Feasible paths of interventions

Deliverable D4.3 of the H2020 project LEVITATE


Zach, M.; Sawas, M.; Boghani, H.C.; Zwart, R. de



The main goal of this deliverable (Feasible paths of interventions) is to provide preliminary answers to one of the central questions of the LEVITATE project: Given a certain vision, a set of quantified policy goals for a city or a region, how can this be connected to recommended policy interventions, supporting to achieve that vision?

The policy support tool (PST) developed in LEVITATE will be the main project output, linking policy interventions to the final impacts of Connected and Automated Transport Systems (CATS) and corresponding indicators. This link should work in both directions:

  1. Forecasting: Predicting the impacts and the development of indicators for certain scenarios and bundles of policy interventions.
  2. Backcasting: Starting from a given vision of the future, defined by vision characteristics and come up with recommended sequence of policy interventions that facilitates a path (development) towards that vision.

This deliverable is setting the basis for the second direction, the backcasting approaches (dynamic and static) in LEVITATE: The results are relevant to integrate the backcasting process into the final version of the PST (dynamic backcasting), but also – in the form of case studies – for further city specific evaluation in WP 5-7 (static backcasting).

Recently, backcasting approaches have been applied in several domains, as discussed in a focussed survey of relevant literature, using various qualitative and quantitative methods. Of particular relevance for LEVITATE is the application of backcasting in the domain of automated driving. A recently completed research project titled “System Scenarios Automated Driving in Personal Mobility” (SAFIP), gives insight how policy interventions can be selected and fine-tuned in order to reach given targets.

Defining a desirable vision in a quantitative way is the essential starting point for the backcasting process. From that vision the idea is to work backwards, via influencing factors (that are impacting the goals and indicators of the vision), to policy interventions which address these factors and thereby contribute towards the vision. Generating this series of logical links represents the central aim of this deliverable, as it highlights feasible paths of intervention, steering into the desired direction.

Previous work in LEVITATE in several work packages has already provided the basic ingredients for this approach. In particular, methods for defining quantitative visions related to CATS have been proposed in WP4, considering a wider range of indicators across four dimensions (safety, society, environment and economy), impact relationships have been analysed in WP3, and relevant use cases, parameters and policy interventions have been collected in WP 5-7, where following main use cases are considered

  • Use case 1 – Automated urban transport (WP5)
  • Use case 2 – Passenger cars (WP6)
  • Use case 3 – Freight transport and logistics (WP7)

After summarising the background and related work that sets the context for backcasting in LEVITATE, the actual backcasting process is explained in more detail. Its main inputs are the existing documentation of city strategies which are relevant to mobility and the LEVITATE indicator framework. Based on that, the following main steps are performed by means of a dialogue with city representatives:

  1. Define Vision
  2. Propose and prioritise Influencing Factors
  3. Propose and prioritise Policy Interventions

For defining the vision of a city and possible transformation corridors in a quantitative way, data-driven methods previously applied in WP4 can be used to support the city dialogues. This results in a relatively small set of target indicators, along with target values and a target timescale.

The most challenging part in the backcasting process might be the second step – to determine the most promising influencing factors – as the impact relationships between these and the target indicators can only be estimated qualitatively, at this stage in the project. Therefore, it will be important to verify the assumed relationships afterwards by means of quantitative methods in WP 5 - 7.

Finally, promising policy interventions are discussed and prioritised with the cities, derived from the selected influencing factors. These policy interventions in principle are taken from the candidates that have already been analysed in the early phase of LEVITATE, but are adapted to specific city requirements and strategies.

The core part of this deliverable presents the detailed results of the backcasting city dialogues for three cities (or regions, respectively)

  1. City of Vienna
  2. Greater Manchester
  3. City of Amsterdam

This will be the base for developing case studies further in LEVITATE. The results of these dialogues show a high degree of congruence (for example, regarding environmental goals), but also exhibit different prioritisation of key targets and influencing factors. One striking difference that was observed is that for the Greater Manchester (GM) area, the economic goals (e.g. increase in employment) and related factors (e.g. housing and road capacities between cities) are seen as a high-priority agenda and is driving force for the activities in GM but not for Vienna and Amsterdam.

The qualitative results presented and discussed in this deliverable can be considered as the first step in describing feasible paths of interventions for cities related to CATS. They will be used for further investigations in task T4.4, where use cases and policy interventions will be combined and, their timewise implementation will be analysed further. Task T4.4 will also provide a brief description of modelling and simulation techniques that will be applied for detailed verification within WP5-7.

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European Commission, Brussels