From time-based to value-based 15-minute cities: quantifying the Value dimension of accessibility and integrating it into equal-access amenity placement

The 15-minute city paradigm holds that essential services should be reachable within a short walk or cycle of every residence. Bruno and colleagues (2024) turned this ideal into a measurable, worldwide framework: a Proximity Time (PT) score averaging travel times to the 20 nearest points of interest (POIs) in each service category, plus a heuristic algorithm that relocates existing POIs or optimally places new ones to equalize per-capita access. The authors themselves identify the framework's principal open extension: shifting from purely time-based proximity to value-based accessibility that accounts for amenity quality and capacity, differential category relevance, non-replicable facilities such as hospitals and stadiums, and heterogeneous preferences across socio-economic groups. A 2026 follow-up by the same group formalizes accessibility along three axes — Proximity, Opportunity, and Value — and quantifies the first two, but explicitly leaves Value unquantified and does not extend the placement optimization. The open problem is to operationalize the Value dimension from observable data with bias control, to integrate category-specific and preference-based weighting into the accessibility measure, and to extend equal-access POI placement to value-weighted objectives and non-replicable, capacity-limited amenities, validated on real cities against the time-based baseline.

Background

The 15-minute city, popularized by Moreno and colleagues in 2021, is an urban-planning model (often called chrono-urbanism) asserting that cities function more effectively, equitably, and sustainably when every resident can reach essential services — food, schools, health care, parks — within a 15-minute walk or bike ride. Bruno, Melo, Campanelli, and Loreto (2024) made this ideal measurable at planetary scale using open data. Their inputs are Points of Interest (POIs, i.e. geolocated amenities such as restaurants, pharmacies, or schools) from OpenStreetMap (a volunteer-built worldwide geographic database), gridded population estimates, and a routing engine computing walking and cycling travel times. The study area is tessellated into hexagonal grid cells; for each cell, the accessibility to a service category is computed in the spirit of "dual access": the average travel time to the $n$ nearest POIs of that category, with $n=20$ fixed across all categories. Averaging over categories yields the Proximity Time score (PT) of a cell. Two simulations then address inequality of access: a heuristic relocation algorithm iteratively moves POIs so that each POI of category $c$ serves, on average, the same number of residents, with the per-capita capacity defined as $\text{CAP}_c = N_{\text{pop}}/N_c$ (total population divided by the number of POIs of the category); and an "empty-city" placement experiment determines the minimum number of POIs a city needs for at least 90% of its residents to live within the 15-minute standard. The worldwide analysis reveals strong heterogeneity of accessibility within and across cities, largely driven by local population density. The authors, however, list the framework's own open limitations. All service categories are treated equally and $n$ is fixed, although POIs have very different usage patterns; service quality, capacity, and opening hours are ignored, so two neighbourhoods with identical PT scores may differ sharply in the value of the services they can reach; some facilities — major hospitals, stadiums, landmark cultural institutions — cannot be replicated in every neighbourhood, breaking the equal-per-capita placement assumption; and socio-economic, cultural, and behavioural heterogeneity (differing preferences, modal choices, safety perceptions) is abstracted away. This resonates with the classic accessibility literature: Geurs and van Wee's component framework already identified destination quality and individual (person-specific) components as parts of accessibility that most operational indicators leave out. Bruno and co-authors' own conclusion is that socio-economic and cultural factors must be included to shift from time-based to value-based cities, and that a different notion of city should be pursued in which what is optimized is not only transit time but the amount of opportunities, wherever they are, available to residents. A companion essay by the same group (Hill et al., 2024) develops the conceptual case for value-based cities without quantification, and their 2026 follow-up formalizes a tri-axial framework — Proximity (active short-range access to local amenities), Opportunity (fast transit access to non-local assets such as jobs, universities, and stadiums), and Value (the quality and personal significance of reachable destinations) — quantifying the first two axes while stating that Value "is not quantified here but only theorised" and leaving the POI placement optimization untouched.

Problem Statement

Extend the universal 15-minute-city framework of Bruno et al. (2024) from purely time-based proximity to value-based accessibility. The overarching objective is a validated value-based generalization of the framework — accessibility scores and placement decisions that reflect the value of reachable services, not only the time to reach them — comprising three jointly supporting components:

1. Value measurement. Define and quantify a Value dimension of accessibility — the quality, capacity, differential usage, and personal or community significance of the POIs a resident can reach — from observable data (for example crowdsourced ratings, transaction prices, visitation statistics, inspections, or stated-preference surveys), with explicit treatment of each source's biases, and validate the measure against independent evidence such as held-out stated-preference or behavioral data.

2. Preference- and category-aware accessibility scores. Integrate category-specific relevance weights and socio-economic, cultural, and behavioural heterogeneity of demand into the accessibility measure, replacing the equal treatment of service categories and the fixed $n$-nearest-POI averaging convention where the data support it, and characterize the sensitivity of accessibility rankings to these choices.

3. Value-aware placement. Extend the equal-access POI relocation and optimal-placement optimization so that what is optimized is not only travel time but the value-weighted opportunities available to residents, including non-replicable, capacity-limited facilities (hospitals, stadiums, landmark institutions) that cannot be replicated in every neighbourhood.

The framework is to be demonstrated on real cities with open data and compared against the time-based PT baseline. The three components are jointly required: a value measure that is never connected to accessibility scoring or placement, or an optimization of time-based objectives, would not answer the question.

References

1. Matteo Bruno, Hygor Piaget Monteiro Melo, Bruno Campanelli, Vittorio Loreto (2024). A universal framework for inclusive 15-minute cities. Nature Cities 1, 633–641, DOI: 10.1038/s44284-024-00119-4 (preprint arXiv:2408.03794, https://arxiv.org/abs/2408.03794). https://doi.org/10.1038/s44284-024-00119-4
2. Carlos Moreno, Zaheer Allam, Didier Chabaud, Catherine Gall, Florent Pratlong (2021). Introducing the "15-Minute City": Sustainability, Resilience and Place Identity in Future Post-Pandemic Cities. Smart Cities 4, 93–111, DOI: 10.3390/smartcities4010006. https://doi.org/10.3390/smartcities4010006
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