Cars cars everywhere

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Institute for Transport Studies FACULTY OF ENVIRONMENT Cars, cars everywhere! (Episode 1) Prof Jillian Anable ITS Seminar, 6 th 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)

Transcript of Cars cars everywhere

Page 1: Cars cars everywhere

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)

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Outline

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

2. Research topics overview

3. Examining variation

4. Celebrating variation (Clustering)

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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

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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

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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

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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’

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The good news …

• Comprehensive

• 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

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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

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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?

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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

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Simplification processes• Focus on private vehicles to understand

personal car ownership

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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

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

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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))

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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!)

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Key parameters (PCA level)

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Where are all the VW diesels?

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Who emits and where?

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Household Energy Use

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Relative consumption from gas, electricity and car use

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Bivariate relationships between parameters

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

DENSITY INCOME INCOME X DENSITY

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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

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Middle-layer super output areas (MSOAs)

Local authority districts

Regions

Lower-layer super output areas (LSOAs)

Spatial Units for analysis

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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.

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Ultimate aim – local authority benchmarking and

analysis tool for exploring car ownership and use

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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

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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

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Variables used in the analysisDomain 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

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Results - eye candy maps and graphs

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cluster1 cluster2 cluster3 cluster4 cluster5 cluster6

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Tier 1 clusters mapped

“Quick and dirty validation”Does it look sensible?

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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..

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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