Search Results (713)

Urban park landscape design has significant potential to alleviate heat stress and promote public health, particularly during extreme summer heat. This study explores how the spatial configuration of landscapes within the Yanghu Wetland Park in Changsha, China, influences pedestrian thermal comfort and destination loyalty under hot summer conditions, and how these factors affect public perceived health. It enriches current research by examining the impact of landscape spatial configuration, thermal comfort, and destination loyalty on public perceived health from a psychological perspective. We identified connections between park users’ spatial perceptions and their psychological and health perceptions. We used structural equation modeling (SEM) to examine the relationships among visitors’ spatial perception, psychological perceptions, and health perceptions within this large urban wetland park. At the same time, we explored how landscape characteristics, thermal comfort, destination loyalty, and public perceived health interact. This research constructs a Spatial–Thermal–Perception–Behavior (SPB) theoretical framework for such complex blue-green spaces, providing a multidimensional perspective on the relationship between the environment and health. Based on a survey of 321 visitors, This study pioneers the SPB theoretical framework, clarifying how this wetland park’s landscape configurations impact public perceived health through the mediating pathways of thermal comfort and destination loyalty. It provides a scientific basis for heat-adaptive landscape design in similar wetland park settings, aiming to enhance resident well-being and improve public perceived health. Full article

The built environment (BE) plays a central role in shaping everyday mobility patterns and determining how physical activity (PA) is integrated into daily life. Foundational BE frameworks such as the 5Ds (density, diversity, design, distance to transit, and destination accessibility) have shaped policy and planning worldwide. However, these frameworks remain predominantly spatial and overlook temporal dynamics. This review addresses this omission by introducing Duration as the sixth dimension (6th D) of the BE framework, reframing accessibility in terms of the lived temporal experience of movement rather than static spatial distance. Travel conditions vary across the day. Routes that are safe and efficient at one time often become congested, stressful, and prohibitive at another. Such variability undermines PA and active transport (AT) and diminishes the health benefits of supportive BE. Methodologically, the review synthesises evidence from 1991 to 2025 across public health, transport planning, BE, and environmental psychology. Pertinent literature (102 shortlisted articles) published in English was retrieved from Scopus, Web of Science (WoS), and PubMed, which collectively provide comprehensive coverage of multidisciplinary research spanning transport planning, public health, and behavioural sciences. The PRISMA 2020 approach and VOSviewer (version 1.6.20), were used, together with a structured, Excel-based integrative synthesis, to analyse publication trends, conceptual evolution, and integrative patterns in the retrieved literature. The synthesis shows that accessibility, mobility stress, and travel behaviour are strongly time-dependent. This time dependence is systematic rather than incidental across contexts. Globally, commute durations beyond 45 min are associated with lower life satisfaction and poorer health outcomes. Embedding Duration within BE frameworks establishes a time-responsive and equity-sensitive paradigm for healthier and more resilient urban systems. Full article

The current refugee crisis has revealed flaws in existing systems. Factors such as socioeconomic background, access to housing, and urban policies influence refugees’ abilities to fully participate in city life. The research objective is to analyze the interplay between housing access for adult refugees residing in Curitiba, Brazil, and the city’s targeted public policies and strategies for refugees. The research methodology adopts a case study approach centered on Curitiba, Brazil, with the city shown as a key destination for refugees in Brazil. This study combines qualitative and quantitative techniques, following a structured research protocol that guides the processes of data collection and analysis. The innovation and originality lie in offering a new perspective on how urban strategies intersect with the rights and inclusion of refugees, exploring the relationship between refugees’ housing access and its interconnection with the strategic digital city framework. The results highlight the importance of a multifaceted approach to addressing housing access challenges for refugees, which includes safeguarding their rights, promoting stability, integration, and ensuring their participation in shaping public policies. The conclusion outlines the urgent need to promote integration by reassessing housing affordability, ensuring access to services, engaging refugees in decision-making processes, and improving their social welfare. Full article

This paper examines the role of institutional logics in shaping heritage-led urban regeneration across fifteen adaptive reuse projects of former tobacco factories in Europe. By categorizing managing authorities into public, private, and community-led actors, the study interprets regeneration outcomes, such as community participation, tourism growth, and crime reduction, through the lens of institutional theory. The analysis reveals that each authority type operates under distinct logics: regulative (public), market-driven (private), and normative (community), which significantly influence the depth and type of impact achieved. Through a comparative framework and empirical indicators, the paper highlights how institutional arrangements affect not only project design but also questions of inclusion, identity, and sustainability. Findings challenge simplistic binaries of top-down versus bottom-up governance and offer a more nuanced understanding of how urban heritage can serve divergent values. The paper concludes with implications for urban policy and future research on hybrid and participatory models of heritage governance. Full article

Promoting demand-responsive transit (DRT) is crucial for developing sustainable and green transportation systems in urban areas, especially in light of decreasing transit ridership and increasingly varying demand. However, the effectiveness of such services hinges on their ability to efficiently match varying travel demand. This paper presents a data-driven framework for the joint optimization of customized bus routes and timetables, to enhance both service quality and operational sustainability. Our approach leverages large-scale taxi trip data to identify latent travel demand, applying a spatial–temporal clustering method to group trip requests and identify DRT stops by trip origin, destination, and direction. An adaptive large neighborhood search (ALNS) algorithm is improved to co-optimize passenger waiting times and bus operation costs, where an unbalanced penalty for early or late schedule deviations is developed to better reflect passengers’ discomfort. The framework’s performance is validated through a real-world case study, demonstrating its ability to generate efficient routes and schedules. The model manages to improve passenger experience and reduce operation costs. By creating a more appealing and efficient service, this model contributes directly to the goals of green transport in terms of reducing the total vehicle kilometers that are traveled, and demonstrating a viable, high-quality alternative to private car usage. This study offers a practical and robust tool for transit planners to design a next-generation DRT system that is both economically viable and environmentally sustainable. Full article

Urban metro systems face increasing pressure to reconcile passenger service quality with infrastructure maintenance demands during late-night operations. This study proposes a coordinated optimization framework that integrates train timetabling with a flexible and selective skip-stop strategy. A mixed-integer programming model is formulated to jointly maximize passenger Origin–Destination (OD) accessibility and extend available maintenance windows. To solve the high-dimensional and computationally intensive model efficiently, a hybrid GWO-CNN algorithm is designed, where a Convolutional Neural Network (CNN)-based surrogate model replaces the time-consuming fitness evaluation process in the Grey Wolf Optimizer (GWO). A real-world case study on the Beijing metro network demonstrates that the proposed method increases OD accessibility by 23.60% and extends maintenance window by 8310 s. Compared to the conventional GWO, the GWO-CNN algorithm achieves superior solution quality with a 98.4% reduction in computation time. Sensitivity analyses further reveal the trade-offs between skip-stop rates, objective weight settings, and optimization outcomes, offering practical insights for metro operators in tailoring late-night scheduling strategies to both passenger demand and maintenance priorities. Full article

Origin–destination (OD) flow prediction is fundamental to intelligent transportation systems, yet existing diffusion-based models face two critical limitations. First, they inadequately exploit spatial semantics, focusing primarily on temporal dependencies or topological correlations while neglecting urban functional heterogeneity encoded in Points of Interest (POIs). Second, static embedding fusion cannot dynamically capture semantic importance variations during denoising—particularly during traffic surges in POI-dense areas. To address these gaps, we propose the Cross-Attention Diffusion Model (CADM), a semantically conditioned framework for short-term OD flow forecasting. CADM integrates POI embeddings as spatial semantic priors and employs cross-attention to enable semantic-guided denoising, facilitating dynamic spatiotemporal feature fusion. This design adaptively reweights regional representations throughout reverse diffusion, enhancing the model’s capacity to capture complex mobility patterns. Experiments on real-world datasets demonstrate that CADM achieves balanced performance across multiple metrics. At the 30 min horizon, CADM attains the lowest RMSE of 5.77, outperforming iTransformer by 1.9%, while maintaining competitive performance at the 15 min horizon. Ablation studies confirm that removing POI features increases prediction errors by 15–20%, validating the critical role of semantic conditioning. These findings advance semantic-aware generative modeling for spatiotemporal prediction and provide practical insights for intelligent transportation systems, particularly for newly established transportation hubs or functional zone reconfigurations where semantic understanding is essential. Full article

Nighttime tourism has become a key driver of urban nighttime economic development. The nighttime tourism experiencescape (NTE)—comprising elements such as atmospheric lighting landscapes, culturally distinctive night markets, and diverse entertainment formats—creates an environment markedly distinct from daytime settings. This NTE significantly influences tourist experiences and contributes critically to the sustainable development of urban destinations. Grounded in the Stimulus–Organism–Response framework, this study investigates how the NTE shapes tourist loyalty. Empirical results indicate that the effect of the NTE on tourist loyalty is primarily mediated by place attachment, with place dependence demonstrating a stronger mediating effect than place identity. In the direct pathway, only the socio-symbolic dimension of the NTE exerts a significant positive impact on tourist loyalty. The study offers both theoretical and practical contributions: it reveals the mechanisms that influence tourist loyalty in nocturnal contexts and offers actionable insights into the sustainable management of nighttime tourism in urban destinations. Full article

Bike-sharing services contribute to reducing emissions and conserving natural resources within urban transportation systems. They also promote public health by encouraging physical activity and generate economic benefits through shorter travel times, lower transportation costs, and decreased demand for parking infrastructure. This paper examines the use of shared micro-mobility services in the Italian cities of Florence and Bologna, based on an analysis of GPS origin–destination data and associated temporal coordinates provided by the RideMovi company. Given the still-limited number of studies on free-floating and electric-scooter-sharing systems, the objective of this work is to quantify the performance of electric bikes and e-scooters in bike-sharing schemes and compare it to traditional, muscular bikes. Trips were reconstructed starting from GPS data of origin and destination of the trip with a shortest path criteria that considers the availability of bike lanes. Results show that e-bikes are from 22 to 26% faster on average with respect to muscular bikes, extending trip range in Bologna but not in Florence. Electric modes attract more users than traditional bikes, e-bikes have from 40 to 128% higher daily turnover in Bologna and Florence and e-scooters from 33 to 62% higher in Florence with respect to traditional bikes. Overall, turnover is fairly low, with less than two trips per vehicle per day. The performance is measured in terms of trip duration, speed, and distance. Further characteristics such as daily turnover by transport mode are investigated and compared. Finally, spatial analysis was conducted to observe demand asymmetries in the two case studies. The results aim to support planners and operators in designing and managing more efficient and user-oriented services. Full article

The dual Hukou system, originating in China’s planned economy period, structured Chinese society into separate urban and rural segments, thereby generating distinct sets of rights and benefits for agricultural and non-agricultural residents regarding land, social security, education, and healthcare. Urban home purchase is a pivotal indicator of social integration for rural–urban migrants in destination cities. While the literature has extensively examined migrants’ residential conditions in China, the institutional impact of the agricultural hukou system—a core constraint—on their urban homeownership, along with its underlying mechanisms and heterogeneity, remains underexplored. To address this gap, this study adopts a twofold approach: theoretically, it employs the separating equilibrium model in housing markets with incomplete information to verify that agricultural hukou acts as an institutional barrier to migrants’ local home purchases; empirically, it uses data from the China Migrants Dynamic Survey (CMDS) and applies the Fairlie decomposition method to quantify the constraint effect. The empirical results suggest that agricultural hukou exerts a 29.72% suppressive effect on migrants’ urban home purchase behavior. This effect operates indirectly by weakening migrants’ long-term settlement intention, which serves as a mediating variable. Moreover, the hindrance of agricultural hukou varies heterogeneously across groups, differing in education level, generational cohort, and regional distribution. To advance the fair and sustainable development of the real estate market, we advocate accelerating hukou reform by decoupling public services from residence status, fostering inclusive urbanization, and ensuring equitable development of housing markets. Full article

Tourism today represents a strategic engine of economic growth, contributing substantially to GDP, employment, and export revenues. Accounting for approximately 10% of global GDP, the sector plays a significant role in tourism-intensive countries. Tourism has shown remarkable resilience and recovery capacity in the post-COVID era, reaffirming its status not only as an economic sector but also as a spatial phenomenon. Due to its inherent place-based nature, tourism cannot be outsourced: it relies on the unique cultural, environmental, and territorial assets of specific locations. While this makes tourism a powerful driver of local development, it also presents challenges related to environmental stress, cultural commodification, and social tensions, especially in over-visited destinations such as Venice, Barcelona, or Lisbon. This paper introduces the concept of “anti-gravity tourism”, a novel framework inspired by physics, to describe planning strategies aimed at counteracting the gravitational pull exerted by mass tourism hotspots. Using the STESY model, the study applies spatial analysis to four case study areas, identifying Destination Areas (DAs) through clustering techniques and developing spatial design proposals aligned with the principles of the New Urban Agenda (NUA). The results highlight how “anti-gravity” strategies can be operationalized through context-sensitive planning tools to mitigate overtourism, support territorial equity, and maximize positive externalities. Ultimately, the paper argues for a paradigm shift towards tourism policies that ensure long-term sustainability by balancing economic growth with social inclusion and environmental stewardship. Full article

Resource-based regions play an indispensable role as strategic bases for national energy and raw material supply in the global industrialization and urbanization process. However, intensive and large-scale natural resource exploitation—particularly mineral extraction—often triggers dramatic land use/cover changes, leading to a series of problems including cultivated land degradation, ecological function deterioration, and human settlement environment degradation. However, a systematic understanding of the functional transitions within the land use system and their drivers in such regions remains limited. This study takes Shenmu City, a typical resource-based city in the ecologically vulnerable Loess Plateau, as a case study to systematically analyze the transition characteristics and driving mechanisms of land use functions from 2000 to 2020. By constructing an integrated “element–structure–function” analytical framework and employing a suite of methods, including land use transfer matrix, Spearman correlation analysis, and random forest with SHAP interpretation, we reveal the complex spatiotemporal evolution patterns of production–living–ecological functions and their interactions. The results demonstrate that Shenmu City has undergone rapid land use transformation, with the total transition area increasing from 27,394.11 ha during 2000–2010 to 43,890.21 ha during 2010–2020. Grassland served as the primary transition source, accounting for 66.5% of the total transition area, while artificial surfaces became the main transition destination, receiving 38.6% of the transferred area. The human footprint index (SHAP importance: 4.011) and precipitation (2.025) emerged as the dominant factors driving land use functional transitions. Functional interactions exhibited dynamic changes, with synergistic relationships predominating but showing signs of weakening in later periods. The findings provide scientific evidence and a transferable analytical framework for territorial space optimization and ecological restoration management not only in Shenmu but also in analogous resource-based regions facing similar development–environment conflicts. Full article

The contemporary digital landscape overwhelms visitors with fragmented and dynamic information, complicating travel planning and often leading to decision paralysis. This paper presents a real-world case study on the design and deployment of an intelligent tourism assistant for Valencia, Spain, built upon a Retrieval-Augmented Generation (RAG) architecture. To address the complexity of integrating static attraction data, live events, and geospatial context, we implemented a multi-agent system orchestrated via the ReAct (Reason + Act) paradigm, comprising specialized Retrieval, Events, and Geospatial Agents. Powered by a large language model, the system unifies heterogeneous data sources—including official tourism repositories and OpenStreetMap—within a single conversational interface. Our contribution centers on practical insights and engineering lessons from developing RAG in an operational urban tourism environment. We outline data preprocessing strategies, such as coreference resolution, to improve contextual consistency and reduce hallucinations. System performance is evaluated using Retrieval Augmented Generation Assessment (RAGAS) metrics, yielding quantitative results that assess both retrieval efficiency and generation quality, with the Mistral Small 3.1 model achieving an Answer Relevancy score of 0.897. Overall, this work highlights both the challenges and advantages of using agent-based RAG to manage urban-scale information complexity, providing guidance for developers aiming to build trustworthy, context-aware AI systems for smart destination management. Full article

This paper presents a comprehensive methodology to forecast the daily energy demand associated with recharging private electric vehicles in urban areas. The approach is based on plausible scenarios regarding the penetration of battery-powered vehicles and the availability of charging infrastructure. Accurate space and time forecasting of charging activities and power requirements is a critical issue in supporting the transition from conventional to battery-powered vehicles for urban mobility. This technological shift represents a key milestone toward achieving the zero-emissions target set by the European Green Deal for 2050. The methodology leverages Floating Car Data (FCD) samples. The widespread use of On-Board Units (OBUs) in private vehicles for insurance purposes ensures the methodology’s applicability across diverse geographical contexts. In addition to FCD samples, the estimation of charging demand for private electric vehicles is informed by a large-scale, detailed survey conducted by ENEA in Italy in 2023. Funded by the Ministry of Environment and Energy Security as part of the National Research on the Electric System, the survey explored individual charging behaviors during daily urban trips and was designed to calibrate a discrete choice model. To date, the methodology has been applied to the Metropolitan Area of Rome, demonstrating robustness and reliability in its results on two different scenarios of analysis. Each demand/supply scenario has been evaluated in terms of the hourly distribution of peak charging power demand, at the level of individual urban zones or across broader areas. Results highlight the role of the different components of power demand (at home or at other destinations) in both scenarios. Charging at intermediate destinations exhibits a dual peak pattern—one in the early morning hours and another in the afternoon—whereas home-based charging shows a pronounced peak during evening return hours and a secondary peak in the early afternoon, corresponding to a decline in charging activity at other destinations. Power distributions, as expected, sensibly differ from one scenario to the other, conditional to different assumptions of private and public recharge availability and characteristics. Full article

Urban park accessibility is often planned with fixed service radii, that is, circular walking catchments around each park defined by a maximum walking distance of about 1500 m, roughly a 15–20 min walk in this study, yet real visitation is uneven and dynamic, leaving persistent gaps between normative coverage and where people actually originate. We propose an interpretable discovery-to-parameter workflow that converts behavior evidence into localized accessibility and actionable planning guidance. Monthly Origin–Destination (OD) and heatmap samples are fused to construct visitation intensity on a 200 m grid and derive empirical park service boundaries. Multiscale Geographically Weighted Regression (MGWR) then quantifies spatial heterogeneity, and its local coefficients are embedded into the enhanced two-step floating catchment area (E2SFCA) model as location-specific supply weights and distance-decay bandwidths. Compared with network isochrones and uncalibrated E2SFCA, the MGWR–E2SFCA achieves higher Jaccard overlap and lower population-weighted error, while maintaining balanced coverage–precision across districts and day types. A $\Delta$-surface lens decomposes gains into corridor correction and envelope contraction, revealing where conventional radii over- or under-serve residents. We further demonstrate an event-sensitivity switch, in which temporary adjustments of demand and decay parameters can accommodate short-term inflows during events such as festivals without contaminating the planning baseline. Together, the framework offers a transparent toolset for diagnosing mismatches between normative standards and observed use, prioritizing upgrades in under-served neighborhoods, and stress-testing park systems under recurring demand shocks. For land planning, it pinpoints where barriers to access should be reduced and where targeted connectivity improvements, public realm upgrades, and park capacity interventions can most effectively improve urban park accessibility. Full article