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  • Institute for Transport StudiesFACULTY 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)

  • Outline

    1. Core project data (and the challenges ..)

    2. Research topics overview

    3. Examining variation

    4. Celebrating variation (Clustering)

  • 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, DfTpublished the first 5 years online

  • 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

  • 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

  • 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

  • The good news


    Available to a fine spatial scale (units of approx700 households)

    Collected roughly annually (unlike Census)

    Total annual mileage of vehicles

    Private vs Business keepership

    Detailed vehicle characteristics

  • Time limits on the data

    MOT records began in 2005, robust data from 2007 only

    For vehicles < 3 yrs old, we dont 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

  • 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 dont have a CO2 value (infer from engine size and fuel type)

    4% of vehicles dont 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?

  • 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

  • Simplification processes Focus on private vehicles to understand

    personal car ownership

  • 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_kmP_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

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    Potential uses of the data

    Trends over time

    Differences between


    Emissions: air

    quality and

    climate change

    Core MOT

    and DVLA


    Vehicle types:



    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

  • 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))

  • 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!)

  • Key parameters (PCA level)

  • Where are all the VW diesels?

  • Who emits and where?

  • Household Energy Use

  • Relative consumption from gas, electricity and car use

  • Bivariate relationships between parameters

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


  • 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

  • Middle-layer super output areas (MSOAs)

    Local authority districts


    Lower-layer super output areas (LSOAs)

    Spatial Units for analysis

  • 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.

  • Ultimate aim local authority benchmarking and

    analysis tool for exploring car ownership and use


    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

  • Measures of accessibility

    Available from the UK Department for