Build and jointly assess scenarios: Difference between revisions
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{{SumpStep/top|step=4|phase=2|title=Build and jointly assess scenarios}}<div class="sump-intro"> | {{SumpStep/top|step=4|phase=2|title=Build and jointly assess scenarios}}<div class="sump-intro"> | ||
Now the plan turns from diagnosis to direction. Alternative futures are sketched and weighed, not a single finished proposal handed down for comment, but several plausible directions the area could take, each tested for what it would actually deliver and for whom. Assessing those options openly, and with the stakeholders who will live with the result, is what makes the strategy that follows both evidenced and supported. A scenario quietly chosen behind closed doors rarely survives contact with the people it affects. | Now the plan turns from diagnosis to direction. Alternative futures are sketched and weighed, not a single finished proposal handed down for comment, but several plausible directions the area could take, each tested for what it would actually deliver and for whom. Assessing those options openly, and with the stakeholders who will live with the result, is what makes the strategy that follows both evidenced and supported. A scenario quietly chosen behind closed doors rarely survives contact with the people it affects. | ||
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<div class="sump-prereq__title">Before you start this step you should already have</div> | |||
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<div class="sump-prereq__item"><div class="sump-prereq__box"></div><div class="sump-prereq__text">A dated [[Analyse the mobility situation|accessibility baseline]] for the area as it stands, measured both against a normative standard and against what residents said they need.</div></div> | |||
<div class="sump-prereq__item"><div class="sump-prereq__box"></div><div class="sump-prereq__text">The groups your diagnosis found least well served, because a scenario aimed at the area average will not close the gap that matters.</div></div> | |||
<div class="sump-prereq__item"><div class="sump-prereq__box"></div><div class="sump-prereq__text">Every body that holds a lever [[Set up working structures|at the table and willing to weigh options together]], since no single authority in a periphery can impose a preferred future.</div></div> | |||
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Because the same accessibility model underlies both the baseline and the scenarios, a team can test a concrete change, a new mobility hub at a particular junction, a demand-responsive service across a low-density fringe, a relocated or added amenity, and read off how much of the perceived-accessibility gap it closes, and for which groups. The five intervention families DREAMS works with across the living labs give a ready vocabulary for these tests | Because the same accessibility model underlies both the baseline and the scenarios, a team can test a concrete change, a new mobility hub at a particular junction, a demand-responsive service across a low-density fringe, a relocated or added amenity, and read off how much of the perceived-accessibility gap it closes, and for which groups. The five intervention families DREAMS works with across the living labs give a ready vocabulary for these tests. They are '''mobility hubs''', '''flexible activity hubs''', '''demand-responsive transport''', '''car-sharing and car-pooling''', and '''shared micromobility'''. Most can be combined, and the point of the exercise is to find the package that performs, not to crown a single measure. | ||
== Backcasting | == Backcasting starts from the future you want == | ||
Forecasting asks what happens if current trends continue. '''Backcasting''' asks the more useful question for a 15-minute plan | Forecasting asks what happens if current trends continue. '''Backcasting''' asks the more useful question for a 15-minute plan. If this is the neighbourhood we want in ten years, what has to happen to get there? DREAMS built its scenario work around participatory backcasting workshops in the living labs, bringing together government, business and civil society to define a desired 15-minute state and then work backwards to the interventions, business models and governance changes that would reach it. The method suits the outskirts particularly well, because it forces the cross-boundary conversation about who funds and who delivers each step early, while the vision is still being shaped, rather than after a plan is fixed. | ||
The living labs show the raw material of these scenarios in practice. In [[Living_Labs/1|Vienna's Liesing district]], a flexible activity hub reactivating an underused space, paired with a light bike-sharing station, was co-designed with residents rather than specified in advance. In the Paris T12 corridor, the scenario centred on new shared-mobility stations feeding the tramway, tested directly against whether they shortened last-mile trips and pulled people out of cars. Reading how a comparable lab framed its options is a fast way to populate your own scenario set with interventions that have already been tried. | The living labs show the raw material of these scenarios in practice. In [[Living_Labs/1|Vienna's Liesing district]], a flexible activity hub reactivating an underused space, paired with a light bike-sharing station, was co-designed with residents rather than specified in advance. In the Paris T12 corridor, the scenario centred on new shared-mobility stations feeding the tramway, tested directly against whether they shortened last-mile trips and pulled people out of cars. Reading how a comparable lab framed its options is a fast way to populate your own scenario set with interventions that have already been tried. | ||
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== Building and assessing your own scenarios == | == Building and assessing your own scenarios == | ||
The sequence holds whether you use the DREAMS tools or your own modelling. Start from the baseline and the population groups your diagnosis singled out, and define two or three contrasting scenarios rather than one, each a coherent package of measures aimed at the gaps that matter most. Co-create them with residents and local actors, so the options reflect real needs, not just what is easy to build. Then assess each one consistently | The sequence holds whether you use the DREAMS tools or your own modelling. Start from the baseline and the population groups your diagnosis singled out, and define two or three contrasting scenarios rather than one, each a coherent package of measures aimed at the gaps that matter most. Co-create them with residents and local actors, so the options reflect real needs, not just what is easy to build. Then assess each one consistently, against how much of the perceived-accessibility gap it closes, for which groups, at what cost, and how it performs on the strategic objectives the area cares about. Bring the assessed options back to stakeholders as the basis for an open choice, and record both the chosen direction and the reasoning, because the scenarios you reject are part of the evidence trail too. Resist the urge to model a dozen variants when a few well-drawn, genuinely different futures will serve the decision far better than an exhaustive set no one can compare. | ||
<div class="dreams-resource-cards"> | <div class="dreams-resource-cards"> | ||
Latest revision as of 12:44, 31 August 2026
Step 4 of 12 Strategy Development
Now the plan turns from diagnosis to direction. Alternative futures are sketched and weighed, not a single finished proposal handed down for comment, but several plausible directions the area could take, each tested for what it would actually deliver and for whom. Assessing those options openly, and with the stakeholders who will live with the result, is what makes the strategy that follows both evidenced and supported. A scenario quietly chosen behind closed doors rarely survives contact with the people it affects.
Why scenarios matter more in the outskirts
In a dense core, the menu of mobility measures is broad and familiar, and the argument is usually about priorities among well-understood options. In the periphery the situation is different. The interventions that close a 15-minute gap are often newer and less proven, a shared mobility service, a flexible activity hub, a demand-responsive line, and their success depends heavily on local conditions that vary from one outskirt to the next. That uncertainty is exactly why scenario work earns its place here. Rather than betting the plan on one untested idea, scenario work lets a team lay several options side by side, see which closes the most of the perceived-accessibility gap, and understand the trade-offs before any money is committed.
The other reason is ownership. The DREAMS approach treats residents and local actors as co-authors of the scenarios, not an audience for them. Because the periphery's levers are shared across a core city, suburban municipalities and a regional transport tier, no single authority can simply impose a future. Building the options together is how a cross-boundary coalition comes to back the same direction.
How DREAMS structures the choice
DREAMS builds the answer in layers, each one adding a constraint the layer below it leaves out. The lower two describe the neighbourhood as it stands today, first against a textbook standard and then against what residents themselves need and will accept. The three above them take a proposed scenario and put it through the three tests a planning team has to pass before committing to anything.
Because the same accessibility model underlies both the baseline and the scenarios, a team can test a concrete change, a new mobility hub at a particular junction, a demand-responsive service across a low-density fringe, a relocated or added amenity, and read off how much of the perceived-accessibility gap it closes, and for which groups. The five intervention families DREAMS works with across the living labs give a ready vocabulary for these tests. They are mobility hubs, flexible activity hubs, demand-responsive transport, car-sharing and car-pooling, and shared micromobility. Most can be combined, and the point of the exercise is to find the package that performs, not to crown a single measure.
Backcasting starts from the future you want
Forecasting asks what happens if current trends continue. Backcasting asks the more useful question for a 15-minute plan. If this is the neighbourhood we want in ten years, what has to happen to get there? DREAMS built its scenario work around participatory backcasting workshops in the living labs, bringing together government, business and civil society to define a desired 15-minute state and then work backwards to the interventions, business models and governance changes that would reach it. The method suits the outskirts particularly well, because it forces the cross-boundary conversation about who funds and who delivers each step early, while the vision is still being shaped, rather than after a plan is fixed.
The living labs show the raw material of these scenarios in practice. In Vienna's Liesing district, a flexible activity hub reactivating an underused space, paired with a light bike-sharing station, was co-designed with residents rather than specified in advance. In the Paris T12 corridor, the scenario centred on new shared-mobility stations feeding the tramway, tested directly against whether they shortened last-mile trips and pulled people out of cars. Reading how a comparable lab framed its options is a fast way to populate your own scenario set with interventions that have already been tried.
The value of this step is lost if scenarios are modelled in private and only the winner is shown. Put the alternatives in front of stakeholders while they are still genuinely open, with their accessibility and impact figures attached, so the choice is made on shared evidence. A scenario the coalition helped weigh is one it will help deliver.
Building and assessing your own scenarios
The sequence holds whether you use the DREAMS tools or your own modelling. Start from the baseline and the population groups your diagnosis singled out, and define two or three contrasting scenarios rather than one, each a coherent package of measures aimed at the gaps that matter most. Co-create them with residents and local actors, so the options reflect real needs, not just what is easy to build. Then assess each one consistently, against how much of the perceived-accessibility gap it closes, for which groups, at what cost, and how it performs on the strategic objectives the area cares about. Bring the assessed options back to stakeholders as the basis for an open choice, and record both the chosen direction and the reasoning, because the scenarios you reject are part of the evidence trail too. Resist the urge to model a dozen variants when a few well-drawn, genuinely different futures will serve the decision far better than an exhaustive set no one can compare.