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Theme 2: Transport & Mobility

26 September, 2018

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Aims of the theme: Target activities and places of highest demand and fastest growth, develop novel metrics, classifications and deployment models which facilitate new frameworks for changing mobility demand…

Aims of the theme

  • Target activities and places of highest demand and fastest growth
  • Develop novel metrics, classifications and deployment models which facilitate new frameworks for changing mobility demand
  • Understand evolving relationships between technologies, socio demographic change and structural factors
  • Apply spatially disaggregated, place-based analytics to all projects
  • Facilitate integration between transport and energy studies and policy

Subtheme 2.1 Targeting high energy demand

2.1.1: High Energy Consumers

Chatterton, UWE

Lead: Tim Chatterton, UWE (with Karen Lucas, Caroline Mullen, Milena Buchs, Leeds and time from 2 PDRFs)

  • Pockets of high domestic electricity usage + EV uptake overlap = strain on the grid
  • To tackle ‘over consumption’ is potentially efficient and most equitable
  • These users could have the greatest social and economic capital to reduce consumption
  • What is the relationship between energy poverty and ‘excess’ demand?
  • Recent reductions in travel demand have been greatest in highest income households
  • What is going on in these households?


  • Develop and test a methodology and set of tools for identifying, characterising and assessing specific geographical locations that have disproportionately high levels of energy consumption (= heat + electricity + (car based) mobility)
  • Apply political theory and theories of consumption to define ‘excess’ and define responsibilities and capabilities for change


  1. Spatial analysis of secondary data: 24.5m electricity and 21m gas meters (BEIS sub-national statistics) and 36m vehicle odometers (+ linked to Census, NTS, Understanding Society, LCFS, NEED) @ LSOA – characterise areas, develop indices of ‘excess’, understand correlates with excess through spatial regression & machine learning
  2. Primary research: 8 areas of ‘excess demand’ identified: data collected from a nest of methods: questionnaires, METER gadgets and deliberative for a to explore patterns of consumption and potential fiscal & non fiscal interventions
  3. Work with project partners – Ofgem, Network Grid Operators

2.1.2: Long Distance Travel

Wadud, Leeds

Lead: Zia Wadud, Leeds:

  • Central urban areas = ‘Peak car’ but not on trunk roads and motorways
  • Leisure travel known to consume ~50% of distance travelled. Domestic leisure (‘staycations’) on the rise
  • Investment in high speed rail and airport infrastructure
  • Technological solutions more challenging
  • Many unknowns: composition of travel at certain times; social distribution; substitution; interaction of local and long distance journeys and mobility patterns


  • Develop a typology of ‘long distance passenger travel’ based on meaningful metrics of distance, duration, frequency, regularity, activity, occupancy, energy intensity & flexibility Examine the interplay between longer distance and daily travel
  • Estimate elasticities of different trip types based on different metrics • Link to technological advances including electrification, biofuels and other future scenarios: automation, Brexit, ageing population
  • Scope an innovative diary study targeting long distance/ less frequent trips


  • Link and harmonise aggregate and disaggregate data: new waves of Understanding Society & National Travel Survey; data from airlines, rail operators, Highways England, Local Authority ANPR cameras
  • Analysis: (i) trend analysis and regression for aggregate data (ii) clustering, regression, classification and visualization techniques for disaggregate data (iii) scenario development for understanding the future
  • 15 interviews with data experts among transport operators, market research organisations, business travel management companies.
  • Evidence session and debate hosted by the Commission on Travel Demand (2.3.2)

Subtheme 2.2 Flexing transport demand

2.2.1: Flexing Passenger Mobility

Mattioli, Leeds

Lead: Giulio Mattioli, ITS Leeds

  • FASTER = advancing the science of energy and flexibility of energy in use
  • Transport & energy system models poorly account for spatial and temporal differences and the constraints and opportunities for flexibility
  • Assumptions that PEVs will substitute ICEs and be charged at optimal times are based on average or aggregated mileages- crude understanding of patterns of use across the day, week and space
  • Mode switch potentials are based on the substitutability of single car journeys without considering activity sequencing across the day or family
  • Access to flexible transportation is becoming easier for private cars and fleet managers through car ‘usership’ rather than ownership


  • Produce a passenger car vehicle level classification which is spatially, socially and temporally disaggregated
  • Develop novel indices of flexibility: intensity, duration, regularity (timing, destinations, occupancy), sequencing, periodicity, turbulence, temporal rhythms over the week
  • Inform policies aimed at substituting ICES with PEVs and modal switch from car use
  • Evaluate implications of temporalities, occupancy etc on demand for ‘on demand’ services


  • Reconfigure National Travel Survey 7-day diary into vehicle-level episodic data
  • Estimate metrics based on variability/concentration indices and sequence analysis techniques
  • Apply clustering to classify vehicles using new metrics
  • Fuse with vehicle-level MOT data to provide spatial characterisations of car use ‘flexibility’
  • Link resulting data with the ‘MOT dataset’ (see 2.1.1) to estimate average patterns of car use

2.2.2: Modelling Flexibility over time, mode, place

Brand, Oxford

Lead: Christian Brand, ECI Oxford

  • UK fleet models account poorly for potential for flexibility (ICE substitution, distance reduction, intra-household dynamics) and the spatial and temporal differences
  • National scale energy models and local-level distribution network need to ‘meet in the middle’ to understand how changes in demand and generation in the distribution network will impact the transmission network and vice versa
  • General Distribution and Electricity Transmission Network models need better assessments of where and when high-end domestic and electric vehicle (PEV) use will combine.


  • Improve UK modelling capability in assessing future transport and electricity systemic change
  • Develop existing state-of-the-art UK fleet models with better assessments of the potential for technology substitution, modal shift and demand flexibility
  • Assess how electricity infrastructures (local substations, line-loads) may evolve with domestic energy and PEV hotspots and rhythms of charging


  • Detailed car and van fleet model at LSOA level findings from earlier projects (2.1.1, 2.1.2, 2.2.1, 2.2.2), scale up to regional level and integrated into a transport-energy-environment system model developed elsewhere (UK TCM, Brand et al, 2017) to explore wider energy and transport impacts of future consumption pathways.
  • Agent-based modelling approach to represent the wide variety of PEV charging control strategies and charging behaviours within case study LSOAs through the use of aggregate level LSOA data combined with real charging profiles and insights into the variability of end-user behaviour in the home. This will take into account different charging objectives (i.e. low cost, low carbon, low network impact) that can be grouped into LSOA-level agents that in turn can then be combined together under a transmission network grid supply point. Scenarios of future consumption patterns in different types of neighbourhoods will be assessed.
  • Two workshops will be held; the first (Reading) to bring the two modelling communities together to discuss approaches and methods, and the second (Oxford/London) to develop the policy-relevant scenarios of future consumption patterns.

Subtheme 2.3 Accelerating Deployment

2.3.1 The Governance of Radical Mobility Change

Marsden, Leeds

Lead: Greg Marsden, ITS Leeds

  • Only 1% of papers on transport policy examine the processes of policy implementation Transition will (necessarily) be different from place to place (within UK)
  • FASTER = identifying critical variables about institutional arrangements and local contexts leading to reductions (and failures)
  • FASTER = policy that both enables but also regulates new business model innovation
  • New Mobility Services (MAAS, on-demand taxis, car clubs) – developing faster than policy can keep up and discriminate spatially. How can policy enable sustainable and equitable acceleration of innovations?


  • Explore why particular policies are adopted more rapidly and work better in different places
  • Develop a spatial governance model of acceleration and policy transfer which seeks to identify (i) to what extent institutional structures and governance context matter to policy adoption and effectiveness (ii) the social processes underpinning the diffusion of policies across space and time
  • Understand at what level and what types of tools can accelerate adoption and how this might vary across policies.


  • Desk based reviews + international exchanges in conjunction with Project 2.3.1
  • 25 interviews which focus on specific policies and places where adoption has been accelerated (including international examples) compared with best equivalent locations where such change is yet to take effect
  • A multi-level model (from district to national) which has relevant transport network information and census data to which different policies and the scale of policy implementation can be added. Look (i) at differences across space (ii) at changing rates of adoption of a sub-set of policies over time.
  • Comparison of areas (informed by the spatial typology) on uptake of New Mobility Services – documentary analysis and interviews with actors
  • 40 interviews tracing how policies around smart mobility (e.g. IT-enabled shared mobility) spread through space and time.
  • Case studies on critical policies which will be used as part of the engagement programme with national, devolved, regional and local stakeholders and industry groups.
  • Produce a tool with open source coding routines which could be adopted by CCC to develop a more spatially disaggregated understanding of likely policy implementation pathways

2.3.2 Commission on Travel Demand

Marsden & Anable, Leeds

Lead: Greg Marsden, ITS Leeds

  • Historical failure to forecast travel demand, energy & emissions
  • Rapidly changing demand trends among certain cohorts, in certain mode, activity and space-specific contexts are open to much interpretation and debate
  • Policy risks missing windows of opportunity for radical change by misinterpreting public expectations, misunderstanding demand trends until too late and failing to capture opportunities to enable innovation
  • CTD was established as part of EUED DEMAND – proven a forum to bring business and government together to discuss future transport
  • This project will run for the full 5 years of UKCRED


  • Build on the established high level expert environment and address a series of new topics aligned to the UKCRED priorities e.g.: Flexibility, Automation, Electrification, Sharing, Fiscal Incentives, Charge points and LGV growth.
  • Bring insights to the fore from across the Centre amongst decision-makers and will provide a forum for the input and integration of other knowledge from the UK and abroad


  • Each topic will have an assigned expert
  • Projects within UKCRED will form basis for some evidence gathering
  • Supplemented by rapid evidence reviews (incl. those from other themes) and working papers
  • Each topic will have a call for evidence and two Commission meetings with international academics and practioners, ensuring a rolling programme of debating and exposing UKCRED findings
  • UKCRED int’l visits/visitor programme will be engaged

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