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Safe travel

Collision data glossary and methodologies

Glossary of terms used

(Refers mainly to Annual Statistics Report & Webpages)

Term Description
AADT AADT stands for annual average daily traffic. The source of the AADT’s used in this report is the Department for Transport.
Average distance from home Distance from home is a measure built into MAST which is based on a calculation of the distance in kilometres between a crash location and the home address of each person involved. MAST aggregates all the results of this calculation, and expresses them as averages for all individuals included, summarised by dimensions as required.
Casualty Casualty data refers to the people themselves who are injured in a collision. Often more than one person is injured so casualty statistics are usually higher than collision numbers.
Child In this report we have used the DfT definition of a child which is up to and including the age of 15.
Collision This refers to the incident itself.
Cycling to/from school Journeys in which school pupils up to and including 16 years of age are travelling to, or from, school by bicycle. It includes journeys to/from pre-school or after-school activities based at the school but exclude journeys made to/from school activities which are not based at the school itself. Journeys between school and childcare organisation/childminder are included, but journeys between childcare and the pupil’s home are not.
Cluster Sites (historically known as accident black spots) Collision clusters are identified using a five-year history of collision data.   As collision numbers are falling over time it is increasingly likely that the starting point minimum criteria will need to be varied accordingly to either search for fewer collisions or a wider geographical radius. The revised and confirmed minimum criteria is decided by the  Safer  Travel  Programme  Officer using professional judgement.  The next step to verify the sites  is  even more important  when  minimum  criteria  has  been  lowered; it  is more likely  that  randomly  located  collisions will be picked up and it will be harder to find true cluster sites with distinct collision patterns.
Dark Collisions ‘Darkness’ means half an hour after sunset to half an hour before sunrise. ‘Daylight’ means all other times.
Devon CC Devon County Council
Driving Licence Data The driving licence data refers to 2017 data relating to Devon residents living in the all EX postcodes except EX23, the TQ6 to TQ14 postcodes, PL19 to PL21 postcodes and PL8 area. The number of driving licence holders does not necessarily represent the number of active drivers.
DFT Department for Transport, a central government department.
Fatal collision Human casualties who sustained injuries which caused death less than 30 days after the collision. Confirmed suicides are excluded.
Five year average Five year averages are taken from the previous five years prior to 2017 (therefore 2012 to 2016).
HES Hospital Episode Statistics – HES is a data warehouse containing details of all admissions, outpatient appointments and A&E attendances at NHS hospitals in England. In this report we use the data relating to transport accident hospital admissions. (RD&E, Northern Devon Healthcare Trust and Devon Community Hospital admissions only).
KSI Acronym for Killed or Seriously Injured
Major Roads Traffic A major roads traffic count is in chapter 6 – major roads refer to an average taken from the M5 and the A roads in the Devon County Council area.
National Data National data refers to England, Scotland & Wales, except for where a casualty billion vehicle KM rate is given – in which case this is a rate for just England.
Per Population figures – Casualties per 1 million population This is a national key indicator area that has been suggested by the DfT. The calculation also helps to measure the performance between different geographical areas that vary in size and population. 2017 mid year estimates will be used in this report.
School Journey Vehicle passenger data is based on the related vehicle journey purpose being entered as ‘taking pupil to/from school’. School bus journeys should be included if the bus is travelling to/from a school. It is possible that if it is unknown whether it was a school bus, it may be coded ‘Journey as part of work’ and therefore will be absent from these figures. Journeys to/from pre-school or after-school activities based at the school should be included in these figures, but journeys made to/from school activities which are not based at the school itself should be excluded.
Cycling to/from school is a data field within the vehicle journey purpose section.
Child walking to/from school figures are based on a casualty data field and as of 2014 this field no longer exists in the Devon & Cornwall Police Stats19 form so future reporting of this data is uncertain.
School Run School run is coded as taking a pupil to/from school in Stats19. A driver/rider involved in an accident whilst travelling to school to collect a pupil, or returning home after having taken a pupil to school, should be coded if this is the only purpose for the journey.  The journey purpose in an accident involving a parent travelling to work and taking a child to school should be coded ‘Taking pupil to/from school’. If the child is in or leaving the vehicle when the accident occurs it should be coded ‘Commuting to/from work’ if the accident occurs after the child has alighted from the vehicle.
Serious collision Examples of ‘Serious’ injury are, fracture, internal injury, severe cuts, crushing, burns (excluding friction burns), concussion, severe general shock requiring hospital treatment, detention in hospital as an in-patient, either immediately or later and injuries to casualties who die 30 or more days after the accident from injuries sustained in that accident.
Settlements over 7000 This refers to towns in Devon with a population size of 7,000 or more residents. The towns include Axminster, Barnstaple (including Roundswell and Bickington), Bideford, Bovey Tracey, Braunton, Crediton, Cullompton, Dawlish, Exeter, Exmouth, Honiton, Ilfracombe, Ivybridge, Kingsteignton, Newton Abbot, Northam, Okehampton, Ottery St Mary, Seaton, Sidmouth, Tavistock, Teignmouth, Tiverton and Totnes)
Slight injury collision Examples of ‘Slight’ injury are sprains, not necessarily requiring medical treatment, neck whiplash injury, bruises, slight cuts and slight shock requiring roadside attention. (Persons who are merely shaken and who have no other injury should not be included unless they receive or appear to need medical treatment).
South West This includes Bath and North East Somerset, Bournemouth, City of Bristol,
Cornwall, Isles of Scilly, North Somerset, Plymouth, Poole, South Gloucestershire, Swindon, Torbay, Wiltshire, Devon, Dorset, Gloucestershire and Somerset.
Statistical Tests We have used two statistical tests in this report. Their uses in relation to collisions are described by ROSPA:
The Poisson test can be used to determine whether the recent increase is likely to persist or whether the increase was due to random fluctuation and therefore the number of collisions at the site will return to previous levels. In other words the Poisson test is used to calculate the probability of a particular number of collisions occurring at a location in a given year when the long-term average for that location is known.
The Chi Squared test can be used to determine whether the number of collisions of a particular type is ‘significantly’ higher than at similar sites.
Stats19 Data The recording system for collisions reported/recorded by the Police. It only includes collisions that occurred on a highway, involved one or more vehicles and human death or personal injury. It only includes collisions that were notified to the Police within 30 days of occurrence.
Strategic Framework The Strategic Framework sets out the package of policies that the Department for Transport believe will continue to reduce deaths and injuries on the road. They are split between national and local priorities.
TAG Transport Analysis Guidance. The data used from TAG refers specifically to data within document Unit 3.4.1 – Table 4a the economic value of prevention of collisions.
VRU’s Vulnerable Road Users –includes cyclists, motorcyclist and pedestrians.
Wet Collisions This refers to the road surface condition at the time of the accident. ‘Wet/damp’ is the code we use. To get a wet/dry ratio the number of wet collisions is divided between the number of dry collisions.  Flood (surface water over 3cm deep), snow and frost/ice codes are not included in the wet/damp ratio data.
Wet/Damp Cluster Sites Road surface related cluster analysis is conducted on behalf of the Asset Management Team who carry out their own further investigations into the road surface condition at any locations identified. Criteria:
5 collisions in 3 years that included factors listed below that occurred within 200m and 33% or more of the collisions occurred on wet/damp road surface.
Factor code and description:
101 –Poor or defective road surface
102 –Deposit on Road e.g. Oil, mud, chippings etc
103 –Slippery road due to weather
108 –Road layout i.e. bend hill narrow carriageway
110 –Slippery inspection cover or road marking.
307 –Travelling too fast for circumstances
308 –Following too close401–Junction overshoot
409 –Swerved
410 –Loss of control
707 –Rain, sleet, snow or fog
Although we have refined the initial selection of collisions, each site that matches the criteria will need to be verified to check if each site’s collisions have a genuine link with potential road surface issues. This manual verification process involves reading through the Police officers account of what happened for every collision and other information available.


Route Analysis Methodology

Six Scores make up the Route Rankings:

1 All Severities: Number of collisions over 5 years
2 All Severities: Average collisions per kilometre
3 All Severities: Collision rate per billion vehicle kilometres
4 Killed/Seriously Injured (KSI): Number of KSI collisions over 5 years
5 Killed/Seriously Injured (KSI): Average KSI collisions per kilometre
6 Killed/Seriously Injured (KSI): KSI Collision rate per billion vehicle kilometres

The total score of the six aspects then gives the ROUTE RANKING. Route ranking number 1 represents the worst performing route as it has the highest overall score of the six aspects.

Any major junctions where two different routes meet are not included in route analysis process; any poor performing junctions / nodes will be identified as part of our annual cluster analysis process. Traffic flow and route length is taken into consideration with this route analysis method, as well as collision data. The collision data used in the scoring is based on data covering the last 5 years (2013-2017). Traffic flow data for A roads comes from Department for Transport open source data that relates to the latest year available. B road traffic count data comes from our in house counters that have recorded counts over the years, so there is no single year baseline used and the type of counter recorder is variable. Road lengths are calculated in kilometres. If a collision is located on a junction where a B road meets an A road, it will be included within the A road results, and not the B road table to avoid double counting.

The rankings are split into 4 quarters or quartiles that indicate the best and worst routes, and the upper and lower performing routes.

Note: The B road results are only an approximate indication of collision performance as the results are greatly affected by limited traffic count data collected; the AADT traffic counts used in the calculations span a number of different years and half of the readings are at least 5 years old. AADT data for B roads are not directly comparable with A road AADTs; A road traffic data was sourced from the Department for Transport however the B road data comes from in house Devon County Council counters –both use different methodology to calculate AADT data.

Cluster Site Methodology

All roads, all severities, 5 in 30 metre radius, over 5 years

Collision  clusters  are  identified  using  a  five  year  history  of  collision  data.   As  collision  numbers  are  falling  over time  it is  increasingly likely that the  starting  point  minimum criteria  will  need  to  be  varied  accordingly  to either search for fewer collisions or a wider geographical radius. The revised and confirmed minimum criteria is decided by  the  Safer  Travel  Programme  Officer  using  professional  judgement.  The  next  step  to  verify  the  sites  is  even more important  when  minimum  criteria  has  been  lowered; it  is more likely  that  randomly  located  collisions will be picked up and it will be harder to find true cluster sites with distinct collision patterns.

The Safer Travel Programme Officer analyses each of the highlighted output reports and will eliminate sites for further investigation based on the following reasons:

  • If sites are previous, pending, current or other CSR schemes
  • If a site is already identified through another cluster parameters (e.g. comes up in the ‘all severities urban’ criteria, and is then identified again under A/B/C road parameters).
  • Wrongly/poorly located records reducing clusters below parameters
  • Eliminating output where the polygon captures multiple roads
  • Recent years zero collisionsûSpurious records e.g. medical episode contributing to collision numbers
  • Highways England roads

Verification Process. Each site that matches the criteria will need to be verified to check if each site’s collisions have a genuine link with potential  road  surface  issues. This  manual  verification  process  involves  reading  through  the  Police  officers account of what happened for every collision and other information available.

What Happens Next? All of the annual investigation collision processes do not automatically presume a capital funded solution will be sought and undertaken. Once a site is  fully  investigated,  if  appropriate,  a viable    solution    is    sought    and    a preliminary   cost  for  the  solution  is calculated.   If   no   solution   can   be identified these schemes should be re-investigated   by   a   different   trained team.

MOSAIC Demographic Profiles

Mosaic is a geodemographic classification of households, essentially it segments households into different types or groups to help understand likely behaviour. The Profiles are complied by Experian using data collated from a number of government and commercial sources. More info here.

Name Description
Rural Vogue Country-loving families pursuing a rural idyll in comfortable village homes while commuting some distance to work
Scattered Homesteads Older households appreciating rural calm in stand-alone houses within agricultural landscapes
Wealthy Landowners Prosperous owners of country houses including the rural upper class, successful farmers and second-home owners
Village Retirement Retirees enjoying pleasant village locations with amenities to service their social and practical needs
Empty-Nest Adventure Mature couples in comfortable detached houses who have the means to enjoy their empty-nest status
Bank of Mum and Dad Well-off families in upmarket suburban homes where grown-up children benefit from continued financial support
Alpha Families High-achieving families living fast-track lives, advancing careers, finances and their school-age kids’ development
Premium Fortunes Influential families with substantial income established in distinctive, expansive homes in wealthy enclaves
Diamond Days Retired residents in sizeable homes whose finances are secured by significant assets and generous pensions
World-Class Wealth Global high flyers and families of privilege living luxurious lifestyles in London’s most exclusive boroughs
Penthouse Chic City suits renting premium-priced flats in prestige central locations where they work hard and play hard
Metro High-Flyers Ambitious 20 and 30-somethings renting expensive apartments in highly commutable areas of major cities
Uptown Elite High status households owning elegant homes in accessible inner suburbs where they enjoy city life in comfort
Cafes and Catchments Affluent families with growing children living in upmarket housing in city environs
Modern Parents Busy couples in modern detached homes juggling the demands of school-age children and careers
Mid-Career Convention Professional families with children in traditional mid-range suburbs where neighbours are often older
Thriving Independence Well-qualified older singles with incomes from successful professional careers in good quality housing
Dependable Me Single mature owners settled in traditional suburban semis working in intermediate occupations
Fledgling Free Pre-retirement couples with respectable incomes enjoying greater space and spare cash since children left home
Boomerang Boarders Long-term couples with mid-range incomes whose adult children have returned to the shelter of the family home
Family Ties Active families with teens and adult children whose prolonged support is eating up household resources
Legacy Elders Time-honoured elders now mostly living alone in comfortable suburban homes on final salary pensions
Solo Retirees Senior singles whose reduced incomes are satisfactory in their affordable but pleasant owned homes
Bungalow Haven Peace-seeking seniors appreciating the calm of bungalow estates designed for the elderly
Classic Grandparents Lifelong couples in standard suburban homes enjoying retirement through grandchildren and gardening
Far-Flung Outposts Inter-dependent households living in the most remote communities with long travel times to larger towns
Outlying Seniors Pensioners living in inexpensive housing in out of the way locations
Local Focus Rural families in affordable village homes who are reliant on the local economy for jobs
Satellite Settlers Mature households living in expanding developments around larger villages with good transport links
Affordable Fringe Settled families with children owning modest, 3-bed semis in areas where there’s more house for less money
First-Rung Futures Pre-family newcomers who have bought value homes with space to grow in affordable but pleasant areas
Flying Solo Bright young singles on starter salaries choosing to rent homes in family suburbs
New Foundations Occupants of brand new homes who are often younger singles or couples with children
Contemporary Starts Fashion-conscious young singles and partners setting up home in developments attractive to their peers
Primary Ambitions Forward-thinking younger families who sought affordable homes in good suburbs which they may now be out-growing
Cultural Comfort Thriving families with good incomes in multi-cultural urban communities
Community Elders Established older households owning city homes in diverse neighbourhoods
Asian Heritage Large extended families in neighbourhoods with a strong South Asian tradition
Ageing Access Older residents owning small inner suburban properties with good access to amenities
Career Builders Motivated singles and couples in their 20s and 30s progressing in their field of work from commutable properties
Central Pulse Entertainment-seeking youngsters renting city centre flats in vibrant locations close to jobs and night life
Learners & Earners Inhabitants of the university fringe where students and older residents mix in cosmopolitan locations
Student Scene Students living in high density accommodation close to universities and educational centres
Flexible Workforce Self-starting young renters ready to move to follow worthwhile incomes from service sector jobs
Bus-Route Renters Singles renting affordable private flats away from central amenities and often on main roads
Self Supporters Hard-working mature singles who own budget terraces manageable within their modest wage
Offspring Overspill Lower income owners whose adult children are still striving to gain independence meaning space is limited
Down-to-Earth Owners Ageing couples who have owned their inexpensive home for many years while working in routine jobs
Disconnected Youth Young people endeavouring to gain employment footholds while renting cheap flats and terraces
Renting a Room Transient renters of low cost accommodation often within subdivided older properties
Make Do & Move On Yet to settle younger singles and couples making interim homes in low cost properties
Midlife Stopgap Maturing singles in employment who are renting short-term affordable homes
Budget Generations Families supporting both adult and younger children where expenditure can exceed income
Childcare Squeeze Younger families with children who own a budget home and are striving to cover all expenses
Families with Needs Families with many children living in areas of high deprivation and who need support
Solid Economy Stable families with children renting better quality homes from social landlords
Seasoned Survivors Deep-rooted single elderly owners of low value properties whose modest home equity provides some security
Aided Elderly Supported elders in specialised accommodation including retirement homes and complexes of small homes
Pocket Pensions Penny-wise elderly singles renting in developments of compact social homes
Dependent Greys Ageing social renters with high levels of need in centrally located developments of small units
Estate Veterans Longstanding elderly renters of social homes who have seen neighbours change to a mix of owners and renters
Low Income Workers Older social renters settled in low value homes in communities where employment is harder to find
Streetwise Singles Hard-pressed singles in low cost social flats searching for opportunities
High Rise Residents Renters of social flats in high rise blocks where levels of need are significant
Crowded Kaleidoscope Multi-cultural households with children renting social flats in over-crowded conditions
Inner City Stalwarts Long-term renters of inner city social flats who have witnessed many changes