Fifteen Minutes on Foot: How Post-Soviet Cities Manage Access to Essential Services
Researchers from HSE University and the Institute of Geography of the Russian Academy of Sciences analysed three major Russian cities to assess their alignment with the '15-minute city' concept—an urban design that ensures residents can easily access essential services and facilities within walking distance. Naberezhnye Chelny, where most residents live in Soviet-era microdistricts, demonstrated the highest levels of accessibility. In Krasnodar, fewer than half of residents can easily reach essential facilities on foot, and in Saratov, just over a third can. The article has been published in Regional Research of Russia.
Humanity is increasingly drawn to urban life, with both the number and size of cities continuing to grow. According to Rosstat, as of January 1, 2023, approximately 109.7 million people in Russia—around 75% of the country’s population—lived in cities. Alongside urban growth, problems in the urban environment are also intensifying, including social inequality, uneven access to infrastructure, and negative environmental impacts. The concept of a 15-minute city has been proposed as one potential solution to these problems, aiming to ensure equitable access to basic services and reduce pressure on transportation systems. It envisions cities designed so that every resident can reach essential services—such as education, healthcare, shopping, and leisure—within a 15-minute walk or bicycle ride.
Researchers at HSE University and the RAS Institute of Geography studied the accessibility of essential services in three major Russian cities: Krasnodar, Saratov, and Naberezhnye Chelny. These cities clearly demonstrate the diversity of urban environments, each with its own characteristics: the historically established centre in Saratov, the extensive private housing sector in Krasnodar, the Soviet-era microdistricts in Naberezhnye Chelny, and the ongoing development of modern high-rise apartment buildings in all three cities.
The researchers used urban infrastructure data from 2GIS and OSM to evaluate how easily residents of large cities can access various services on foot. The team analysed the locations of 23 types of urban facilities, including kindergartens, schools, hospitals, stores, public transport stops, and more, and calculated a comprehensive accessibility index for urban infrastructure.
Naberezhnye Chelny, where most residents live in Soviet-era microdistricts, proved to be the closest to the 15-minute city model. There, 74% of residents live within a 15-minute walk of all essential facilities they might need. In Saratov, where the hilly terrain fragments the urban space, only 34.7% enjoy this level of accessibility, while in Krasnodar—with its extensive areas of private low-rise housing—the figure is 46.5%.
The researchers paid particular attention to how service accessibility varies across different neighbourhood types: historical centres, Soviet-era microdistricts, modern high-rise residential blocks, and areas of low-rise private houses.
The highest accessibility was observed in historical centres, where essential facilities are concentrated and located within walking distance. These areas often include higher-tier urban services—such as theatres, museums, and universities—which significantly enhance residents' quality of life. Soviet-era microdistricts also delivered strong results, outperforming modern high-rise residential complexes, where housing development often outpaces the provision of necessary infrastructure.
The lowest accessibility was found in low-rise neighbourhoods, especially on the outskirts of cities, where the average walking time to infrastructure facilities was three times longer than in historical centres and twice as long as in Soviet-era microdistricts.
However, when focusing only on essential everyday facilities—such as stores, public transport stops, and schools—and excluding unique institutions like theatres or universities, the differences between cities become less pronounced, with 86% to 92% of residents in each city living within a 15-minute walk of essential services.
Petr Gonyukhov
'To improve the accessibility of essential facilities, which are often concentrated in city centres, it is important to develop transportation networks and establish smaller service hubs directly within residential neighbourhoods. A separate challenge in suburban private-housing areas is that shops and services tend to emerge spontaneously near bus stops and intersections, forcing residents to spend more time traveling to reach them,' explains Petr Gonyukhov, a master's student at the HSE Faculty of Urban and Regional Development and co-author of the article.
These recommendations are particularly relevant for post-Soviet cities, where most residents still live in typical Soviet-era buildings. To improve access to essential services in new neighbourhoods, it is important to establish additional activity hubs and develop public transportation systems.
'The accessibility index provides a clear picture of where cities already align with the concept of a comfortable urban space and where additional efforts are needed to improve infrastructure. This data is valuable for both urban planners and residents, as it helps identify critical areas and guide urban development efforts,' sums up Alexander Sheludkov, Associate Professor at the Joint Department with the RAS Institute of Geography of the HSE Faculty of Geography and Geoinformation Technology and co-author of the article.
The study was conducted as part of the Spatial Analysis and Modelling of Urban Processes project implemented by the Research and Study Group at the HSE Faculty of Geography and Geoinformation Technology.
See also:
HSE University to Develop Predictive Analytics System for Icebreaker Motors
Industrial automation is one of the key applications of artificial intelligence. A predictive analytics system for large electric motors is among the solutions being developed for the industry as part of HSE University’s Strategic Technological Project ‘Multi-Agent Platform of AI Solutions for Industry-Specific Tasks.’ What is predictive analytics, how can it improve the operation of electric motors, and what specialists joined forces to develop this technology? Anton Zarubin, Dean of the School of Computer Science, Physics, and Technology at HSE University–St Petersburg and the project development coordinator, explains in this interview with the HSE News Service.
How to Assess Students’ Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.
Biologists Discover 'Molecular Fingerprint' of Preeclampsia
Researchers at HSE University employed a new method to model hypoxia in placental cells during pregnancies complicated by preeclampsia and identified molecular markers of tissue hypoxia. Since hypoxia is one of the key mechanisms underlying preeclampsia, these findings are important for a more accurate and timely diagnosis of the disease and for the development of effective treatment methods. The paper has been published in Placenta.
‘Hedgehog’ Versus ‘Relatives’: Researchers Measure How the Brain Responds to Unexpected Words During Natural Speech
Russian neurophysiologists, including researchers from HSE University, have demonstrated the feasibility of using event-related fields (ERFs) to study brain activity during natural speech perception. The researchers showed that this approach can be applied not only to individual words but also to continuous speech. Their findings indicate that words whose meanings differ significantly from the preceding context require longer processing times. The study also reveals that the brain processes function words in two stages: first, it identifies their grammatical role and then uses this information to predict the next word. The study has been published in Frontiers in Human Neuroscience.
HSE Researchers Create New Corpus of Early Child Speech in Russian
Researchers at the HSE Centre for Language and Brain have presented RusLan-M, an open multimedia corpus that makes it possible to trace the development of early child speech in Russian from first words to the emergence of complex grammatical constructions. The database contains around 41 hours of video recordings and more than 35,000 child utterances. The new resource will help researchers study more precisely how children acquire Russian and, in the longer term, develop more reliable tools for assessing speech development. The study has been published in Language Resources and Evaluation.
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
Researchers Rank Recommendation Algorithms Using Sports Tournament Model
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed an approach for selecting recommendation algorithms more effectively. Their approach uses pairwise comparisons of algorithms to create a tournament table, with the overall ranking based on their performance across all datasets in the tournament. This can reduce the number of algorithms that need to be tested when developing new services, saving both time and money. The study was presented at the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026).
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
Researchers at HSE University and Sber Train Neural Networks to Better Predict User Preferences
The HSE FCS AI and Digital Science Institute and Sber have introduced a new architecture for recommendation systems that combines two classes of models, enabling algorithms to better predict users’ interests and needs. A preprint of the paper has been published on arxiv.org and presented at Urban ML.