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
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Cars cars everywhere
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)
3.
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
15. This slide will move on in 20 seconds!
15
This slide will move on in 20 seconds!
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!)
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
27.
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
32.
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
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