Search Results (1,663)

Urbanization, characterized by population growth and socioeconomic development, is a major driving factor of land use land cover (LULC) change. A spatio-temporal understanding of land cover change is crucial, as it provides essential insights into the pattern of urban development. This study conducted a longitudinal analysis of LULC change in order to evaluate the tradeoffs of urban growth and sustainability challenges in the Himalayan region. Landsat time-series satellite imagery from 1988 to 2024 were analyzed for two major cities in Nepal—Kathmandu metropolitan city (KMC) and Pokhara metropolitan city (PMC). The LULC classification was conducted using a machine learning support vector machine (SVM) approach. For this study period, our analysis showed that KMC and PMC witnessed urban growth of over 400% and 250%, respectively. In the next step, LULC change and urban expansion patterns were predicted based on the urban development indicator using the Cellular Automata Markov chain (CA-Markov) model for the years 2040 and 2056. Based on the CA-Markov chain analysis, the projected expansion areas of the urban area for the two future years are 282.39 km² and 337.37 km² for Kathmandu, and 93.17 km² and 114.15 km² for PMC, respectively. The model was verified using several Kappa variables (K-location, K-standard, and K-no). Based on the LULC trends, the majority of urban expansion in both the study areas has occurred at the expense of prime farmlands, which raises grave concern over the sustainability of the food supply to feed an ever-increasing urban population. This haphazard urban sprawl poses a significant challenge for future planning and highlights the urgent need for effective strategies to ensure sustainable urban growth, especially in restoring local food supply to alleviate over-reliance on long-distance transport of agro-produce in high-altitude mountain regions. The alternative planning of sustainable urban growth could involve adequate consideration for urban farming and community gardening as an integral part of the urban fabric, both at the household and city infrastructure levels. Full article

As China’s urbanization deepens, the spatial structure of residential areas and land use patterns has undergone profound transformations, with the differentiation of housing prices emerging as a key indicator of urban spatial dynamics and socioeconomic stratification. This study examines the spatial and temporal evolution of residential housing prices in Qingdao’s main urban area over a 20-year period, using data from three representative years (2003, 2013, and 2023) to capture key stages of change. It employs Local Indicators of Spatial Association (LISA) spatial and temporal path and leap analyses, as well as Geographically and Temporally Weighted Regression (GTWR) modeling. The results show that Qingdao’s housing price patterns exhibit distinct spatiotemporal heterogeneity, characterized by multi-level transitions, leapfrog dynamics and strong spatial dependence. The urban center and coastal zones demonstrate positive synergistic growth, while some inland and peripheral areas show negative spatial coupling. Evident is the spatial restructuring from a monocentric to a polycentric pattern, driven by shifts in industrial layout, policy incentives, and transportation infrastructure. Key driving factors, such as community attributes, locational conditions, and amenity support, show differentiated impacts across regions and over time. Business agglomeration and educational resources are primary positive drivers in central districts, whereas natural environments and commercial density play a more complex role in peripheral areas. These findings provide empirical evidence to inform our understanding of housing market dynamics and offer insights into urban planning and the design of equitable policies in transitional urban systems. Full article

In the 21st century, the role of railway stations began to change, as they began to integrate various modes of transport to become multimodal transit hubs (MTHs). They are often part of urban transformation plans due to the vast and underutilized spaces associated with them. This paper aims to reveal the changes in urban functions within transformed city areas due to the development of MTHs, as well as within MTHs themselves, by utilizing a widely accepted theoretical concept: the “15-minute city”. All conclusions are drawn by analyzing MTHs in urban transformation areas of European metropolises: Barcelona, Berlin, and London. The research shows that areas previously designated only for one use—industry—become areas with diverse urban functions after urban transformation. The reduction in infrastructural areas has resulted in the concentration of urban functions within these areas. This concentration can be observed in the following two ways: urban diversity has increased, and urban functions occupy significantly larger areas than before the urban transformation. It has been established that MTHs are catalysts for comprehensive urban transformation, as indicated by economic investments largely directed toward the development of urban functions in their surrounding areas. Full article

Urban sprawl and suburbanization are reshaping peri-urban areas, challenging urban planning and community well-being. Our study investigates questions regarding the perception of Cultural Ecosystem Services (CES) and development preferences (DP) related to Green Infrastructure (GI) in Vác, Hungary, including how CES and DP indicators related to GI vary spatially; how they align with municipal DI designations; how they relate to sociodemographic factors; and how they are applicable to urban planning practices. We used PPGIS and structured interviews with 375 residents to collect over 4900 spatial data points in order to analyze how perceived values, development preferences, officially designated GI elements and sociodemographic characteristics, relate to each other. The results show that CES are strongly associated with GI elements, especially along the riverfront and in downtown areas. However, development preferences, especially congestion and safety concerns, were more dispersed, often located in outer residential areas and along transportation routes. Statistical analyses showed significant differences across age, marital status, and co-residence with children, influencing both CES perception and development preferences. Our study highlights the gap between official GI designations and community-valued spaces, emphasizing the importance of participatory planning and the integration of sociodemographic dimensions into planning practices in rapidly transforming suburban environments. Full article

Universities play a critical role in shaping sustainable mobility strategies, especially in urban contexts where the institutional transport system can influence environmental and social outcomes. This study integrates Environmental and Social Life Cycle Assessment (E-LCA and S-LCA) to evaluate the current university transport system from internal combustion engines, diesel, and compressed natural gas (CNG), focusing on the operation and maintenance phases. Also, it compares seven scenarios, including electric, renewable sources, and biodiesel technologies. Environmental impacts were assessed using the ReCiPe 2016 midpoint method, which considers the following impact categories: Global Warming Potential (GWP); Ozone Formation, Human Health (OfHh); Ozone Formation, Terrestrial Ecosystem (OfTe); Terrestrial Acidification (TA); and Fine Particulate Matter Formation (FPmf). The sensitivity analysis explores scenarios to assess the effects of technological transitions and alternative energy sources on the environmental performance. Social impacts are assessed through a Social Performance Index (SPI) and Aggregated Social Performance Index (ASPI), which aggregates indicators such as safety, travel cost, punctuality, accessibility, and inclusive design. Accessibility emerged as the lowest indicator (ranging from 0.61 to 0.67), highlighting opportunities for improvement. Our findings support decision-making processes for integrating sustainable transport strategies into a University Mobility Plan, emphasizing the importance of combining technical performance with social inclusivity. Full article

Diverse realities challenge the management capacity of public and private systems to ensure equitable quality and efficient access to resources, in line with the 2030 Agenda and the Sustainable Development Goals, which aim to close gaps in essential services and ensure quality of life. The reality in Spain, and more specifically in the Principality of Asturias, is that most resources are concentrated in urban areas rather than rural ones, partly due to the region’s geography. Background/Objectives: This study aimed to explore the perspectives of various stakeholders on the early childhood care system in the Principality of Asturias (Spain), with the purpose of analyzing the mechanisms and determinants involved in its functioning and identifying opportunities for improvement. Methods: A qualitative study was conducted using the theoretical framework of the National Institute on Minority Health and Health Disparities (NIMHD) as a conceptual basis. Semi-structured interviews were carried out with 24 participants selected based on their relationship with early childhood care systems, encompassing different levels of responsibility and operational roles. Data were analyzed using a phenomenological approach, employing inductive and deductive coding to identify recurring patterns and code co-occurrences within ATLAS.ti software. Conclusions: This study reveals major barriers to equitable early childhood intervention (ECI) in rural areas, such as geographic isolation, lack of specialists, long waiting times, and poor transport. Six key themes emerged, including the need for standardized system management, better family support, and digital tools like centralized electronic health records. Rural areas are directly limited regarding their access to services, highlighting the need for fair territorial planning and a holistic, inclusive care model. Improving coordination, accessibility, and technology is vital. Full article

Bratislava is currently experiencing massive development, and its developers are very active. As the city develops, the improvement of its public transport becomes increasingly crucial. Public transport (PT) must be ecological, economical, and accessible to all social groups of the population. Bratislava currently has the opportunity to change the modal split in favor of PT and thus end the decline that began in the early 1990s. Rail transport is an ecological type of PT incorporated into smart cities, contributing to city land use. The current PT rail track in Bratislava comprises tram and train infrastructure. Trains ensure the transportation of people from the municipalities surrounding Bratislava, while trams ensure the transportation of people within the city. Tram and train PT must be merged, as their integration could improve traveling times. Bratislava is suitable for the creation of a dual rail transport system covering the urbanized area. The goal of this article is to present a technical solution for a double-gauge system for operation, considering traffic engineering and planning to aid decision making. Considerable professional and expert work was undertaken, in contrast to the political administration’s “decision making”. Cases from Central Europe are presented. Full article

Electric vehicle (EV) charging stations play a pivotal role in the widespread adoption and integration of electric vehicles into mainstream transportation systems. While the effects of climate change and greenhouse gases are increasing worldwide, the transition to electric vehicles is of high importance in terms of both ecological and sustainability. EV charging stations serve as the backbone of this transition, providing essential infrastructure to support the charging needs of EV owners and facilitate the transition to electric vehicles. In this study, a MINLP mathematical model is developed for the multi-objective optimization of EVCS. For implementation, Istanbul’s European side and a large-scale synthetic case are addressed considering both current demand and estimations for low, medium, and high EV numbers by the Energy Market Regulatory Authority (EMRA) for 2030 and 2035. The primary aim is to minimize station numbers, capacity, waiting time, and station idle time while meeting the demand. During the solvation of the mathematical model, both present demand and future EV usage forecasts are taken into consideration. This involves simulating different scenarios using EMRA’s 2030 and 2035 estimates and determining the optimal locations and capacities for charging stations for each demand level. Efficiencies in different scenarios were evaluated and the created mathematical model provides to optimize EV charging stations in multiple ways, there will be savings in total cost and labor force. The findings of the study will provide a valuable guide to the EV charging station infrastructure planning of the highways, regions, and urban areas to be selected in possible studies. The multi-directional optimization model addressed in this study will support decision-makers and industry experts in making informed decisions towards the sustainable and efficient development of EV charging infrastructure. Full article

This study investigates urban heat vulnerabilities in Seville, Spain, using a multidimensional framework that integrates remote sensing, Space Syntax, and social vulnerability metrics. This research identifies Heat Boundaries (HBs), which are critical urban entities with elevated Land Surface Temperatures (LSTs) that act as barriers to adjacent vulnerable neighbourhoods, disrupting both physical and social continuity and environmental equity, and examines their relationship with the urban syntax and social vulnerability. The analysis spans two temporal scenarios: a Category 3 heatwave on 26 June 2023 and a normal summer day on 14 July 2024, incorporating both daytime and nighttime satellite-derived LST data (Landsat 9 and ECOSTRESS). The results reveal pronounced spatial disparities in thermal exposure. During the heatwave, peripheral zones recorded extreme LSTs exceeding 53 °C, while river-adjacent neighbourhoods recorded up to 7.28 °C less LST averages. In the non-heatwave scenario, LSTs for advantaged neighbourhoods close to the Guadalquivir River were 2.55 °C lower than vulnerable high-density zones and 3.77 °C lower than the peripheries. Nocturnal patterns showed a reversal, with central high-density districts retaining more heat than the peripheries. Correlation analyses indicate strong associations between LST and built-up intensity (NDBI) and a significant inverse correlation with vegetation cover (NDVI). Syntactic indicators revealed that higher Mean Depth values—indicative of spatial segregation—correspond with elevated thermal stress, particularly during nighttime and heatwave scenarios. HBs occupy 17% of the city, predominantly composed of barren land (42%), industrial zones (30%), and transportation infrastructure (28%), and often border areas with high social vulnerability. This study underscores the critical role of spatial configuration in shaping heat exposure and advocates for targeted climate adaptation measures, such as HB rehabilitation, greening interventions, and Connectivity-based design. It also presents preliminary insights for future deep learning applications to automate HB detection and support predictive urban heat resilience planning. Full article

Critical infrastructure, such as transport networks, energy facilities, and urban installations, is increasingly vulnerable to natural hazards and climate change. Remote sensing technologies, namely satellite imagery, offer solutions for monitoring, evaluating, and enhancing the resilience of these vital assets. This paper explores how applications based on synthetic aperture radar (SAR) and optical satellite imagery contribute to the protection of critical infrastructure by enabling near real-time monitoring and early detection of natural hazards for actionable insights across various European critical infrastructure sectors. Case studies demonstrate the integration of remote sensing data into geographic information systems (GISs) for promoting situational awareness, risk assessment, and predictive modeling of natural disasters. These include floods, landslides, wildfires, and earthquakes. Accordingly, this study underlines the role of remote sensing in supporting long-term infrastructure planning and climate adaptation strategies. The presented work supports the goals of the European Union (EU-HORIZON)-sponsored ATLANTIS project, which focuses on strengthening the resilience of critical EU infrastructures by providing authorities and civil protection services with effective tools for managing natural hazards. Full article

Meeting the European Green Deal’s goal of climate neutrality by 2050 calls for a 90 percent decrease in emissions from the transportation sector. Thus, there is need to accelerate the shift to more sustainable mobility for integrated and smarter multimodal and intermodal mobility. In European countries, more than 70% of the inhabitants live in metropolitan areas. Achieving low-carbon and more sustainable mobility is important to ensuring sustainable urban infrastructure. However, current mobility planning frameworks do not consider the key factors and strategies that encourage residents to choose sustainable transport modes. Hence, there is a need to identify the most efficient actions that should be employed either in the short or long term to achieve accessible, safe, cost-effective, and green transport systems specifically through the development of sustainable public transportation. Moreover, a paradigm shift is needed to explore the synergy between transportation and its relationship to the city. Accordingly, this article presents an action plan as an approach to assess key strategies needed to foster sustainable and smart mobility planning and design by deploying effective strategies and design solutions that support different green means of transportation for smart urban development. Qualitative data on sustainable mobility planning and design strategies was collected via secondary sources from the literature, and descriptive data analysis was carried out. Findings from this study identify internal and external factors required to promote sustainable multimodal and intermodal mobility based on the city’s transport policies and actions. Implications from this study provide a use case for the technological requirements required for electric mobility planning, design, and system operation for the actualization of sustainable public transportation to improve smart urban development. Full article

As cities grow, they increase in complexity, requiring the effective use of land resources. Cycling is generally regarded as an alternative transport mode to support the development of the cities of tomorrow. In response to urbanization, in many cities worldwide, a common concern associated with investing in cycling networks is the resulting use after such investment. This study uses a continuous longitudinal dataset of daily cycling counts from January 2018 to June 2024 to assess bicycle volumes across three of New Zealand’s largest cities. The results reveal that the relationship between distance and cycle count is not uniform across space, with some areas showing a negative effect between distance and cycling, and others showing a positive one. A global OLS model hides these complexities, as shown in the geographically weighted regression (GWR) model. The coefficients for distance (−0.49) and precipitation (−95.23) in the global OLS are higher, and do not reveal the non-uniformity between cities, wheras themultiple GWR coefficients for distance range between −0.57 and −0.47 and precipitation between −33.47 and −97.63. The results reveal that cycling volume demonstrates lower sensitivity to changes in distance compared to variations in weather conditions. At the city level, there are notable intercity differences in sensitivity. The variability in the coefficients across locations suggests that, although distance and precipitation have general effects, local factors, such as infrastructure quality, topography, weather adaptation measures, and cultural attitudes toward cycling, play a critical role in modulating these relationships. The findings highlight the complexity of spatial interactions and emphasize the need for localized interventions when planning cycling networks. Full article

Sustainability has an ever-increasing importance in our lives, mainly due to climate changes, finite resources, and a growing population, where each of us is called to make a change. Although climate change is a global phenomenon, our individual choices can make the difference. The transportation sector is one of the largest contributors to global carbon emissions, making the transition toward sustainable mobility a critical priority. The adoption of electric vehicles is widely recognized as a key solution to reduce the environmental impact of transportation. However, their widespread acceptance depends on various technological, behavioral, and economical factors. Within this research we use as an artifact the CO₂ Emission Management Gauge (CEMG) devices to better understand how the manufacturers, with integrated features on vehicles, could significantly enhance sales and drive the movement towards electric vehicle adoption. This study proposes an innovative new theoretical model based on Task-Technology Fit, Technology Acceptance, and the Theory of Planned Behavior to understand the main drivers that may foster electric vehicle adoption, tested in a quantitative study with structural equation modelling (SEM), and conducted in a South European country. Our findings, not without some limitations, reveal that while technological innovations like CEMG provide consumers with valuable transparency regarding emissions, its influence on the intention of adoption is dependent on the attitude towards electric vehicles and subjective norm. Our results also support the influence of task-technology fit on perceived usefulness and perceived ease-of-use, the influence of perceived usefulness on consumer attitude towards electric vehicles, and the influence of perceived ease-of-use on perceived usefulness. A challenge is also presented within our work to expand CEMG usage in the future to more intrinsic urban contexts, combined with smart city algorithms, collecting and proving CO₂ emission information to citizens in locations such as traffic lights, illumination posts, streets, and public areas, allowing the needed information to better manage the city’s quality of air and traffic. Full article

Urban multimodal travel trajectory prediction is a core challenge in Intelligent Transportation Systems (ITSs). It requires modeling both spatiotemporal dependencies and dynamic interactions among different travel modes such as taxi, bike-sharing, and buses. To address the limitations of existing methods in capturing these diverse trajectory characteristics, we propose a spatiotemporal multi-model ensemble framework, which is an ensemble model called GLEN (GCN and LSTM Ensemble Network). Firstly, the trajectory feature adaptive driven model selection mechanism classifies trajectories into dynamic travel and fixed-route scenarios. Secondly, we use a Graph Convolutional Network (GCN) to capture dynamic travel patterns and Long Short-Term Memory (LSTM) network to model fixed-route patterns. Subsequently the outputs of these models are dynamically weighted, integrated, and fused over a spatiotemporal grid to produce accurate forecasts of urban total traffic flow at multiple future time steps. Finally, experimental validation using Beijing’s Chaoyang district datasets demonstrates that our framework effectively captures spatiotemporal and interactive characteristics between multimodal travel trajectories and outperforms mainstream baselines, thereby offering robust support for urban traffic management and planning. Full article

This study presents a method for evaluating and optimizing the service network capacity of Urban Rail Transit Networks (URTNs) based on existing infrastructure conditions. By integrating passenger route choice behavior, the method assesses the network’s potential maximum capacity through the actual utilization rates of throughput capacity across various sections and routes. Furthermore, by incorporating route design and Transit-Oriented Development (TOD) strategies, the approach achieves a dual enhancement of network capacity and service quality. An optimization model was developed to maximize the network capacity while minimizing passenger travel costs, and it was solved using Adaptive Large Neighborhood Search (ALNS) and the Method of Successive Averages (MSA) algorithms. A case study of the Chongqing URTN demonstrated the model’s effectiveness. The results indicate that integrating route design and TOD strategies can significantly enhance the service capacity of urban rail networks. This method will assist decision-makers in understanding the current utilization status of the network’s capacity and evaluating its potential capacity. During TOD planning at stations, it simultaneously assesses changes in network capacity, thereby achieving a balance between land development, passenger demand, and the transportation system. Full article