Efforts to reduce the emissions from car travel have so far been hampered by a lack of specific information on car ownership and use. The Motoring and vehicle Ownership Trends in the UK (MOT) project seeks to address this by bringing together new sources of data to give a spatially and disaggregated diagnosis of car ownership and use in Great Britain and the associated energy demand and emissions. Data from annual car M.O.T tests, made available by the Department for Transport, will be used as a platform upon which to develop and undertake a set of inter-linked modelling and analysis tasks using multiple sources of vehicle-specific and area-based data. Through this the project will develop the capability to understand spatial and temporal differences in car ownership and use, the determinants of those differences, and how levels may change over time and in response to various policy measures. The relationship between fuel use and emissions, and the demographic, economic, infrastructural and socio-cultural factors influencing these will also be tested. Consequently, the MOT project has the potential to transform the way in which energy and emissions related to car use are quantified, understood and monitored to help refine future research and policy agendas and to inform transport and energy infrastructure planning. www.its.leeds.ac.uk/research/featured-projects/mot

1. Institute for Transport Studies
FACULTY OF ENVIRONMENT
Cars, cars everywhere! (Episode 1)
Prof Jillian Anable
ITS Seminar, 6th December, 2016
With strong acknowledgement to the rest of the ‘MOT’ team:
Sally Cairns and Paul Emmerson (TRL); Tim Chatterton (UWE), Eddie Wilson (Bristol)
& Ian Philips (ITS)

2. Outline
1. Core project data (and the challenges ..)
2. Research topics overview
3. Examining variation
4. Celebrating variation (Clustering)

4. Ministry of Transport (MOT) Test
• Annual safety
inspection for all road
vehicles older than 3yrs
• Since 2005, results have
been captured and
stored digitally
• Nov 2010, DfT
published the first 5
years online

5. MOT data (DVSA)
• 2005-2014
• 325 million tests
• Varying intervals
between tests
• One row per test
Vehicle stock data (DVLA)
• 2003-2012
• 56 million vehicles
• Annual or quarterly
recording points
• One row per vehicle
Vehicles master table – one
row per vehicle; columns
contain quarterly attributes
Local area tables – one
row per LSOA or Data
Zone; columns contain
average or total values
Aberdeen Data
Safe Haven

6. MOT dataset
(test data)
• Test date
• Test type and
result
• Odometer
reading
• Location of test
(Postcode Area)
MOT dataset
(vehicle data)
• First use date
• Make, model
and colour
• Engine size
• Fuel type
• Vehicle class
Stock tables
(vehicle data)
•Keeper location
(LSOA/ Data
Zone)
•Private or
commercial
•CO2 value

7. Types of vehicles
• Work reported here focuses on Class 4/4A vehicles in private
use in 2011 – closest match to Census data on ‘cars or vans
owned, or available for use, by members of this household’

8. The good news …
• Comprehensive
• Available to a fine spatial scale (units of approx
700 households)
• Collected roughly annually (unlike Census)
• Total annual mileage of vehicles
• Private vs Business keepership
• Detailed vehicle characteristics

9. Time limits on the data
• MOT records began in 2005, robust data from 2007 only
• For vehicles < 3 yrs old, we don’t know anything about them
until they show up for their first test as we are only given
vehicle information (from stock tables) once they appear in
the MOT dataset
• This means only three years usable at the moment
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
MOT test data
Stock data for vehicles aged 3 years+
Stock data for vehicles aged <3 years

10. Other issues
• No usage data for vehicles less than 3 years old – interpolate mileage evenly over
first 3 yrs?
• No information on when vehicles leave the fleet (e.g. that are scrapped or go
abroad)
• If vehicles arrive and disappear within the first three years we will never know
about them …
• No information on unlicensed vehicles
• No information on foreign vehicles – only some take a test but they are still driving
on UK roads!
• About 30% vehicles don’t have a CO2 value (infer from engine size and fuel type)
• 4% of vehicles don’t have a location (leave out as mostly ‘between keepers’)
• ‘Clocked’ (rolled over) mileages –have no records ‘with confidence’ (about 4%)
How aggressive should the cleaning algorithms be?
Missing mileages – how should we fill them in?

11. Key challenges
• Missing or inconsistent
vehicle data between
MOT tests
• Converting odometer
readings into usable
mileage information
Use of flags to
identify consensus
or lack of
consensus
Convert readings to regular census
points – challenges with missing or
erroneous odometer readings

12. Simplification processes
• Focus on private vehicles to understand
personal car ownership

13. Focus on generating a 2011
table of area statistics
41,729 LSOA/DZ rows, potentially hundreds of columns….
LSOA P.ALL.av_km P_ALL_tot_km P_ALL_med_km P_ALL_av_engine P_ALL_av_odo P_ALL_av_co2 P_ALL_av_fud
E01000001 8.886015125 3216.737475 6.4574928 2069.069061 48226.17127 191.4078947 731262.5691
E01000002 9.051169099 3267.472045 6.3166752 2019.069252 49428.92244 196.0218341 731309.6787
E01000003 10.16558441 1555.334415 6.627278592 1628.594771 54496.73856 179.8488372 731235.8758
E01000005 11.09056427 1242.143198 7.025591232 1736.955357 63109.66964 187.5405405 731687.9196
E01000006 15.69251804 6465.317431 10.05196262 1679.441748 74335.29612 171.4271186 731668.6481
E01000007 12.44802015 2663.876312 9.746187264 1725.635514 76412.56075 170.8783784 731634.5421
E01000008 14.91905651 3401.544884 10.9491719 1744.631579 84965.74123 172.16 731434.7719
E01000009 12.81574705 4767.457901 10.05679066 1702.569892 73322.66935 169.3089431 731604.7984
E01000010 14.11702585 4390.395039 10.1533513 1681.045016 83460.1672 171.7061856 731522.9196
E01000011 16.83051041 5924.339664 11.2452912 1701.747159 78386.04545 172.8506787 731586.2727
E01000012 12.94207982 3856.739788 10.39072954 1689.738255 77655.3557 171.3435897 731585.0034
E01000013 12.17399428 5003.511651 9.322929792 1662.043796 67630.51338 171.7727273 731730.8613
E01000014 13.18887501 6488.926507 9.705953664 1652.327236 71498.79675 169.2107023 731599.6118
E01000015 12.45622582 6838.467977 10.1646167 1674.032787 76304.89071 169.9630682 731484.3424
E01000016 11.91133929 6003.315002 9.204643008 1635.246032 67193.38492 171.1965318 731557.9881
E01000017 11.33179817 5779.217068 9.493520256 1659.582353 72917.85882 170.8575668 731594.7176
E01000018 13.24891908 5922.266829 9.30200832 1644.959732 72014.23043 167.557554 731457.7539
E01000019 12.98602553 5389.200597 9.59169024 1618.036145 72167.88193 168.0606061 731566.441
E01000020 12.44321096 6594.901809 9.67215744 1633.160377 70815.6717 170.0410557 731552.4943
E01000021 13.35662214 5943.696853 9.789639552 1616.620225 75115.82022 167.1684982 731563.6292
E01000022 11.95601003 4674.79992 9.807342336 1656.762148 68202.85422 171.9538462 731750.4706
E01000023 12.48741649 4008.460693 10.12116442 1669.037383 69516.38318 169.2669683 731775.0561
E01000024 13.06312488 5016.239953 9.376038144 1615.328125 71028.63281 172.1373391 731488.5052

15. This slide will move on in 20 seconds!
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Potential uses of the data
Trends
over time
Differences
between
places
Emissions: air
quality and
climate change
Core MOT
and DVLA
dataset
Vehicle types:
technology
diffusion
Additional data sets
Census, air quality, energy use,
Experian data, indices of
accessibility, deprivation etc.
Car ownership
and use: transport
policy evaluation
Fuel use: future
energy scenariosLinks to socio-
demographics

16. Analyses to date
• Descriptive (the pretty stuff!) …
• Variation in car use per person at a range of spatial
scales
• Understanding the contribution of car use to total
household energy footprints
• Considering the distribution of motoring costs,
emissions and exposure, and associated social
justice issues
• How motoring costs vary with income
• How pollution (exposure and ‘creation’) varies
with income
• Modelling the determinants of car ownership and
use (regression (+spatial regression))

17. Near-term priorities
• Developing techniques for benchmarking the
performance of different areas for policy
evaluation
• Comparing insights from MOT/DVLA data and
conventional home-based trip models
• Exploring how and why vehicle age profiles vary
• Temporal analysis of spatial changes over time
• HARVEST: HARnessing emergent VEhicle data
for Sustainable Transport (esrc proposal
submitted today!)

19. Key parameters (PCA level)

20. Where are all
the VW
diesels?

21. Who emits and where?

22. Household Energy Use

23. Relative consumption from
gas, electricity and car use

24. Bivariate
relationships
between
parameters

25. Relationship between average LSOA
income, density & car ownership
(TOP: LOESS curves; BOTTOM: Ave. cars per person/LSOA per banding)
DENSITY INCOME INCOME X DENSITY

26. Total energy use more dependent on average
mileages than types of vehicles owned
Link to average car use
 R2 = 0.77
Link to average emissions
Each dot represents an LSOA
 R2 = 0.016

28. Middle-layer super
output areas (MSOAs)
Local authority
districts
Regions
Lower-layer super
output areas (LSOAs)
Spatial Units for analysis

29. Variation at the vehicle-level
• Intra-areal mileage distributions are usually reasonably
similar to each other.
• Variation between areas is greater at smaller spatial
scales.
• Mileage distributions and mean mileages are
reasonably closely related, BUT
• To assess whether areas are different, it is useful to use
measures in addition to mean averages, such as the
share of household with no cars, or the number of high
mileage vehicles.

30. Ultimate aim – local
authority benchmarking and
analysis tool for exploring
car ownership and use
30
Local authority can
input areas and time
period of interest
Output of changes in
car ownership and
use for that area
Output of changes in
car ownership and
use for ‘similar’ areas
Needs cluster analysis to define
‘similar’ areas

31. Measures of accessibility
Available from the UK Department for
Transport:
• Connectivity index to major rail
stations and road junctions
• Measure of accessibility to 8 key
services by car and non-car modes
Generated through the project:
• Distances to places of different sizes
• Measure of connectivity to all rail
stations
• Measure of non-rail public transport
provision Index of connections to
major road junctions

33. Variables used in the analysis
Domain Variables Source
Car use Miles per person per year, Gini coefficient of
distribution of vehicle use across MSOA
population, high mileage vehicles per person,
MOT project
Car characteristics Vehicle age, engine size, % diesel, total
particulate emissions , total CO2 emissions
MOT project
Car ownership Cars per person Census
Mode share % car commute mode share, % bike commute
mode share
Census
Accessibility Total travel time to 8 locations by car and by
public transport / walk. total distance to 8
locations
DfT Accessibility
statistics
Morphology and
land-use
Distance to work, distance to nearest settlement
of: 5000, 25000, 50000, 100000, 250000 and
500000 residents.
Derived from
Ordnance Survey,
census and UK
Borders data
Social and
demographic
% one adult households,
% unemployed,
% people in non-car owning households,
% professional,
% Intermediate occupations,
% Routine / manual occupations,
% work from home,
% female,
% children, mean income,
% of population working over 31 hours per week
Census, Office for
National Statistics
(ONS)
Lots of regression
analysis
Lots of cluster
analysis

34. Results - eye candy maps and
graphs
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0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
cluster1 cluster2 cluster3 cluster4 cluster5 cluster6

35. Tier 1 clusters mapped
“Quick and dirty validation”
Does it look sensible?

36. What next from the MOT
project?
• Clustering / area typology/ benchmarking
• HARVEST…
• Work with DfT & CDRC to ensure a legacy
product, open to others
• The MOT Atlas coming to a screen near you …
• … over to you..

37. Acknowledgements
The work has been undertaken under EPSRC Grant EP/K000438/1.
Grateful thanks to members of DfT, VOSA, DVLA and DECC, SPT, DSC and who have
provided advice and support for this work
Website is: http://www.motproject.net