1 Executive Summary

This Area Profile presents a systematic overview of resident and road risk in Slough. 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.

Slough's overall resident casualty rate is 17% higher than the national rate, 15% higher than the rate for the South East region and 50% higher than the rate for Berkshire as a whole. Despite this, resident casualty numbers have seen a steady downward trend over the last decade. Forty seven percent of Slough's resident casualties are injured outside of the county. The greatest and over-represented number of Slough's resident casualties are from mosaic type N57; established older households owning city homes in diverse neighbourhoods. Slough's resident casualties are most likely to come from the more deprived 30% of the population. Resident casualties have been broken down into the following cohorts:

  1. Resident child casualty numbers from Slough have seen a broad downward trend since 2016. No resident children have been killed since 2018. Over three quarters of Slough's child casualties were injured in Slough.
  2. Resident older casualty numbers from Slough are relatively low but there has been an overall downward trend over the last ten years. Almost two-thirds of Slough's resident older casualties were injured on Slough's roads.
  3. Resident pedestrian casualty numbers have seen a downward trend since 2014. In 2021, three resident pedestrian casualties were killed. This is the highest number killed in a single year since 2015. Eighty-two percent of slough's resident pedestrian casualties were injured on Slough's roads.
  4. Resident child pedestrian casualty numbers decreased after 2016, although numbers increased in 2021 following the pandemic and the closure of schools.
  5. Resident pedal cyclist casualty numbers have decreased since 2016. Over three-quarters of Slough's pedal cyclist casualties were injured on Slough's roads.

Collision involved resident drivers from Slough have decreased over the last ten years. The rate per 100,000 population is 7% higher than the national rate and 15% higher than the rate for the South East region. The rate for Slough is higher than all other Berkshire authorities. Most of the collision involved drivers are of working age (25-54) and are more likely to come from communities of mosaic type N57, established older households owning city homes in diverse neighbourhoods or type N58, thriving families with good incomes in diverse suburbs

An extra section has been added to this study to specifically look at young drivers (aged 17 to 24). There has been a clear downward trend in numbers of collision involved resident young drivers over the last ten years. However, the rate per 100,000 population is 21% above the national rate and 8% higher than the regional rate. The rate for Slough is higher than all other Berkshire authorities.  Forty-four percent of Slough's resident young drivers were involved in collisions in Slough.

The number of Slough's resident motorcycle riders involved in collisions has fluctuated over the last decade, with a clear downward trend between 2016 and 2020. Interestingly there was a big increase in number in 2021 following the pandemic. The largest number of riders can be found in the 17 to 24 age group. Slough's resident motorcyclist collision involvement rate was 5% higher than the national rate and in line with the rate for the South East region. Slough's motorcyclist involvement rate is lower than all the other Berkshire authorities.

As well as reviewing the risk to residents, this Area Profile has considered collision rates on the local road network. Collisions on Slough's road network have decreased steadily since 2015. Despite this, the collision rate per 100km road on Slough's road network over the last five years was nearly three times higher than the national rate, 126% higher than the rate for the South East region and 172% higher than the overall rate for Berkshire. Slough's collision rate is higher than that of all the other Berkshire authorities.

As with all roads in Slough, collision numbers on urban roads decreased steadily from 2015. The collision rate between 2017 and 2021 was significantly higher than all other Berkshire authorities. Analysis of the collision dynamics at the time of the collision show that almost a third of collisions on urban roads resulted in no vehicle-to-vehicle impact. Where multiple vehicles were involved, 21% involved rear vehicle impact, 9% side impact and 9% head on or another point of the vehicle. 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.

Collision numbers on rural roads in Slough have fallen considerably since 2016 since 2012, There was very little change between 2020 and 2021.  Slough's collision rate between 2017 and 2021 was considerably higher than the national, regional and Berkshire rates. In part this is because Slough is predominately urban and has a much shorter amount of road network that is classified as rural. Analysis of the collision dynamics at the time of the collision show that over half of collisions involved a rear impact, compared to 26% on all roads. Fewer rural road collisions have either no impact, head on impacts or side impacts compared to all roads. The driver actions at the time of the collision show that there were considerably more collisions involving slow vehicle manoeuvres on rural roads compared to all roads. Runoffs were also more prevalent on rural roads.

The factors that contribute towards crashes 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 factor 'exceeding speed limit' or 'Traveling too fast for conditions' has dropped significantly on Slough's roads. Together these factors still play a part in 11% of all collisions, a percentage that is in line with the national percentage and the South East region.

Factors that relate to the road environment have also been measured. Road surface factors including slippery, icy and defective roads are summarised and show a declining trend. In 2021 there were only two road surface collisions in Slough, although one of these was fatal. The recording of 'loss of control', 'Unsafe behaviour' , 'close following', 'distraction', 'impairment' and 'medically unfit' factors have all follow a declining trend.

In summary the road safety risk rates for Slough's residents are higher than the national and regional norm but have decreased over the last ten years. Resident drivers have a higher risk rate than many 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 Slough, as well as casualties and drivers involved in collisions anywhere in Britain who reside in Slough.

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 Slough’s road network and Slough’s residents, primarily using STATS19 collision data1 and Mosaic 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 Slough’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 Slough’s key road safety issues with those of comparator regions and national figures. The aim is to allow Slough 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 Slough 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 Slough’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 Slough 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 Slough’s roads. Breakdowns by rurality classification of road are also included in this section.

Section 5 includes Appendices detailing all Mosaic Types and the profile and distribution of specific Mosaic Types relevant to Slough. 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 STATS 19 collision data. The residents section covers casualties and motor vehicle users involved in collisions who are residents of Slough, 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 2017 and 2021 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 Slough’s roads, regardless of where those involved reside. Network analysis is also based on the national STATS19 dataset over the five-year period between 2017 and 2021 inclusive. For a more complete explanation, please refer to 5.1.1 on methodology for calculating network collision risk.

3 Slough 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 Slough Resident Casualties

This section examines all casualties who were residents of Slough at the time of injury. For information about Slough’s resident motor vehicle users involved in collisions on all roads, please refer to section 3.2.

3.1.1 All Resident Casualties

3.1.1.1 Rates

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

Between 2017 and 2021, Slough had a resident casualty rate of 263 casualties per year, per 100,000 population.

Figure 3.1: Annual average Slough resident casualties per 100,000 population (2017 - 2021)

Annual average Slough resident casualties per 100,000 population (2017 - 2021)

3.1.1.2 Comparisons

Slough’s resident casualty rate was 17% higher than the national rate, and 15% higher than the regional rate for the South East. This was 50% higher than the overall rate for Berkshire as a whole. Within Berkshire, Slough had the highest resident casualty rate. When compared against the most similar comparator authorities, Slough’s resident casualty rate was in line with that of Sutton. This was lower than the rates of Luton and Hounslow, but higher than the rates of Hillingdon, Thurrock and Derby.

3.1.1.2.1 Residency by Small Area

Figure 3.2 shows the home location of Slough’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 amongst those living in and around Britwell, Colnbrook, Chalvey, Wexham Lea, and Langley.

Figure 3.2: Slough resident casualties home location by LSOA, casualties per year per 100,000 population (2017-2021)

Slough resident casualties home location by LSOA, casualties per year per 100,000 population (2017-2021)

3.1.1.4 Socio Demographic Analysis

3.1.1.4.1 Age

Figure 3.4 shows the numbers of resident casualties by age group.

Most resident casualties were aged 25 to 34 or 35 to 44. Very few casualties were aged over 64 or under 5.

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

Residents aged 17 to 24, 25 to 34, or 35 to 44 were over-represented as casualties when population is taken into account. Furthermore there is a greater level of over-representation amongs Slough’s residents in these age groups than there is nationally. As is the case nationally, residents aged over 44 or under 17 are either under-represented as casualties or are injured at a rate that is in line with their resident populations.

Figure 3.4: Slough resident casualties, by age group (2017-2021)

Slough resident casualties, by age group (2017-2021)

Figure 3.5: Slough resident casualties, by age group and indexed by population (2017-2021)

Slough resident casualties, by age group and indexed by population (2017-2021)
3.1.1.4.2 Segmentation

Analysis of the Mosaic communities in which Slough’s resident casualties live provides an insight into those injured in collisions. For an explanation of Mosaic 7 and how to understand the following chart, please refer to section 5.1.1.1.

The largest number of resident casualties come from Slough’s communities of established older households owning city homes in diverse neighbourhoods (Type N57). Furthermore, when the relative population of these communities in Slough is taken into account, this Type is over-represented as casualties.

The next largest resident casualty numbers come from communities of thriving families with good incomes in diverse suburbs (Type N58) and of stable families with children, renting higher value homes from social landlords (Type I36). These Types are also over-represented as casualties based on their population within Slough.

Although they a represent lower number of casualties, residents from communities of large families living in traditional terraces in neighbourhoods with a strong community identity (Type N59), of professional singles and couples in their 20s and 30s progressing in their field of work from commutable properties (Type O61) and of well-qualified older singles with incomes from successful professional careers in good quality housing (Type G27) are all over-represented as casualties.

Figure 3.6: Slough resident casualties, by Mosaic Type (2017-2021)

Slough resident casualties, by Mosaic Type (2017-2021)
3.1.1.4.3 Deprivation

Figure 3.7 shows resident casualties by the IMD of the LSOA (Lower Super Output Area) in which they reside.

The largest number of Slough’s resident casualties come from communities in the more deprived 30% decile. When the population of communities in this decile is taken into account, these residents are slightly over-represented as casualties. The next largest numbers of casualties come from Slough’s communities in the more deprived 40% and 50% deciles, although these casualty levels are in line with the relative population of these communities in Slough. Although they represent a much lower number of casualties overall, those from Slough’s communities in the more deprived 20% decile are considerably over-represented as casualties when population is taken into account.

Figure 3.7: Slough resident casualties, by Index of Multiple Deprivation (2017-2021)

Slough resident casualties, by Index of Multiple Deprivation (2017-2021)

3.1.2 Resident Child Casualties

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

3.1.2.1 Rates

Figure 3.8 shows Slough resident child casualty rate compared to the national and regional rates, and to the most similar comparators.

Slough’s resident child casualty rate was 101 child casualties per year, per 100,000 child population between 2017 and 2021.

Figure 3.8: Annual average Slough resident child casualties per 100,000 population (2017-2021)

Annual average Slough resident child casualties per 100,000 population (2017-2021)

3.1.2.2 Comparisons

Slough’s resident child casualty rate was 9% lower than the national rate, and 4% lower than the South East regional rate. Despite this, the rate was 37% higher than Berkshire as a whole. Within Berkshire, Slough had the highest child casualty rate, followed by Reading. Of the most similar comparator authorities, Slough’s resident child casualty rate was in line with Hounslow’s - higher than the rates for Thurrock and Hillingdon, but lower than the rates for Sutton, Derby and Luton.

3.1.2.2.1 Residency by Small Area

Figure 3.9 shows the home location of Slough’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 child casualty rates can be found amongst resident living in central Slough and Baylis. There are also high resident child casualty rates around parts of Wexham Lea, Colnbrook, Cippenham and Britwell.

Figure 3.9: Slough resident child casualties home location by LSOA, casualties per year per 100,000 population (2017-2021)

Slough resident child casualties home location by LSOA, casualties per year per 100,000 population (2017-2021)

3.1.2.4 Socio Demographic Analysis

3.1.2.4.1 Segmentation

Analysis of the Mosaic communities in which Slough’s resident child casualties live provides an insight into those injured in collisions.

The largest number of resident child casualties come from Slough’s communities of established older households owning city homes in diverse neighbourhoods (Type N57). This is followed by those from communities of thriving families with good incomes in diverse suburbs (Type N58) and of stable families with children, renting higher value homes from social landlords (Type I36). When taking into account the population share of these communites within Slough, all three of these Types are over-represented as child casualties.

Figure 3.11: Slough resident child casualties, by Mosaic Type (2017-2021)

Slough resident child casualties, by Mosaic Type (2017-2021)
3.1.2.4.2 Deprivation

Figure 3.12 shows resident child casualties by the IMD of the LSOA (Lower Super Output Area) in which they reside.

The greatest number of resident child casualties come from Slough’s communities in the more deprived 30% decile. Furthermore, these communities are slightly over-represented as child casualties based on their population share withing Slough.

Figure 3.12: Slough resident child casualties, by Index of Multiple Deprivation (2017-2021)

Slough resident child casualties, by Index of Multiple Deprivation (2017-2021)

3.1.3 Resident Older Casualties

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

3.1.3.1 Rates

Figure 3.13 shows the resident older casualty rates for Slough compared to the national and regional rates, as well as the most similar comparators.

Between 2017 and 2021, Slough had an older casualty rate of 142 older casualties per year, per 100,000 older population.

Figure 3.13: Annual average Slough resident older casualties per 100,000 population (2017-2021)

Annual average Slough resident older casualties per 100,000 population (2017-2021)

3.1.3.2 Comparisons

Slough’s resident older casualty rate was 15% higher than the national rate, 6% higher than the South East regional rate, and 44% higher than the overall rate for Berkshire. Within Berkshire, Slough had the highest older casualty rate. Of the most similar comparator authorities, Hounslow had the highest resident older casualty rate, followed by Slough.

3.1.3.2.1 Residency by Small Area

Figure 3.14 shows the home location of Slough’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 rates of resident older casualties can be found amongst those living around Stoke Road and in Stoke. There are also high rates around parts of Britwell, Haymill, Cippenham and Langley.

Figure 3.14: Slough resident older casualties home location by LSOA, casualties per year per 100,000 population (2017-2021)

Slough resident older casualties home location by LSOA, casualties per year per 100,000 population (2017-2021)

3.1.4 All Slough Resident Pedestrian Casualties

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

3.1.4.1 Rates

Figure 3.16 shows the resident pedestrian casualty rates for Slough compared to the national and regional rates, as well as the most similar comparators.

Slough had a resident pedestrian casualty rate of 33 casualties per year, per 100,000 population between 2017 and 2021.

Figure 3.16: Annual average Slough resident pedestrian casualties per 100,000 population (2017-2021)

Annual average Slough resident pedestrian casualties per 100,000 population (2017-2021)

3.1.4.2 Comparisons

Slough’s resident pedestrian casualty rate was 8% higher than the national rate, 37% higher than the South East regional rate, and 64% higher than the rate for Berkshire as a whole. Within Berkshire, Slough had the highest resident pedestrian casualty rate, followed by Reading. Of the most similar comparator authorities, Slough’s resident pedestrian casualty rate was higher than that of Thurrock, but lower than the rates found in Derby, Sutton, Hillingdon, Luton and Hounslow.

3.1.4.2.1 Residency by Small Area

Figure 3.17 shows the home location of Slough’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.

The highest resident pedestrian casualty rates can be found around central Slough and Chalvey. There are also high pedestrian casualty rates amongst residents in Haymill, Cippenham, and Wexham.

Figure 3.17: Slough resident pedestrian casualties home location by LSOA, casualties per year per 100,000 population (2017-2021)

Slough resident pedestrian casualties home location by LSOA, casualties per year per 100,000 population (2017-2021)

3.1.4.4 Socio Demographic Analysis

3.1.4.4.1 Age

Figure 3.19 shows the numbers of resident pedestrian casualties by age group.

Between 2017 and 2021, nearly a quarter (24%) of Slough’s resident pedestrian casualties were aged five to 16. There were also high numbers of resident pedestrian casualties aged 35 to 44 and 25 to 34. Fewer pedestrian casualties were aged under 5 or over 54.

It is more informative to consider Figure 3.20 which shows resident pedestrian casualty numbers by age group indexed by the population of those age groups in Slough. There is also a national index value for comparison.

Residents aged 25 to 34 are over-represented as pedestrian casualties when the population share of this age group is taken into account. Furthermore, this is a greater level of over-representation than is observed nationally. Those residents aged five to 16 and 17 to 24 are also over-represented as pedestrian casualties, although to a lesser extent than nationally.

Slough’s residents aged 75 to 84 are also over-represented as pedestrian casualties based on their population, despite this age group being under-represented nationally. The same is true of the 35 to 44 age group, although to a lesser extent.

Figure 3.19: Slough resident pedestrian casualties, by age group (2017-2021)

Slough resident pedestrian casualties, by age group (2017-2021)

Figure 3.20: Slough resident pedestrian casualties, by age group and indexed by population (2017-2021)

Slough resident pedestrian casualties, by age group and indexed by population (2017-2021)
3.1.4.4.2 Segmentation

Analysis of the Mosaic communities in which Slough’s resident pedestrian casualties live provides an insight into those injured in collisions. For an explanation of Mosaic 7 and how to understand the following chart, please refer to section 5.1.1.1.

The greatest number of resident pedestrian caualties come from Slough’s communities of established older households owning city homes in diverse neighbourhoods (Type N57). However, an index value of 105 suggests that the rate at which these communities are injured as pedestrians is only slightly higher than the expected rate based on their population share within Slough.

The next highest level of pedestrian casualties is for resident communities of thriving families with good incomes in diverse suburbs (Type N58). This Type is considerably over-represented as pedestrian casualties when the population of these communities in Slough is taken into account.

Figure 3.21: Slough resident pedestrian casualties, by Mosaic Type (2017-2021)

Slough resident pedestrian casualties, by Mosaic Type (2017-2021)
3.1.4.4.3 Deprivation

Figure 3.22 shows resident pedestrian casualties by the IMD of the LSOA (Lower Super Output Area) in which they reside.

The largest number of resident pedestrian casualties come from Slough’s communities in the more deprived 30% decile, followed by those in the more deprived 40% decile. Furthermore, these communities are slightly over-represented as pedestrian casualties, based on their population.

Figure 3.22: Slough resident pedestrian casualties, by Index of Multiple Deprivation (2017-2021)

Slough resident pedestrian casualties, by Index of Multiple Deprivation (2017-2021)

3.1.5 Slough Resident Child Pedestrian Casualties

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

3.1.5.1 Rates

Figure 3.23 shows the resident child pedestrian casualty rates for Slough compared to the national and regional rates, as well as the most similar comparators.

Between 2017 and 2021, Slough had a resident child pedestrian casualty rate of 34 casualties per year, per 100,000 child population.

Figure 3.23: Annual average Slough resident child pedestrian casualties per 100,000 population (2017-2021)

Annual average Slough resident child pedestrian casualties per 100,000 population (2017-2021)

3.1.5.2 Comparisons

Slough’s resident child pedestrian casualty rate was 15% lower than the national rate, but 10% higher than the South East regional rate. This was 39% higher than the rate for Berkshire as a whole. Within Berkshire, Slough had the highest resident child casualty rate, followed closely by Reading. Of the most similar comparator authorities, Slough’s rate was higher than that of Thurrock, but lower than the rates of Sutton, Hillingdon, Luton, Hounslow and Derby.

3.1.5.2.1 Residency by Small Area

Figure 3.24 shows the home location of Slough’s resident child pedestrian casualties by lower layer super output area (LSOA). The thematic map is coloured by resident child pedestrian casualties per year per child population of LSOA.

As with all child casualties, there are high resident child casualty rates around central Slough and Wexham Lea. There are also high rates around Langley.

Figure 3.24: Slough resident child pedestrian casualties home location by LSOA, casualties per year per 100,000 population (2017-2021)

Slough resident child pedestrian casualties home location by LSOA, casualties per year per 100,000 population (2017-2021)

3.1.6 All Slough Resident Pedal Cyclist Casualties

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

3.1.6.1 Rates

Figure 3.26 shows the resident pedal cyclist casualty rates for Slough compared to the national and regional rates, as well as the most similar comparators.

Slough’s resident pedal cyclist casualty rate was 26 casualties per year, per 100,000 population.

Figure 3.26: Annual average Slough resident pedal cyclist casualties per 100,000 population (2017-2021)

Annual average Slough resident pedal cyclist casualties per 100,000 population (2017-2021)

3.1.6.2 Comparisons

Slough’s resident pedal cyclist casualty rate was in line with both the national rate and the South East regional rate. This was 9% higher than the overall rate for Berkshire as a whole. Within Berkshire, Reading had the highest resident pedal cyclist casualty rate, followed by Slough. Of the most similar comparator authorities, Slough’s pedal cyclist casualty rate was higher than those of Thurrock and Luton, but lower than the rates of Sutton, Derby and Hounslow.

3.1.6.2.1 Residency by Small Area

Figure 3.27 shows the home location of Slough’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 parts of Chalvey and central Slough. There are also high rates amongst resident living around Cippenham Green.

Figure 3.27: Slough resident pedal cyclist casualties home location by LSOA, casualties per year per 100,000 population (2017-2021)

Slough resident pedal cyclist casualties home location by LSOA, casualties per year per 100,000 population (2017-2021)

3.1.6.4 Socio Demographic Analysis

3.1.6.4.1 Age

Figure 3.29 shows the numbers of resident pedal cyclist casualties by age group.

The highest levels of pedal cyclist casualties can be found amongst Slough’s residents aged five to 16, followed by those aged 25 to 34 and 17 to 24. Very few injured resident pedal cyclist were aged over 64, and none were aged under five.

It is more informative to consider Figure 3.30 which shows resident pedal cyclist casualty numbers by age group indexed by the population of those age groups in Slough. There is also a national index value for comparison.

When accounting for the demographics of Slough’s resident population, those aged 17 to 24 are considerably over-represented as pedal cyclist casualties, and to a much greater extent than they are nationally. Residents aged 25 to 34 are also over-represented, but to a lesser extent than they are nationally.

Despite being under-represented as pedal cyclist casualties nationally, Slough’s residents aged five to 16 are over-represented. The same is also true of residents aged 85 and over, although Figure 3.29 shows that these represent a small number of casualties.

Figure 3.29: Slough resident pedal cyclist casualties, by age group (2017-2021)

Slough resident pedal cyclist casualties, by age group (2017-2021)

Figure 3.30: Slough resident pedal cyclist casualties, by age group and indexed by population (2017-2021)

Slough resident pedal cyclist casualties, by age group and indexed by population (2017-2021)
3.1.6.4.2 Segmentation

Analysis of the Mosaic communities in which Slough’s resident pedal cyclist casualties live provides an insight into those injured in collisions. For an explanation of Mosaic 7 and how to understand the following chart, please refer to section 5.1.1.1.

The largest number of pedal cyclist casualties come from Slough’s communities of established older households owning city homes in diverse neighbourhoods (Type N57), followed closely by those from communities of thriving families with good incomes in diverse suburbs (Type N58). When taking into account the relative population of these communities within Slough, the rate at which communities of Type N57 are injured as pedal cyclists is lower than the expected rate. However, resident communities of Type N58 are considerably over-represented as pedal cyclist casualties.

Figure 3.31: Slough resident pedal cyclist casualties, by Mosaic Type (2017-2021)

Slough resident pedal cyclist casualties, by Mosaic Type (2017-2021)
3.1.6.4.3 Deprivation

Figure 3.32 shows resident pedal cyclist casualties by the IMD of the LSOA (Lower Super Output Area) in which they reside.

The greatest number of pedal cyclist casualties come from Slough’s communities in the more deprived 30% decile. When accounting for the population share of these communities in Slough, these residents are over-represented as pedal cyclist casualties. The next highest level of pedal cyclist casualties come from communities in the more deprived 40% decile, however an index value of 104 suggests this is broadly in line with the expected rate.

Figure 3.32: Slough resident pedal cyclist casualties, by Index of Multiple Deprivation (2017-2021)

Slough resident pedal cyclist casualties, by Index of Multiple Deprivation (2017-2021)

3.2 Slough Resident Drivers involved in Collisions

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

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

This section analyses all persons recorded as being a Slough 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 3.33 shows the resident driver involvement rates for Slough compared to the national and regional rates, as well as the most similar comparators.

Between 2017 and 2021, Slough had a collision involvement rate amongst resident drivers of 283 drivers per year, per 100,000 population.

Figure 3.33: Annual average Slough resident involved drivers per 100,000 population (2017-2021)

Annual average Slough resident involved drivers per 100,000 population (2017-2021)

3.2.1.2 Comparisons

Slough’s resident driver collision involvement rate was 7% higher than the national rate and 15% higher than the South East regional rate. This was 57% higher than the collision involvement rate for Berkshire as a whole, with Slough having the highest rate of all six Berkshire local authorities. Of the most similar comparator authorities, Slough’s rate was in line with that of Thurrock, lower than the rates of Hounslow and Luton, but higher than those of Hillingdon, Sutton and Derby.

3.2.1.2.1 Residency by Small Area

Figure 3.34 shows the home location of Slough’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 collision invovlement rates can be found amongst residents living in and around Chalvey, Wexham and Colnbrook. There are also high rates around Manor Park, Baylis and Langley.

Figure 3.34: Slough resident involved drivers home location by LSOA, involved drivers per year per 100,000 population (2017-2021)

Slough resident involved drivers home location by LSOA, involved drivers per year per 100,000 population (2017-2021)

3.2.1.4 Socio Demographic Analysis

3.2.1.4.1 Age

Figure 3.36 shows the numbers of resident involved drivers by age group.

The highest levels of collision involvement are amongst Slough’s residents aged 35 to 44, followed by those aged 25 to 34. Very few of Slough’s residents aged 65 and over were involved in collisions.

It is more informative to consider Figure 3.37 which shows resident involved driver numbers by age group indexed by the population of those age groups in Slough. There is also a national index value for comparison.

Based on their population shares within Slough, residents aged 25 to 24 are over-represented in collision involvement, and to a greater extent than they are nationally. The same is true of those aged 35 to 44 and those aged 45 to 54. Residents aged 17 to 24 are also over-represented in collision invovlement, although this is in line with the level of over-representation seen nationally.

Figure 3.36: Slough resident involved drivers, by age group (2017-2021)

Slough resident involved drivers, by age group (2017-2021)

Figure 3.37: Slough resident involved drivers, by age group and indexed by population (2017-2021)

Slough resident involved drivers, by age group and indexed by population (2017-2021)
3.2.1.4.2 Segmentation

Analysis of the Mosaic communities in which Slough’s resident drivers live provides an insight into those injured in collisions. For an explanation of Mosaic 7 and how to understand the following chart, please refer to section 5.1.1.1.

The highest collision involvement levels are amongst drivers from Slough’s communities of established older households owning city homes in diverse neighbourhoods (Type N57). Based on the population share of these communities in Slough, they are slightly over-represented in collision involvement.

The next highest numbers of collision-involved drivers come from communities of thriving families with good incomes in diverse suburbs (Type N58), of families with school-age children, who have bought the best house they can afford within popular neighbourhoods (Tpye H30) and of stable families with children, renting higher value homes from social landlords (Type I36). When population is taken into account, drivers from communities of Type N58 are over-represented in collision involvement. However, index values of 101 suggest that drivers from communities of Type H30 and of Type I36 are involved in collisions at the expected rate given their relative population in Slough.

Although they represent lower numbers of collision-involved drivers, residents from communities of large families living in traditional terraces in neighbourhoods with a strong community identity (Type N59) and of professional singles and couples in their 20s and 30s progressing in their field of work from commutable properties (Type O61) are over-represented in collison-involvement.

Figure 3.38: Slough resident involved drivers, by Mosaic Type (2017-2021)

Slough resident involved drivers, by Mosaic Type (2017-2021)
3.2.1.4.3 Deprivation

Figure 3.39 shows resident involved drivers by the IMD of the LSOA (Lower Super Output Area) in which they reside.

The highest levels of collision involvement can be found amongst drivers from communities in the more deprived 30% decile. This is followed by resident drivers from communities in the more deprived 40% and 50% deciles. However, respective index values of 106, 102 and 100 all suggest that these levels of collision invovlement are in line with the relative population of these communities in Slough.

Figure 3.39: Slough resident involved drivers, by Index of Multiple Deprivation (2017-2021)

Slough resident involved drivers, by Index of Multiple Deprivation (2017-2021)

3.2.3 Resident Young Driver Involvement (aged 17 to 24)

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

3.2.3.1 Rates

Figure 3.41 shows the resident young driver involvement rates for Slough compared to the national and regional rates, as well as the most similar comparators.

Between 2017 and 2021, Slough’s resident young drivers had a collision involvement rate of 429 drivers per year, per 100,000 young population.

Figure 3.41: Annual average Slough resident young involved drivers per 100,000 population (2017-2021)

Annual average Slough resident young involved drivers per 100,000 population (2017-2021)

3.2.3.2 Comparisons

Slough’s resident young driver collision involvement rate was 21% above the national rate and 8% higher than the South East regional rate. Slough had the highest resident young driver collision involvement rate of all six of Berkshire’s local authorities, 47% above the overall rate for Berkshire as a whole. Of the most similar comparator authorities, Slough’s rate was lower than those of Sutton, Thurrock and Luton, but higher than those of Hounslow, Derby and Hillingdon.

3.2.3.2.1 Residency by Small Area

Figure 3.42 shows the home location of Slough’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.

Slough has a particular concentration of resident collision-involved young drivers in Langley.

Figure 3.42: Slough resident young involved drivers home location by LSOA, young involved drivers per year per 100,000 population (2017-2021)

Slough resident young involved drivers home location by LSOA, young involved drivers per year per 100,000 population (2017-2021)

3.2.3.4 Socio Demographic Analysis

3.2.3.4.1 Segmentation

Analysis of the Mosaic communities in which Slough’s resident young drivers live provides an insight into those injured in collisions. For an explanation of Mosaic 7 and how to understand the following chart, please refer to section 5.1.1.1.

The highest levels of collision involvement are amongst young drivers living in Slough’s communities of established older households owning city homes in diverse neighbourhoods (Type N57). Furthermore, these communities are over-represented as young collision-involved drivers based on their population share in Slough.

The next highest numbers of collision involved young drivers come from communities of families with school-age children, who have bought the best house they can afford within popular neighbourhoods (Type H30) in Slough. This is followed by communities of thriving families with good incomes in diverse suburbs (Type N58) and of stable families with children, renting higher value homes from social landlords (Type I36). When taking into account the relative population of these communities in Slough, all three of these Types are over-represented in young driver collision-involvement.

Figure 3.44: Slough resident young involved drivers, by Mosaic Type (2017-2021)

Slough resident young involved drivers, by Mosaic Type (2017-2021)
3.2.3.4.2 Deprivation

Figure 3.45 shows resident involved young drivers by the IMD of the LSOA (Lower Super Output Area) in which they reside.

The largest number of collision-involved young drivers come from Slough’s communities in the more deprived 30% decile. However, an index value of 101 indicates that this level of collision involvement is in line with the expected rate, based on the population of these communities in Slough. The next highest level of collision-involvement is amongst Slough’s resident young drivers from communities in the more deprived 40% decile. These communities are also slightly over-represented in young driver collision involvement.

Figure 3.45: Slough resident young involved drivers, by Index of Multiple Deprivation (2017-2021)

Slough resident young involved drivers, by Index of Multiple Deprivation (2017-2021)

3.3 Slough 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 Slough.

3.3.1.1 Rates

Figure 3.47 shows the resident motorcyclist involvement rates for Slough compared to the national and regional rates, as well as the most similar comparators.

Between 2017 and 2021, the collision invovlement rate for Slough’s resident motorcyclists was 28 motorcyclists per year, per 100,000 population.

Figure 3.47: Annual average Slough resident involved motorcyclist per 100,000 population (2017-2021)

Annual average Slough resident involved motorcyclist per 100,000 population (2017-2021)

3.3.1.2 Comparisons

Slough’s resident motorcyclist collision involvement rate was in line with the South East regional rate, 5% higher than the national rate. This was 33% higher than the rate for Berkshire as a whole. Slough’s resident motorcyclists had the highest collision invovlement rate of all six authorities in Berkshire, followed by Reading. Of the most similar comparator authorities, Slough’s rate was higher than those of Derby and Luton, but lower than the rates of Hillingdon, Thurrock, Sutton and Hounslow.

3.3.1.2.1 Residency by Small Area

Figure 3.48 shows the home location of Slough’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.

There are higher rates of collision involvement amongst motorcyclists living in and around parts of Wexham, Britwell, Cippenham and Colnbrook.

Figure 3.48: Slough resident involved motorcyclist home location by LSOA, involved motorcyclists per year per 100,000 population (2017-2021)

Slough resident involved motorcyclist home location by LSOA, involved motorcyclists per year per 100,000 population (2017-2021)

3.3.1.4 Socio Demographic Analysis

3.3.1.4.1 Age

Figure 3.50 shows the numbers of resident involved motorcyclists by age group.

The highest level of collision involvement is amongst Slough’s resident motorcyclists aged 17 to 24. This is followed by resident motorcyclists aged 25 to 34 and those aged 35 to 44.

It is more informative to consider Figure 3.51 which shows resident involved motorcyclist numbers by age group indexed by the population of those age groups in Slough. There is also a national index value for comparison.

Slough’s resident motorcyclists aged 17 to 24 were considerably over-represented in collision involvement based on their population. Although this is also true nationally, it is observed to a greater extent within Slough. Resident motorcyclists aged 25 to 34 are also over-represented in collision involvement, although the extent of this over-representation is less than that seen nationally.

Figure 3.50: Slough resident involved motorcyclists, by age group (2017-2021)

Slough resident involved motorcyclists, by age group (2017-2021)

Figure 3.51: Slough resident involved motorcyclists, by age group and indexed by population (2017-2021)

Slough resident involved motorcyclists, by age group and indexed by population (2017-2021)
3.3.1.4.2 Segmentation

Analysis of the Mosaic communities in which Slough’s resident motorcyclists live provides an insight into those injured in collisions. For an explanation of Mosaic 7 and how to understand the following chart, please refer to section 5.1.1.1.

The greatest number of resident motorcyclists involved in collisions come from communities in Slough of established older households owning city homes in diverse neighbourhoods (Type N57). This is followed by communities of stable families with children, renting higher value homes from social landlords (Type I36). Index values indicate that, when taking into account the population demographics of Slough, both of these Types are over-represented in collision invovlement as motorcyclists.

Figure 3.52: Slough resident involved motorcyclists, by Mosaic Type (2017-2021)

Slough resident involved motorcyclists, by Mosaic Type (2017-2021)
3.3.1.4.3 Deprivation

Figure 3.53 shows resident involved motorcyclists by the IMD of the LSOA (Lower Super Output Area) in which they reside.

Motorcyclists from Slough’s communities in the more deprived 30% decile have the highest level of collision involvement, followed by those from communities in the more deprived 40% decile. Both of these communities are slightly over-represented as collision-involved motorcyclists based on their population within Slough.

Figure 3.53: Slough resident involved motorcyclists, by Index of Multiple Deprivation (2017-2021)

Slough resident involved motorcyclists, by Index of Multiple Deprivation (2017-2021)

4 Slough 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 Slough

This section refers to all collisions which occurred on Slough’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 2017 and 2021 per 100km of road in Slough compared to the national and regional rates, and those of the most similar comparators.

Slough’s collision rate between 2017 and 2021 was 82 collisons per year, per 100km of road.

Figure 4.1: Annual average collisions per 100km of road (2017-2021)

Annual average collisions per 100km of road (2017-2021)

4.1.1.2 Comparisons

Slough’s collision rate was nearly three times higher than the national collision rate (28 per year, per 100km of road). This was 126% higher than the South East regional rate, and 172% higher than the overall collision rate for Berkshire. Within Berkshire, Slough had the highest collision rate, followed by Reading.

4.1.1.2.1 Collisions by Small Area

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

The highest collision rates can be found along the A4 through the centre of Slough, and along the M4 through Colnbrook.

Figure 4.2: Annual average collisions per 100km of road (2017-2021)

Annual average collisions per 100km of road (2017-2021)

4.1.1.4 Collisions by day of the week

Figure 4.4 shows collision in Slough by day of the week and severity. Most collisions occur on weekdays, with the highest collision numbers on Tuesdays and Fridays. Fewer collsions occur on weekends, particularly on Sundays.

Figure 4.4: Slough collisions, by day of the week and severity (2017-2021)

Slough collisions, by day of the week and severity (2017-2021)

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. On weekdays there are clear peaks in collision numbers around the morning (8am to 9am) and evening (3pm to 7pm) peak times. Far fewer collision happen between midnight and 6am.

Figure 4.5: Slough collisions, by hour of the day during weekdays (2017-2021)

Slough collisions, by hour of the day during weekdays (2017-2021)
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, collisions on weekends are distributed more broadly thoughout the day. Most collisions happen between 11am and 7pm, with very few taking place after 11pm or before 8am.

Figure 4.6: Slough collisions, by hour of the day during weekends (2017-2021)

Slough collisions, by hour of the day during weekends (2017-2021)

4.1.1.6 Collisions by light conditions

Figure 4.7 shows collisions in Slough by the light conditions at the time of the collision. Nearly three quarters (72%) of collisions take place during daylight. Of the remaining 28% of collisions, 25% were in the presence of street lighting.

Figure 4.7: Slough collisions by light conditions (2017-2021)

Slough collisions by light conditions (2017-2021)

4.1.1.7 Collisions by weather conditions

Figure 4.8 shows collisions in Slough by the weather conditions present at the time of the collision. Only 13% of collisions take place in the presence of adverse weather conditions, of which most (11%) are during rain without high winds.

Figure 4.8: Slough collisions by weather conditions (2017-2021)

Slough collisions by weather conditions (2017-2021)
4.1.1.7.1 Collision involved drivers who reside in other areas

Nearly two-thirds (63%) of drivers who were involved in a collision in Slough were residents of Slough. Of the remaining 37%, most were from Buckinghamshire (6%), Windsor and Maidenhead (6%), Surrey (3%), Hillingdon (3%), Ealing (2%), or Bracknell Forest (2%).

4.1.1.8 Collision dynamics and driver actions

4.1.1.8.1 Collision dynamics

Figure 4.9 shows collisions in Slough 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. Over a quarter (27%) of collisions in Slough had no impacts between vehicles. The most prevalent collision dynamics were rear impacts (26%). Head-on impacts accounted for 8% of collisions, as did side impacts.

Figure 4.9: Slough collisions by collision dynamics (2017-2021)

Slough collisions by collision dynamics (2017-2021)
4.1.1.8.2 Driver actions

Figure 4.10 shows collisions in Slough 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.

The most prevalent driver actions were right turns (22% of collisions) and slow vehicle maneouvres (22%) such as stopping, being stationary or moving off. By comparison, just over 9% of collisions involved a left turn. Runoffs were involved in 11% of collisions in Slough, with 6% of collisions involving nearside runoffs.

Figure 4.10: Slough collisions by driver actions (2017-2021)

Slough collisions by driver actions (2017-2021)

4.1.1.9 Road environment

4.1.1.9.1 Road class

Figure 4.11 shows collisions in Slough by class of road. Over half (51%) of Slough’s collisions were on unclassified roads. Over a third (36%) were on A roads, with 10% on Slough’s motorways.

Figure 4.11: Slough collisions by road class (2017-2021)

Slough collisions by road class (2017-2021)
4.1.1.9.2 Carriageway type

Figure 4.12 shows collisions in Slough by carriageway type of road. Over two-thirds (68%) of collisions in Slough were on single carriageway roads, whilst 23% were on dual carriageways.

Figure 4.12: Slough collisions by road carriageway type (2017-2021)

Slough collisions by road carriageway type (2017-2021)
4.1.1.9.3 Junction type

Figure 4.13 shows collisions in Slough by the presence and type of junction. Over a third (35%) of collisions occured away from a junction. Of the remaining 65%, most were at normal junctions (46%), with 11% happening at roundabouts.

Figure 4.13: Slough collisions by junction type (2017-2021)

Slough collisions by junction type (2017-2021)
4.1.1.9.4 Junction control

Figure 4.14 shows collisions in Slough by the type of junction control (if the collision took place at a junction). Of those collisions that occured at junctions, two thirds (67%) were uncontrolled.

Figure 4.14: Slough collisions by junction control (2017-2021)

Slough collisions by junction control (2017-2021)

4.2 Collisions on Urban Roads in Slough

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

4.2.1 Rates

4.2.1.1 Collisions on urban road per 100km of urban road

Figure 4.31 below shows the rate of average annual collisions on urban roads between 2017 and 2021 per 100km of urban road in Slough compared to the national and regional rates, and those of the most similar comparators.

Between 2017 and 2021, Slough had an urban road collision rate of 78 collisions per year, per 100km of urban road.

Figure 4.31: Annual average collisions on urban roads per 100km of urban road (2017-2021)

Annual average collisions on urban roads per 100km of urban road (2017-2021)

4.2.1.2 Comparisons

Slough’s urban road collision rate was the highest in Berkshire, followed by Reading. This was over double the overall urban road collision rate for Berkshire as a whole. Slough’s rate was 54% higher than the national rate, and 62% higher than the South East regional rate.

4.2.1.4 Collision dynamics and driver actions on urban roads

4.2.1.4.1 Collision dynamics

Figure 4.33 shows collisions on urban roads in Slough 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. Compared to all roads, more collisions on urban roads resulted in no impact between vehicles (29% on urban roads, 27% on all roads). There were fewer were rear impacts (21% on urban roads, 26% on all roads), with slightly more head on impacts (9% on urban roads, 8% on all roads) and side impacts (9% on urban roads, 8% on all roads).

Figure 4.33: Slough collisions on urban roads by collision dynamics (2017-2021)

Slough collisions on urban roads by collision dynamics (2017-2021)
4.2.1.4.2 Driver actions

Figure 4.34 shows collisions on urban roads in Slough 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 more prevalent in collisions on urban roads (24% on urban roads, 22% on all roads), with slow vehicle maneouvres being less prevalent (19% on urban roads, 22% on all roads). Just over 9% of urban collisions involved a runoff, compared to 11% on all roads.

Figure 4.34: Slough collisions on urban roads by driver actions (2017-2021)

Slough collisions on urban roads by driver actions (2017-2021)

4.3 Collisions on Rural Roads in Slough

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

4.3.1 Rates

4.3.1.1 Collisions on rural road per 100km of rural road

Figure 4.35 below shows the rate of average annual collisions on rural roads between 2017 and 2021 per 100km of rural road in Slough compared to the national and regional rates, and those of the most similar comparators.

Between 2017 and 2021, Slough has a rural road collision rate of 129 collisions per year, per 100km of rural road.

Figure 4.35: Annual average collisions on rural roads per 100km of rural road (2017-2021)

Annual average collisions on rural roads per 100km of rural road (2017-2021)

4.3.1.2 Comparisons

Slough’s collision rate on rural roads was considerably higher than the national, regional, and overall Berkshire rates. In part, this is due to the fact that Slough is predominantly urban, and so has a much shorter amount of road network that is classified as rural.

4.3.1.4 Collision dynamics and driver actions on rural roads

4.3.1.4.1 Collision dynamics

Figure 4.37 shows collisions on rural roads in Slough 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 profile of collison dynamics on rural roads is notably different to that on all roads, as seen in Figure 4.9). On rural roads, over half (57%) of collisions involve a rear impact, compared to 26% on all roads. Fewer rural road collisions have either no impact between vehicles (14% on rural roads, 27% on all roads), head on impacts (3% on urban roads, 8% on all roads), or side impacts (3% on rural roads, 8% on all roads).

Figure 4.37: Slough collisions on rural roads by collision dynamics (2017-2021)

Slough collisions on rural roads by collision dynamics (2017-2021)
4.3.1.4.2 Driver actions

Figure 4.38 shows collisions on rural roads in Slough 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.

There were considerably more collisions involving slow vehilce maneouvres on rural roads (40%) compared to all roads (22%). There were also fewer collisions involving right turns (9% on rural roads, 22% on all roads), left turns (2% on rural roads, 9% on all roads). Runoffs were more prevalent on rural roads (20%) than on all roads (11%).

Figure 4.38: Slough collisions on rural roads by driver actions (2017-2021)

Slough collisions on rural roads by driver actions (2017-2021)

4.4 Contributory Factors

Each section below examines trends in reported collisions on Slough’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 4.41 shows collisions on Slough’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.

Between 2017 and 2021, 11% of Slough’s officer attended collisions were attributed a speed choice CF. This is in line with the national percentage, and the percentage for the South East region. This is only slightly higher than the percentage for Berkshire as a whole (10%). Within Berkshire, Slough’s proportion of speed choice collisions was in line with Windsor and Maidenhead. This was lower than in West Berkshire, but higher than in Bracknell Forest, Wokingham and Reading.

Of the most similar comparator authorities, Slough’s percentage of speed choice collisions was in line with Thurrock. This was higher than in Hillingdon, Hounslow and Sutton, but lower than in Derby and Luton.

Figure 4.41: Percentage of collisions in Slough and comparators where CF306 and/or CF307 were recorded (2017-2021)

Percentage of collisions in Slough and comparators where CF306 and/or CF307 were recorded (2017-2021)

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 4.44 shows collisions on Slough’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.

In Slough, 4.6% of officer attended collisions were attributed an impairment CF. This is considerably lower than the national (6.5%) and regional (7.9%) percentages, as well as the proportion reported in Berkshire as a whole (7.4%). Within Berkshire, Slough had the lowest proportion of impairment collisions. Of the most similar comparator authorities, Slough’s proportion of impairment collisions was in line with Sutton. This was lower than all the other comparator authorities.

Figure 4.44: Percentage of collisions in Slough and comparators where CF501 and/or CF502 were recorded (2017-2021)

Percentage of collisions in Slough and comparators where CF501 and/or CF502 were recorded (2017-2021)

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 4.47 shows collisions on Slough’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.

On Slough’s roads, 3.6% of collisions attended by a police officer were attributed a road surface CF. This is lower than the national percentage (8.1%), South East regional percentage (8.2%), and percentage for Berkshire as a whole (7.0%). Slough’s proportion of road surface collisions was the lowest of the six authorities withn Berkshire, and was lower than all of the most similar comparator authorities.

Figure 4.47: Percentage of collisions in Slough and comparators where CF101 and/or CF102 and/or CF103 were recorded (2017-2021)

Percentage of collisions in Slough and comparators where CF101 and/or CF102 and/or CF103 were recorded (2017-2021)

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 4.50 shows collisions on Slough’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.

Just under 12% of Slough’s officer attended collisions were attributed a control error CF. This is lower than the percentages reported nationally (17%), in the South East region (17%), and across Berkshire (16%). Within Berkshire, Reding had the lowest proportion of control error collisions, followed by Slough. Of the most similar comparator authorities, Slough had the lowest percentage of control error collisions.

Figure 4.50: Percentage of collisions in Slough and comparators where CF408 and/or CF409 and/or CF410 were recorded (2017-2021)

Percentage of collisions in Slough and comparators where CF408 and/or CF409 and/or CF410 were recorded (2017-2021)

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 4.53 shows collisions on Slough’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.

Between 2017 and 2021, 22% of Slough’s police officer attended collisions were attributed an unsafe behaviour CF. This was higher than the percentages reported nationally (18%) and in the South East region (18%). Within Berkshire, Bracknell Forest had the highest percentage of unsafe behaviour collisions, followed by Slough. Slough’s percentage of unsafe behaviour collisions was higher than the percentage reported across Berkshire as a whole (20%). Of the most similar comparator authorites, Luton had the highest percentage of unsafe behaviour collisions, followed by Slough.

Figure 4.53: Percentage of collisions in Slough and comparators where CF601 and/or CF602 were recorded (2017-2021)

Percentage of collisions in Slough and comparators where CF601 and/or CF602 were recorded (2017-2021)

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 4.56 shows collisions on Slough’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.

Almost 5.9% of officer attended collisions in Slough were attributed a distraction CF. This is higher than the percentages recorded nationally (4.7%), across the South East region (5.4%), and in Berkshire as a whole (5.1%). Within Berkshire, Wokingham had the highest proportion of distraction collisions, followed by Slough. Of the most similar comparator authorities, Slough had the highest proportion of distraction collisions, followed by Luton.

Figure 4.56: Percentage of collisions in Slough and comparators where CF508 and/or CF509 and/or CF510 were recorded (2017-2021)

Percentage of collisions in Slough and comparators where CF508 and/or CF509 and/or CF510 were recorded (2017-2021)

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 4.59 shows collisions on Slough’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.

In Slough, 1.7% of collisions attended by a police officer were attributed a medically unfit CF. This is lower than both the national percentage (2.5%) and the South East regional percentage (3.2%). Within Berkshire, Slough’s percentage of medically unfit collisions was in line with Reading, lower than the other four local authorities. Of the most similar comparators, Slough’s percentage was broadly in line with Hillingdon, Sutton and Luton. This was higher than Hounslow and Derby, but lower than Thurrock.

Figure 4.59: Percentage of collisions in Slough and comparators where CF504 and/or CF505 were recorded (2017-2021)

Percentage of collisions in Slough and comparators where CF504 and/or CF505 were recorded (2017-2021)

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 4.62 shows collisions on Slough’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.

Within Slough, 7% of officer attended collisions were attributed a close following CF. This was higher than the national percentage (4.3%), the South East regional percentage (4.5%), and the overall percentage across Berkshire (4.9%). Slough’s percentage was the highest of the six Berkshire local authorities, and was higher than all of the most similar comparator authorities.

Figure 4.62: Percentage of collisions in Slough and comparators where CF308 was recorded (2017-2021)

Percentage of collisions in Slough and comparators where CF308 was recorded (2017-2021)

5 Appendices

5.1 Analytical Techniques

5.1.1 Resident road users

Casualty and driver postcodes in STATS 19 make it possible to identify where casualties from Slough reside. Thematic maps are used to illustrate the number of casualties per head of population from each small area in Slough. 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 Slough’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 Slough 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 Mosaic 7 segmentation, deprivation and/or rurality. More information on Mosaic is provided later in this section.

5.1.1.1 Mosaic 7

Insight into the lifestyles of Slough resident road casualties and motor vehicle users can be provided through socio demographic analysis. RSA Mosaic profiling uses Experian’s Mosaic 7 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 Mosaic Types, so residency analysis is based on about five out of six Slough residents involved in reported injury collisions.

Mosaic 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. The system was devised under the direction of Professor Richard Webber, a leading authority on consumer segmentation, using data from a wide range of public and private sources. It is used to inform policy decisions, communications activity and resource strategies across the 7.

Mosaic presently classifies the community represented by each UK postcode into one of 15 Groups and 66 Types. Each Group embraces between 3 and 6 Types. A complete list of Mosaic Types is provided in 5.2.1 whilst profiles and distribution for the Mosaic Types identified in this Area Profile as providing insight on Slough’s residents are detailed in 5.2.2.

This profile displays Mosaic 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 Slough resident casualties or drivers in each Mosaic Type or Group, corresponding to the value axis to the left of the chart. The columns in the foreground provide an index for each Mosaic 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 Slough 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 Mosaic profiles for drivers may be provided with indices based on Experian’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 Slough and can be interpreted in the same way as indices on Mosaic 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 Slough 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 Slough’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 Slough, 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 Slough, 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 Slough into context, comparisons with other areas have been made.

5.1.3.1 Regional

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.

5.1.3.2 Socio Demographic

It is not always appropriate to compare an authority solely against its neighbours, especially when the authority has unique characteristics in terms of socio-demographic composition and/or road network. In this Area Profile Slough’s most similar authorities have been selected using Mosaic classification. Because of the size of Slough only district authorities have been selected for comparison. The chosen six districts are:

  • Local Authority
  • Hillingdon London Borough
  • Hounslow London Borough
  • Sutton London Borough
  • Luton Borough
  • Derby City
  • Thurrock

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 STATS 19 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 Mosaic 7

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

5.2.1 Complete list of Mosaic Types

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

5.2.2 Profile and distribution for selected Mosaic Types

The table below shows Mosaic 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.

Figure 5.1 shows Slough’s LSOAs colour coded by dominant Mosaic Type.

Figure 5.1: Dominant Mosaic Types in Slough

Dominant Mosaic Types in Slough

5.3 Data Tables

All Casualties - Slough Residents (3.1.1)
Year Fatal Serious Slight Total
2012 3 50 534 587
2013 3 55 545 603
2014 2 52 496 550
2015 6 51 531 588
2016 6 57 514 577
2017 3 47 460 510
2018 6 47 374 427
2019 3 42 354 399
2020 0 31 262 293
2021 6 38 295 339
Total 38 470 4365 4873
Child Casualties - Slough Residents (3.1.2)
Year Fatal Serious Slight Total
2012 0 8 57 65
2013 0 6 60 66
2014 0 5 53 58
2015 0 6 54 60
2016 0 9 65 74
2017 0 3 53 56
2018 1 6 37 44
2019 0 2 43 45
2020 0 4 23 27
2021 0 5 31 36
Total 1 54 476 531
Pedestrian Casualties - Slough Residents (3.1.4)
Year Fatal Serious Slight Total
2012 1 10 59 70
2013 1 10 53 64
2014 0 12 63 75
2015 4 11 54 69
2016 1 8 39 48
2017 1 11 55 67
2018 2 12 44 58
2019 1 8 38 47
2020 0 8 26 34
2021 3 12 27 42
Total 14 102 458 574
Pedal Cycle User Casualties - Slough Residents (3.1.6)
Year Fatal Serious Slight Total
2012 0 6 55 61
2013 0 9 50 59
2014 0 9 49 58
2015 0 13 52 65
2016 0 11 59 70
2017 0 5 53 58
2018 1 3 34 38
2019 0 3 37 40
2020 0 4 29 33
2021 0 2 25 27
Total 1 65 443 509
Motor Vehicle Drivers Involved in Injury Collisions - Slough Residents (3.2.1)
Year Fatal Serious Slight Total
2012 3 46 561 610
2013 2 50 601 653
2014 3 51 563 617
2015 8 54 583 645
2016 8 48 542 598
2017 6 54 481 541
2018 8 65 396 469
2019 7 65 355 427
2020 4 32 281 317
2021 4 41 318 363
Total 53 506 4681 5240
Motorcyclists Involved in Injury Collisions - Slough Residents (3.3.1)
Year Fatal Serious Slight Total
2012 1 19 38 58
2013 2 14 33 49
2014 1 11 39 51
2015 2 9 42 53
2016 2 20 40 62
2017 0 15 41 56
2018 0 15 29 44
2019 0 12 29 41
2020 0 7 19 26
2021 1 12 29 42
Total 9 134 339 482
Young Adult Drivers Involved in Injury Collisions - Slough Residents (3.2.3)
Year Fatal Serious Slight Total
2012 1 9 94 104
2013 0 6 97 103
2014 0 9 76 85
2015 1 8 72 81
2016 1 3 70 74
2017 1 11 53 65
2018 2 5 52 59
2019 1 9 50 60
2020 0 4 36 40
2021 1 9 37 47
Total 8 73 637 718
All Collisions - Slough Roads (4.1)
Year Fatal Serious Slight Total
2012 2 39 373 414
2013 3 46 349 398
2014 2 42 389 433
2015 3 45 417 465
2016 4 44 368 416
2017 0 38 315 353
2018 7 40 238 285
2019 2 33 240 275
2020 0 33 167 200
2021 6 30 183 219
Total 29 390 3039 3458
Urban Collisions - Slough Roads (4.2)
Year Fatal Serious Slight Total
2012 2 33 312 347
2013 2 36 283 321
2014 1 37 306 344
2015 3 37 339 379
2016 2 34 282 318
2017 0 36 276 312
2018 5 32 199 236
2019 1 27 202 230
2020 0 27 153 180
2021 5 27 164 196
Total 21 326 2516 2863
Rural Collisions - Slough Roads (4.3)
Year Fatal Serious Slight Total
2012 0 6 61 67
2013 1 10 66 77
2014 1 5 83 89
2015 0 8 78 86
2016 2 10 86 98
2017 0 2 39 41
2018 2 8 39 49
2019 1 6 38 45
2020 0 6 14 20
2021 1 3 19 23
Total 8 64 523 595
Collisions by Hour of the Day (Weekdays) - Slough Roads (4.1.1.5)
Time of Day Fatal Serious Slight Total
Midnight 0 2 5 7
1am 0 0 2 2
2am 1 0 3 4
3am 1 0 3 4
4am 0 0 5 5
5am 0 3 6 9
6am 2 2 29 33
7am 0 10 45 55
8am 0 12 94 106
9am 0 6 37 43
10am 0 6 26 32
11am 0 2 27 29
Noon 0 7 53 60
1pm 0 6 42 48
2pm 1 6 54 61
3pm 2 14 83 99
4pm 1 9 75 85
5pm 1 16 85 102
6pm 2 14 72 88
7pm 1 8 59 68
8pm 0 4 37 41
9pm 0 6 28 34
10pm 1 4 18 23
11pm 0 4 11 15
Total 13 141 899 1053
Collisions by Hour of the Day (Weekends) - Slough Roads (4.1.1.5)
Time of Day Fatal Serious Slight Total
Midnight 0 0 4 4
1am 0 1 3 4
2am 0 0 4 4
3am 0 0 2 2
4am 0 1 2 3
5am 0 0 3 3
6am 0 0 3 3
7am 0 0 5 5
8am 0 1 7 8
9am 1 1 6 8
10am 0 1 6 7
11am 0 0 19 19
Noon 0 2 23 25
1pm 0 2 18 20
2pm 0 6 15 21
3pm 1 2 12 15
4pm 0 4 23 27
5pm 0 3 20 23
6pm 0 3 21 24
7pm 0 0 16 16
8pm 0 1 7 8
9pm 0 3 12 15
10pm 0 2 9 11
11pm 0 0 4 4
Total 2 33 244 279
Collisions Involving Factors 306 and/or 307 (Speed Related) - Slough Roads (4.4.1)
Year Fatal Serious Slight Total
2012 0 4 16 20
2013 1 2 23 26
2014 0 4 22 26
2015 2 3 25 30
2016 0 9 36 45
2017 0 3 30 33
2018 1 4 8 13
2019 0 5 9 14
2020 0 4 11 15
2021 3 4 10 17
Total 7 42 190 239
Collisions Involving Factors 501 and/or 502 (Impairment Related) - Slough Roads (4.4.2)
Year Fatal Serious Slight Total
2012 0 4 16 20
2013 1 4 9 14
2014 0 5 15 20
2015 1 1 11 13
2016 0 3 16 19
2017 0 1 11 12
2018 0 2 8 10
2019 0 0 1 1
2020 0 3 5 8
2021 1 3 5 9
Total 3 26 97 126
Collisions Involving Factors 101 and/or 102 and/or 103 (Road Surface Related) - Slough Roads (4.4.3)
Year Fatal Serious Slight Total
2012 0 0 8 8
2013 0 2 15 17
2014 1 2 15 18
2015 0 3 12 15
2016 0 0 12 12
2017 0 1 14 15
2018 0 0 4 4
2019 0 0 7 7
2020 0 0 3 3
2021 1 0 1 2
Total 2 8 91 101
Collisions Involving Factors 408 and/or 409 and/or 410 (Control Error Related) - Slough Roads (4.4.4)
Year Fatal Serious Slight Total
2012 0 7 42 49
2013 0 8 56 64
2014 0 4 55 59
2015 0 4 45 49
2016 0 6 40 46
2017 0 5 39 44
2018 0 4 15 19
2019 0 2 11 13
2020 0 4 12 16
2021 1 3 7 11
Total 1 47 322 370
Collisions Involving Factors 601 and/or 602 (Unsafe Behaviour Related) - Slough Roads (4.4.5)
Year Fatal Serious Slight Total
2012 1 7 58 66
2013 1 11 42 54
2014 1 4 47 52
2015 0 6 50 56
2016 0 12 34 46
2017 0 5 44 49
2018 0 10 30 40
2019 0 12 29 41
2020 0 11 20 31
2021 1 13 19 33
Total 4 91 373 468
Collisions Involving Factors 508 and/or 509 and/or 510 (Distraction Related) - Slough Roads (4.4.6)
Year Fatal Serious Slight Total
2012 0 0 13 13
2013 0 2 22 24
2014 0 1 20 21
2015 0 1 15 16
2016 1 4 14 19
2017 0 3 16 19
2018 0 4 4 8
2019 0 0 6 6
2020 0 2 5 7
2021 1 2 8 11
Total 2 19 123 144
Collisions Involving Factors 504 and/or 505 (Medically Unfit) - Slough Roads (4.4.7)
Year Fatal Serious Slight Total
2012 1 0 5 6
2013 0 1 6 7
2014 0 1 4 5
2015 0 2 3 5
2016 0 1 2 3
2017 0 0 6 6
2018 0 1 3 4
2019 0 1 1 2
2020 0 0 0 0
2021 0 0 3 3
Total 1 7 33 41
Collisions Involving Factors 308 (Close Following Related) - Slough Roads (4.4.8)
Year Fatal Serious Slight Total
2012 0 4 32 36
2013 0 0 34 34
2014 0 0 29 29
2015 0 5 23 28
2016 0 2 27 29
2017 0 2 25 27
2018 0 0 8 8
2019 0 1 13 14
2020 0 1 3 4
2021 0 0 8 8
Total 0 15 202 217

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.

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

  2. See Appendix B below, or go to http://www.experian.co.uk/marketing-services/products/mosaic-uk.html↩︎