Private Households
V62 Students in halls of residence ####################################
V63 Active communal populations #######################################
V64 Inactive communal populations #####################################
V65 Non residential postcodes #########################################

5.2.2 Profile and distribution for selected Acorn Types

The table below shows Acorn Types identified by socio-demographic profiling of the resident casualties and resident drivers sections of the report, with some of the main characteristics of these Types. These can be used to create a picture of the target audience in terms of economic and educational position; family life; and transport preferences including mileage and car ownership. This information is invaluable for understanding target audiences and knowing how to communicate with them.

D8 G20 M37 Q49

Affluent, older homeowners

Mixed lifestages in semi-detached homes

Restricted residents, socially renting

Socially renting single adult households

Affluent, older homeowners are professional families living in large, detached homes located in areas of the country which are within easy reach of a major conurbation. They are married couples aged in their fifties and early sixties with young adult children.

With substantial household incomes many have paid off the mortgage on their home or are close to doing so and looking ahead to retirement.

Mixed life stages in semi-detached homes are stable middle class neighbourhoods with a range of household types from young families through to empty nesters. Often areas of average population density, housing often comprises of three or four bedroom semi-detached properties of medium value either bought with a mortgage or owned outright. Household incomes are typically above the national average.

Restricted residents, socially renting are families living in semi-detached or terraced houses. They are typically aged in their late thirties and forties with school-age children. The tenure of properties is mixed between homeowners and those in social housing, or renting from the local housing authority. Household income levels are in line with UK average.

Socially renting single adult households are neighbourhoods with a high proportion of socially rented flats and smaller homes that are more likely to be occupied by single adults and single-parent families. They are typically aged in their late thirties, forties or fifties with school-age children. Levels of both household and disposable income are well below the UK average.

5.3 Data Tables

All Casualties - Wokingham Residents (3.1.1)
Year Fatal Serious Slight Total
2013 1 52 302 355
2014 4 41 317 362
2015 2 42 319 363
2016 3 51 275 329
2017 5 37 216 258
2018 5 31 226 262
2019 1 30 204 235
2020 2 25 167 194
2021 4 39 192 235
2022 4 36 217 257
Total 31 384 2435 2850
Child Casualties - Wokingham Residents (3.1.2)
Year Fatal Serious Slight Total
2013 0 5 26 31
2014 0 5 17 22
2015 0 5 30 35
2016 0 4 26 30
2017 0 5 17 22
2018 0 3 25 28
2019 0 1 24 25
2020 1 2 22 25
2021 0 4 22 26
2022 0 4 14 18
Total 1 38 223 262
Pedestrian Casualties - Wokingham Residents (??)
Year Fatal Serious Slight Total
2013 0 7 26 33
2014 2 10 24 36
2015 1 7 27 35
2016 0 4 31 35
2017 1 9 18 28
2018 3 4 17 24
2019 0 7 20 27
2020 0 6 16 22
2021 1 7 20 28
2022 0 6 15 21
Total 8 67 214 289
Pedal Cycle User Casualties - Wokingham Residents (3.1.5)
Year Fatal Serious Slight Total
2013 0 11 41 52
2014 0 8 38 46
2015 0 6 35 41
2016 0 13 39 52
2017 1 5 31 37
2018 0 7 32 39
2019 0 5 33 38
2020 2 5 32 39
2021 0 4 27 31
2022 0 4 22 26
Total 3 68 330 401
Motor Vehicle Drivers Involved in Injury Collisions - Wokingham Residents (3.2.1)
Year Fatal Serious Slight Total
2013 4 56 322 382
2014 4 49 343 396
2015 5 41 359 405
2016 5 49 297 351
2017 1 35 251 287
2018 8 37 232 277
2019 2 31 212 245
2020 5 24 176 205
2021 5 37 196 238
2022 8 39 221 268
Total 47 398 2609 3054
Motorcyclists Involved in Injury Collisions - Wokingham Residents (3.3.1)
Year Fatal Serious Slight Total
2013 0 17 25 42
2014 1 9 28 38
2015 0 17 21 38
2016 2 17 27 46
2017 0 9 17 26
2018 2 7 21 30
2019 1 6 21 28
2020 0 3 13 16
2021 1 12 17 30
2022 0 11 27 38
Total 7 108 217 332
Young Adult Drivers Involved in Injury Collisions - Wokingham Residents (3.2.3)
Year Fatal Serious Slight Total
2013 1 7 49 57
2014 0 7 53 60
2015 0 3 55 58
2016 0 12 60 72
2017 0 7 48 55
2018 0 6 35 41
2019 0 6 34 40
2020 1 2 23 26
2021 1 6 25 32
2022 0 4 26 30
Total 3 60 408 471
All Collisions - Wokingham Roads (4.1)
Year Fatal Serious Slight Total
2013 1 49 200 250
2014 3 44 218 265
2015 1 37 226 264
2016 3 39 204 246
2017 4 39 168 211
2018 3 35 164 202
2019 0 22 146 168
2020 3 28 124 155
2021 3 27 156 186
2022 1 23 159 183
Total 22 343 1765 2130
Urban Collisions - Wokingham Roads (4.2)
Year Fatal Serious Slight Total
2013 0 20 97 117
2014 1 24 106 131
2015 1 20 127 148
2016 2 17 101 120
2017 2 20 99 121
2018 2 17 89 108
2019 0 6 87 93
2020 1 10 64 75
2021 1 18 79 98
2022 1 12 91 104
Total 11 164 940 1115
Rural Collisions - Wokingham Roads (4.3)
Year Fatal Serious Slight Total
2013 1 29 103 133
2014 2 20 112 134
2015 0 17 99 116
2016 1 22 103 126
2017 2 19 69 90
2018 1 18 75 94
2019 0 16 59 75
2020 2 18 60 80
2021 2 9 77 88
2022 0 11 68 79
Total 11 179 825 1015
Collisions by Hour of the Day (Weekdays) - Wokingham Roads (4.1.1.5)
Time of Day Fatal Serious Slight Total
Midnight 1 3 5 9
1am 0 1 2 3
2am 0 0 3 3
3am 0 0 4 4
4am 0 0 3 3
5am 0 1 1 2
6am 1 2 16 19
7am 1 6 39 46
8am 0 14 59 73
9am 0 4 29 33
10am 0 4 30 34
11am 0 3 14 17
Noon 1 4 25 30
1pm 0 5 40 45
2pm 1 8 34 43
3pm 0 3 59 62
4pm 0 8 46 54
5pm 0 7 66 73
6pm 1 8 55 64
7pm 0 7 24 31
8pm 0 1 12 13
9pm 0 4 12 16
10pm 1 3 14 18
11pm 0 3 0 3
Total 7 99 592 698
Collisions by Hour of the Day (Weekends) - Wokingham Roads (4.1.1.5)
Time of Day Fatal Serious Slight Total
Midnight 0 1 4 5
1am 0 0 1 1
2am 0 0 0 0
3am 0 1 0 1
4am 0 1 0 1
5am 0 0 0 0
6am 0 0 2 2
7am 0 1 7 8
8am 0 0 2 2
9am 0 1 9 10
10am 0 1 12 13
11am 0 2 13 15
Noon 0 2 15 17
1pm 1 3 11 15
2pm 0 5 11 16
3pm 1 4 8 13
4pm 0 2 8 10
5pm 1 3 14 18
6pm 0 1 5 6
7pm 0 1 7 8
8pm 0 1 8 9
9pm 0 2 12 14
10pm 0 1 5 6
11pm 0 3 3 6
Total 3 36 157 196
Collisions Involving Factors 306 and/or 307 (Speed Related) - Wokingham Roads (4.4.1)
Year Fatal Serious Slight Total
2013 0 4 16 20
2014 0 1 18 19
2015 0 5 19 24
2016 0 1 16 17
2017 2 4 10 16
2018 0 1 9 10
2019 0 1 8 9
2020 1 0 14 15
2021 0 3 8 11
2022 0 2 12 14
Total 3 22 130 155
Collisions Involving Factors 501 and/or 502 (Impairment Related) - Wokingham Roads (4.4.2)
Year Fatal Serious Slight Total
2013 0 3 5 8
2014 0 3 9 12
2015 0 1 6 7
2016 0 0 5 5
2017 1 2 7 10
2018 0 3 9 12
2019 0 4 5 9
2020 1 5 8 14
2021 1 5 9 15
2022 0 2 12 14
Total 3 28 75 106
Collisions Involving Factors 101 and/or 102 and/or 103 (Road Surface Related) - Wokingham Roads (4.4.3)
Year Fatal Serious Slight Total
2013 0 5 21 26
2014 0 2 19 21
2015 0 5 18 23
2016 0 2 15 17
2017 0 0 11 11
2018 0 0 9 9
2019 0 1 7 8
2020 0 1 5 6
2021 0 0 7 7
2022 0 2 11 13
Total 0 18 123 141
Collisions Involving Factors 408 and/or 409 and/or 410 (Control Error Related) - Wokingham Roads (4.4.4)
Year Fatal Serious Slight Total
2013 0 13 46 59
2014 1 7 40 48
2015 0 7 33 40
2016 1 6 34 41
2017 2 2 22 26
2018 0 5 20 25
2019 0 5 14 19
2020 1 8 11 20
2021 1 2 21 24
2022 0 6 20 26
Total 6 61 261 328
Collisions Involving Factors 601 and/or 602 (Unsafe Behaviour Related) - Wokingham Roads (4.4.5)
Year Fatal Serious Slight Total
2013 0 5 23 28
2014 0 6 33 39
2015 0 7 29 36
2016 0 8 28 36
2017 1 6 22 29
2018 0 7 20 27
2019 0 5 17 22
2020 1 5 15 21
2021 1 6 23 30
2022 0 2 28 30
Total 3 57 238 298
Collisions Involving Factors 508 and/or 509 and/or 510 (Distraction Related) - Wokingham Roads (4.4.6)
Year Fatal Serious Slight Total
2013 0 0 11 11
2014 1 3 12 16
2015 0 3 12 15
2016 0 1 13 14
2017 0 1 8 9
2018 0 1 10 11
2019 0 1 6 7
2020 0 2 5 7
2021 0 2 9 11
2022 0 3 7 10
Total 1 17 93 111
Collisions Involving Factors 504 and/or 505 (Medically Unfit) - Wokingham Roads (4.4.7)
Year Fatal Serious Slight Total
2013 0 1 10 11
2014 1 1 5 7
2015 0 1 4 5
2016 1 3 7 11
2017 0 2 5 7
2018 0 1 5 6
2019 0 3 3 6
2020 0 0 3 3
2021 0 1 6 7
2022 0 0 4 4
Total 2 13 52 67
Collisions Involving Factors 308 (Close Following Related) - Wokingham Roads (4.4.8)
Year Fatal Serious Slight Total
2013 0 0 16 16
2014 0 1 17 18
2015 0 0 17 17
2016 0 1 11 12
2017 0 1 6 7
2018 0 1 6 7
2019 0 2 4 6
2020 0 1 3 4
2021 0 1 2 3
2022 0 0 3 3
Total 0 8 85 93

5.4 Contributory Factor Groupings

In order to facilitate insight into specific road safety issues, Area Profile documents can include sections which analyse collisions on a network and/or resident casualties/drivers on the basis of contributory factors assigned by attending police officers. While conducting this analysis, it has often been found useful to group together certain factors which reflect broadly similar aspects of road risk. This table identifies various groups of factors which RSA has used in the past for this purpose. Clients may wish to devise alternative approaches.

Injudicious Action
Traffic Contraventions Disobeyed automatic traffic signal Disobeyed double white lines Disobeyed 'Give way' or 'Stop' signs or markings Disobeyed pedestrian crossing facility Illegal turn or direction of travel
Driver Errors or Reactions
Manoeuvre Errors Poor turn or manoeuvre Failed to signal or misleading signal Passing too close to cyclist, horse rider or pedestrian
Driver Impairment or Distraction
Substance Impairments Impaired by alcohol Impaired by drugs (illicit or medicinal)
Behaviour or Inexperience
Nervous Behaviour Nervous, uncertain or panic Learner or inexperienced driver/rider Inexperience of driving on the left Unfamiliar with model of vehicle
Speed Choices
Exceeding speed limit Travelling too fast for conditions
Control Errors
Sudden braking Swerved Loss of control Observation Error Failed to look properly Failed to judge other person's path or speed
Distraction
Driver using mobile phone Distraction in vehicle Distraction outside vehicle Health Impairments Uncorrected, defective eyesight Illness or disability, mental or physical
Unsafe Behaviour
Aggressive driving Careless, reckless or in a hurry
Defective steering or suspension
Defective or missing mirrors Overloaded or poorly loaded vehicle or trailer Road Surface Poor or defective road surface Deposit on road (e.g. oil, mud, chippings) Slippery road (due to weather)
Affected Vision Stationary or parked vehicle(s) Vegetation Road layout (e.g. bend, winding road, hill crest) Buildings, road signs, street furniture Dazzling headlights
Dazzling sun Rain, sleet, snow or fog Spray from other vehicles Visor or windscreen dirty or scratched Vehicle blind spot
Close Following
Following too close
Junction Errors
Junction overshoot Junction restart (moving off at junction)
Fatigue Impairment
Fatigue
Pedal Cycle Behaviour
Vehicle travelling along pavement Cyclist entering road from pavement Not displaying lights at night or in poor visibility Cyclist wearing dark clothing at night Pedestrian Behaviour Crossing road masked by stationary or parked vehicle
Failed to look properly Failed to judge vehicle's path or speed Wrong use of pedestrian crossing facility Dangerous action in carriageway (e.g. playing) Careless, reckless or in a hurry Impaired by alcohol
Impaired by drugs (illicit or medicinal) Pedestrian wearing dark clothing at night Disability or illness, mental or physical
Other
Vehicle Defects Tyres illegal, defective or under-inflated Defective lights or indicators Defective brakes

  1. For further information, go to https://www.gov.uk/government/publications/road-accidents-and-safety-statistics-guidance↩︎

  2. https://acorn.caci.co.uk/how-acorn-works/.html↩︎