The paper explores the transformative role of mobility in suburban center of Sremska Kamenica, within the urban area of Novi Sad, Serbia, where a transit road passes through the heart of the settlement center. Throughout history central functions of the settlement were developed along the main road and thanks to its busyness. However, population growth together with the rise of the motorization rate and the share of motor traffic in modal split caused the traffic congestions and threatened the pedestrian and social activities recently. The imminent construction of a new bridge, as part of a ring road around Novi Sad, stands as a pivotal intervention that promises to divert traffic flows away from the overloaded center, ushering in a new era of pedestrian-friendly public spaces. Finalization of this bridge would divert traffic flows – presumably, the traditional center, now overloaded with transit traffic, would be less busy, and at the same time, as shown in Space Syntax analysis, accessibility at the local level wouldn’t fall. The key objective is to understand the development of the centrality in Sremska Kamenica through time and to predict how changes in mobility patterns will affect further public space usage and livability. The research approach is based on the combination of Space Syntax tool, site surveys, contextual analysis and planning documents reading. The paper shows how the traffic diversion facilitated by the new bridge gives the opportunity for urban planners to reimagine public spaces, prioritizing vibrant, walkable spaces that promote social interaction and improve overall urban experience. 

This paper discuss the mass housing neighborhoods named Limans in Novi Sad, Serbia, with the specific case study of urban quarter named Liman 3, showing a good example of gradual functional diversification of neighborhood since post-war period up to present days. The research approach of the paper is based on combination of Space Syntax, Spacematrix and Mixed-use index tool, as well as on contextual analysis with the specific interest on societal and economical issues. Aim of the paper is to show how local circumstances joint with global influences, evident in the example of Liman 3 urban area, have induced transition from completely residential to moderately mixed-use neighborhood during more than 40 years development period.

The paper examines the possibilities of applying the Space Syntax methodology in urban analysis and planning, with a focus on its use for analyzing the existing conditions, testing development scenarios, and formulating guidelines for spatial interventions. In addition to providing a theoretical and methodological framework, this approach offers a set of tools for describing and quantifying the spatial properties of the built environment, most commonly the street network, and its relationship to socio-economic activities. The case study focuses on Sremska Kamenica, a suburban settlement within the urban construction area of Novi Sad, whose future development is examined through potential changes in movement flows and accessibility following the construction of new bridges across the Danube River. The applied methodology combines Space Syntax analysis with field research, contextual analysis, and a review of relevant planning documentation. The analyses were conducted at a global level to observe vehicle movement patterns, complemented by analyses in local radii (400 m) to assess pedestrian accessibility. Two key measures were used: Integration and Choice, to identify dominant movement directions, zones of spatial isolation, and key segments expected to experience increased accessibility after the introduction of new connections. The results indicate the potential for an overall increase in accessibility at the system level, while also revealing differentiated effects within Sremska Kamenica: certain parts of the network become significantly more integrated and attractive for new programs, whereas the central area is expected to experience a reduction in transit traffic while maintaining local accessibility. The findings provide a basis for interpreting the impacts of infrastructural changes and for formulating guidelines for managing settlement development.

The development of polycentric urban regions has strengthened the role of suburban settlements as secondary centres. However, centrality continues to be assessed primarily through the concentration of urban functions and amenities, while its social dimension remains insufficiently explored. This study extends the application of sociotope mapping from public open spaces to the assessment of social centrality in suburban settlement centres. The proposed framework was applied to Veternik, a suburban settlement within the urban region of Novi Sad in Serbia, combining functional and spatial analysis, a questionnaire survey, the Relative Importance Index, and GIS-based sociotope mapping. Functional and spatial analysis identified the existing urban functions, amenities, and their spatial organisation, while the questionnaire survey examined residents’ everyday patterns and frequency of centre-use activities. The Relative Importance Index ranked the relative importance of these activities, and GIS-based sociotope mapping visualised the resulting centre-use values. The findings indicate that Veternik exhibits relatively developed functional centrality, whereas its social centrality remains limited, particularly when considered in relation to users’ perceptions of the centre of Novi Sad. The study demonstrates the potential of sociotope mapping as a complementary framework for assessing social centrality and supporting the planning of socially sustainable suburban settlement centres.

The increasing urbanization has intensified air pollution exposure, seriously threatening public health. This study examines how various machine learning and deep learning models can enhance the optimization of traffic changes within an urban planning framework, with a particular emphasis on air quality. Using local pollutant data from stations located in Novi Sad (Serbia), the models estimate emission changes from major transport infrastructure modifications. Two complementary approaches are used: (1) multi-output regression aimed at predicting air pollutant concentrations, and (2) multi-output classification focused on predicting the air quality index (AQI). For daily predictions, the Long Short-Term Memory (LSTM) model achieved the best R² = 0.5, while the Balanced Random Forest classifier reached 0.775 balanced accuracy for AQI classification. Finally, the most accurate models were applied to two prospective city network scenarios projected - (1) construction of two river bridges rerouting heavy traffic, and (2) creation of an extended pedestrian zone downtown - to quantify the expected reduction in pollutant concentrations and estimate possible changes in AQI categories. Depending on the pollutant, the results indicate a relative daily decrease between \(0.7\%\) and \(2.9\%\) on average for both scenarios. Although the improvement might seem small, this reduction is obtained for 13 street segments within a 100-meter radius around air stations, and a potential reduction for the whole city could be more substantial. Additionally, to turn forecasts into policy-ready evidence for traffic system decisions, we paired predictive models with explainable artificial intelligence (XAI) methods. Applying XAI methods reveals that meteorological factors, including relative humidity, total precipitation, temperature, and wind speed, primarily account for the predictions of developed models. Overall, the XAI methods highlight the combined influence of temporal, spatial, and meteorological factors on AQI.

Urban Development Explorations using Natural Experiments (UDENE) is a forward-looking initiative under the Horizon Europe program that merges Earth Observation (EO) technologies with urban planning to tackle pressing urban challenges. By utilizing Copernicus satellite imagery and organizing local in-situ data into interoperable data cubes, UDENE provides a comprehensive framework for data-driven decision-making. Further, it is applied the concept of “natural experiments” - real-life changes analyzed with the rigor of controlled studies - to uncover causal relationships in urban development. A primary goal is to incorporate structured urban data into the broader Copernicus data cube federation, enabling consistent analysis of urban impacts across different times and locations. To support this, UDENE develops advanced sensitivity analysis methods for validating and applying multivariate causal models, enhancing predictions on factors such as air pollution, urban heat, mobility, and disaster resilience. To close the gap between high-level EO technologies and real-world planning needs, we are introducing the three core tools: the UDENE’s Data Cube, which populates in-situ data EO based analysis-ready data and datasets; the Exploration Tool, which empowers planners and policymakers to simulate, assess, and visualize urban interventions; and a matchmaking tool connecting users with EO-based services. Together, these tools foster informed urban strategies grounded in EO data and causal inference.