Analyse the mobility situation

Step 3 of 12 Preparation & Analysis

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With the working structures in place and the planning framework set, Step 3 is where the plan finally meets reality. It builds the evidence base the rest of the SUMP will stand on: how people actually move today, where access to everyday life falls short, who is most affected by those gaps, and what the genuine opportunities are. Just as importantly, it fixes the baseline, the measured starting point against which the finished plan will later be judged. A plan is only ever as good as the diagnosis underneath it, and a weak or borrowed diagnosis produces a weak or borrowed plan.

What standard analysis misses in the periphery

Conventional mobility analysis is tuned to the dense city core, where the headline problem is congestion and the natural metrics are traffic volumes, journey speeds and modal split at the cordon. Carried unchanged into the outskirts, that lens measures the wrong thing. In low- and mid-density peripheries the real issue is not that movement is slow but that so much of daily life can only be reached by car at all: whether a resident can get to groceries, a doctor, a school or a green space on foot, by bike or by public transport. DREAMS reframes the analysis around proximity and accessibility rather than traffic flow, and that shift changes both what you measure and what you count as a problem in the first place.

The DREAMS benchmarking work (Deliverable 2.3) shows why this matters empirically. Drawing on national household travel surveys across the six study regions, it found that accessibility in the outskirts is generally worse than in the urban core. Budapest's periphery had the lowest accessibility for both work and shopping trips, while Munich was the notable exception, with work, shopping and leisure access actually improving in its suburbs. Above all, the analysis found a high degree of car dependency for everyday trips in the suburbs, and especially for shopping, which is exactly the kind of routine, frequent journey a 15-minute neighbourhood is meant to bring within reach. Diagnosing that car-dependency honestly, trip purpose by trip purpose, is the work of this step.

How DREAMS measures accessibility

The platform's DREAMS Accessibility Tool (DAT) gives practitioners a way to map this directly. Rather than counting cars, it asks of every location a simpler question: what share of the amenities people actually care about, groceries, pharmacies, schools, cafés, parks and the rest, can be reached within an acceptable walk? It answers that question two ways.

The normative view

Every amenity counts equally, and reaching just one of each within a 15-minute walk is enough. This is what an abstract textbook standard says about your area.

The perceived view

The weights, the number of each amenity people want nearby, and the walking times they will genuinely accept, all drawn from the DREAMS resident survey across the six living labs. This is whether residents would actually experience the area as well served.

The two rarely agree, and the gap between them is the insight this step produces.

What the living labs revealed

Measured against the textbook standard, the six labs looked reasonably healthy. On paper each came out as a passable 15-minute neighbourhood, a place where residents could already reach most of the everyday amenities they need within a short walk.

That impression did not stand with what residents actually want. Once the analysis used their real priorities, gathered by a survey in each living lab neighbourhood, measured accessibility fell in every single one of the labs. The survey contained questions like how many of each amenity they need nearby and how long they would deem an acceptable walk for it. How far the measured accessibility fell depended entirely on who you were planning for. For young adults in Évry-Courcouronnes the change was slight, because the area already gave them alternatives for the things they cared about. But the groups with the least flexibility fared badly: for parents in Brussels, older residents in Geretsried, working-age adults in Budapest and low-income households in Utrecht, much of that apparent access turned out to be unusable in practice, and their real proximity was far worse than the textbook picture suggested.

One everyday destination drove the gap almost everywhere: groceries. The food shop, the most frequent trip in most households, was consistently the hardest amenity to reach on foot. That is the concrete counterpart to the benchmarking finding that suburban residents are most car-dependent precisely for their shopping trips.

DREAMS also looked beneath the averages. A persona analysis of the survey data (Deliverable 4.2) found that accessibility needs do not map neatly onto age or income. Some profiles, such as families with children or older people ageing in place, follow clear life-course logics, while others, built around daily convenience or social life, cut straight across demographic lines and coexist within the same neighbourhood. The behavioural choice experiments reinforced the point: public transport remained the most stable and attractive option everywhere, with shared and micromobility services working as complements rather than universal substitutes, their usefulness depending heavily on trip purpose and local context.

These findings translate into three rules for diagnosing your own neighbourhood, and they shape every later step of the SUMP:

Set targets for choice, not coverage.

Residents value alternatives, two or three groceries within reach rather than one. That preference alone is what turned each lab's healthy-looking result into a shortfall, so a target written as "one amenity within 15 minutes" will flatter your area and hide the real need. Write yours around having a genuine choice for the trips that matter most.

Map the underserved edge, not the average.

Every lab held a genuinely well-served core, where residents could reach almost everything on foot, right next to peripheral pockets that could not. A single headline figure for the whole area hides that split. Break your results down spatially and by the groups you are planning for, because that is where the real shortfalls sit and where a plan can do the most good.

Plan for more than walking.

Walking alone cannot close the gap in the outskirts. The choice experiments showed public transport as the steady backbone, with shared and micromobility as complements for particular trips. Build the bicycle, shared modes and public transport into the access mix from the start, not as later add-ons.

Analysing your own neighbourhood

The sequence for this step is the same whether you use the DREAMS tools or your own data. Start by assembling what already exists: household travel surveys, census and land-use data, public transport service data, and an OpenStreetMap-based amenity inventory. Map your area's normative accessibility to see where the obvious cold spots are. Then gather the perceived side through a resident survey, so you learn not just what is reachable but what residents need, how many of each amenity they want, and how far they will really walk. Compare the two, break the results down by the population groups your framework singled out in Step 2, and write the findings up as an explicit, dated baseline. Resist the temptation to analyse everything at once: a focused diagnosis of the trips and groups that matter most will serve the plan far better than an exhaustive one that no one finishes.

Measure the gap, not just the map. The normative accessibility map will flatter your area. The number that should drive your plan is the difference between what a textbook 15-minute standard rewards and what residents actually experience and, underneath it, which trips still force people into a car.

Go deeper

The idea that perceived accessibility differs from the calculated kind is shared by the sister DUT project AccessCity4All, which combines accessibility modelling with walk-along interviews and participatory mapping across neighbourhoods from central to outskirt, a useful complement to the DREAMS survey-based approach. For practical guidance on gathering the data, especially on walking and cycling, see Module 3: Data for active modes (Baltic Sea Region Competence Centre on SUMP).

Sources & further information: EU SUMP Guidelines 2.0 (Rupprecht Consult, 2019); DREAMS Deliverable 2.3, Benchmarking travel and activity location choice behaviour across study locations; DREAMS Deliverable 3.1, The DREAMS Accessibility Modeling Framework for Decision Support in X-Minute City Planning; resident preference and persona data from DREAMS Deliverable 4.2.

← Determine the planning framework

SUMP overview

Build and jointly assess scenarios →