Search Results (694)

Continuous progress in the sector of additive manufacturing has drastically aided the design and fabrication of urban furniture, offering high levels of customization and adaptability. This work looks into the potential of 3D printing to transform urban public spaces by allowing for the creation of functional, aesthetically pleasing, and user-centered furniture solutions. Through additive manufacturing processes, urban furniture can be tailored to meet the unique needs of diverse communities, allowing for the extended usage of sustainable materials, modular designs, and smart technologies. The flexibility of 3D printing also promotes the fabrication of complex, intricate designs that would be difficult or cost-prohibitive using traditional methods. Additionally, 3D-printed furniture can be optimized for specific environmental conditions, providing solutions that enhance accessibility, improve comfort, and promote inclusivity. The various advantages of 3D-printed urban furniture are examined, including reduced material waste and the ability to rapidly prototype and iterate designs alongside the potential for on-demand, local production. By embedding sensors and IoT devices, 3D-printed furniture can also contribute to the development of smart cities, providing real-time data for urban management and improving the overall user experience. As cities continue to encourage and adopt sustainable and innovative solutions, 3D printing is believed to play a crucial role in future urban infrastructure planning. Full article

Urban public transportation systems face increasing pressure from shifting travel patterns, rising peak-hour demand, and the need for equitable and resilient service delivery. While complex network theory has been widely applied to analyze transit systems, limited attention has been paid to behavioral segmentation within such networks. This study introduces a frequency-based framework that differentiates high-frequency (HF) and low-frequency (LF) passengers to examine how distinct user groups shape network structure, congestion vulnerability, and robustness. Using over 20 million smart-card records from Beijing’s multimodal transit system, we construct and analyze directed weighted networks for HF and LF users, integrating topological metrics, temporal comparisons, and community detection. Results reveal that HF networks are densely connected but structurally fragile, exhibiting lower modularity and significantly greater efficiency loss during peak periods. In contrast, LF networks are more spatially dispersed yet resilient, maintaining stronger intracommunity stability. Peak-hour simulation shows a 70% drop in efficiency and a 99% decrease in clustering, with HF networks experiencing higher vulnerability. Based on these findings, we propose differentiated policy strategies for each user group and outline a future optimization framework constrained by budget and equity considerations. This study contributes a scalable, data-driven approach to integrating passenger behavior with network science, offering actionable insights for resilient and inclusive transit planning. Full article

This study explores the migration intentions of university students—representing the potential creative class—in Erzurum, a medium-sized city in eastern Turkey experiencing shrinkage. Within the theoretical framework of shrinking cities, it investigates how economic, social, physical, and personal factors influence students’ post-graduation stay or leave decisions. Survey data from 742 Architecture and Fine Arts students at Atatürk University were analyzed using factor analysis, logistic regression, and correlation to identify key migration drivers. Findings reveal that, in addition to economic concerns such as limited job opportunities and low income, personal development opportunities and social engagement also play a decisive role. In particular, the perception of limited chances for skill enhancement and the belief that Erzurum is not a good place to meet people emerged as the strongest predictors of migration intentions. These results suggest that members of the creative class are influenced not only by economic incentives but also by broader urban experiences related to self-growth and social connectivity. This study highlights spatial inequalities in access to cultural, educational, and social infrastructure, raising important questions about spatial justice in shrinking urban contexts. This paper contributes to the literature on shrinking cities by highlighting creative youth in mid-sized Global South cities. It suggests smart shrinkage strategies focused on creative sector development, improved quality of life, and inclusive planning to retain young talent and support sustainable urban revitalization. Full article

This study presents an integrated geospatial framework for assessing the risk to high-speed railway (HSR) infrastructure, combining a persistent scatterer interferometric synthetic aperture radar (PS-InSAR) analysis with multi-criteria decision-making in a geographic information system (GIS) environment. Focusing on the Honam HSR corridor in South Korea, the model incorporates both maximum ground deformation and subsidence velocity to construct a dynamic hazard index. Social vulnerability is quantified using five demographic and infrastructural indicators, and a two-stage analytic hierarchy process (AHP) is applied with dependency correction to mitigate inter-variable redundancy. The resulting high-resolution risk maps highlight spatial mismatches between geotechnical hazards and social exposure, revealing vulnerable segments in Gongju and Iksan that require prioritized maintenance and mitigation. The framework also addresses data limitations by interpolating groundwater levels and estimating train speed using spatial techniques. Designed to be scalable and transferable, this methodology offers a practical decision-support tool for infrastructure managers and policymakers aiming to enhance the resilience of linear transport systems. Full article

This study proposes an intelligent patrol planning framework that leverages reinforcement learning, spatiotemporal crime forecasting, and simulated sensor telemetry to optimize autonomous vehicle (AV) navigation in urban environments. Crime incidents from Washington DC (2024–2025) and Seattle (2008–2024) are modeled as a dynamic spatiotemporal graph, capturing the evolving intensity and distribution of criminal activity across neighborhoods and time windows. The agent’s state space incorporates synthetic AV sensor inputs—including fuel level, visual anomaly detection, and threat signals—to reflect real-world operational constraints. We evaluate and compare three learning strategies: Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Proximal Policy Optimization (PPO). Experimental results show that DDQN outperforms DQN in convergence speed and reward accumulation, while PPO demonstrates greater adaptability in sensor-rich, high-noise conditions. Real-map simulations and hourly risk heatmaps validate the effectiveness of our approach, highlighting its potential to inform scalable, data-driven patrol strategies in next-generation smart cities. Full article

One of the fundamental goals of contemporary mobility is to optimize transport processes in urban areas. The solution in this area seems to be the implementation of the idea of sustainable transport systems based on the Smart City concept. The article presents a case study—an assessment of the possibilities of changing mobility habits based on the idea of sustainable urban transport, taking into account the criterion of energy consumption of individual means of transport. The analyses are based on a comparison of selected means of transport occurring in the urban environment according to several key parameters for the optimization and efficiency of transport processes, i.e., cost, time, travel comfort, and impact on the natural environment, while simultaneously linking them to the criterion of energy consumption of individual means of transport. The analyzed parameters currently constitute the most important group of challenges in the area of shaping and planning optimal and sustainable urban transport. The presented research was used to indicate the connections between various areas of optimization of the transport process and the energy efficiency of individual modes of transport. Analyses have shown that the least time-consuming process of urban mobility is associated with the highest level of CO₂ emissions and, at the same time, the highest level of energy efficiency. However, combining public transport with other means of transport can meet most of the transport expectations of city residents, also in terms of energy optimization. The research results presented in the article can contribute to the creation of a strategy for the development of the transport network based on the postulates of increasing the optimization and efficiency of individual means of transport in urban areas. At the same time, recognizing the criterion of energy intensity of means of transport as leading in the development of sustainable urban mobility. Thus, confirming the important role of existing transport systems in the process of shaping and planning sustainable urban mobility in accordance with the idea of Smart City. Full article

Urban areas are facing increasing traffic congestion, pollution, and infrastructure strain. Traditional urban transportation systems are often fragmented. They require users to plan, pay, and travel across multiple disconnected services. Mobility-as-a-Service (MaaS) integrates these services into a single digital platform, simplifying access and improving the user experience. This review critically examines the role of MaaS in fostering sustainable mobility ecosystems. MaaS aims to enhance user-friendliness, service variety, and sustainability by adopting a customer-centric approach to transportation. The findings reveal that successful MaaS systems consistently align with multimodal transport infrastructure, equitable access policies, and strong public-private partnerships. MaaS enhances the management of routes and traffic, effectively mitigating delays and congestion while concurrently reducing energy consumption and fuel usage. In this study, the authors examine MaaS as a new mobility paradigm for a sustainable transportation system in smart cities, observing the challenges and opportunities associated with its implementation. To assess the environmental impact, a sustainability index is calculated based on the use of different modes of transportation. Significant findings indicate that MaaS systems are proliferating in both quantity and complexity, increasingly integrating capabilities such as real-time multimodal planning, dynamic pricing, and personalized user profiles. Full article

Consumer-accessible and user-friendly smart products such as unmanned aerial vehicles (UAVs), or drones, have become widely used, adaptable, and acceptable devices to observe, assess, measure, and explore urban and natural environments. A drone’s relatively low cost and flexibility in the level of expertise required to operate it has enabled users from novice to industry professionals to adapt a malleable technology to various disciplines. This review examines the academic literature and maps how drones are currently being used in 93 rural and regional city councils in New South Wales, Australia. Through a systematic review of the academic literature and scrutiny of current drone use in these councils using publicly available information found on council websites, findings reveal potential uses of drone technology for local governments who want to engage with smart technology devices. We looked at how drones were being used in the management of the council’s environment; health and safety initiatives; infrastructure; planning; social and community programmes; and waste and recycling. These findings suggest that drone technology is increasingly being utilised in rural and regional areas. While the focus is on rural and regional New South Wales, a review of the academic literature and local council websites provides a snapshot of drone use examples that holds global relevance for local councils in urban and remote areas seeking to incorporate drone technology into their daily practice of city, town, or region governance. Full article

The integration of autonomous vehicles (AVs) into urban transportation systems has significant potential to enhance traffic efficiency and reduce environmental impacts. This study evaluates the impact of different AV penetration scenarios (0%, 10%, 30%, 50%) on traffic performance and carbon emissions along Prince Mohammed bin Salman bin Abdulaziz Road in Riyadh, Saudi Arabia. Using microscopic simulation (SUMO) based on real-world datasets, we assess key performance indicators such as travel time, stop frequency, speed, and CO₂ emissions. Results indicate notable improvements with increasing AV deployment, including up to 25.5% reduced travel time and 14.6% lower emissions at 50% AV penetration. Coordinated AV behavior was approximated using adjusted simulation parameters and Python-based APIs, effectively modeling vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-network (V2N) communications. These findings highlight the benefits of harmonized AV–human vehicle interactions, providing a scalable and data-driven framework applicable to smart urban mobility planning. Full article

This study explores digital inequality as a socio-spatial phenomenon within the context of smart inclusion, focusing on the Regional Unit of Xanthi, Greece—a region marked by ethno-cultural diversity and pronounced urban–rural contrasts. Using a mixed-methods design, this research integrates secondary quantitative data with qualitative insights from semi-structured interviews, aiming to uncover how spatial, demographic, and cultural variables shape digital engagement. Geographic Information System (GIS) tools are employed to map disparities in internet access and ICT infrastructure, revealing significant gaps linked to geography, education, and economic status. The findings demonstrate that digital inequality is particularly acute in rural, minority, and economically marginalized communities, where limited infrastructure intersects with low digital literacy and socio-economic disadvantage. Interview data further illuminate how residents navigate exclusion, emphasizing generational divides, perceptions of technology, and place-based constraints. By bridging spatial analysis with lived experience, this study advances the conceptualization of digitally inclusive smart regions. It offers policy-relevant insights into how territorial inequality undermines the goals of smart development and proposes context-sensitive interventions to promote equitable digital participation. The case of Xanthi underscores the importance of integrating spatial justice into smart city and regional planning agendas. Full article

Large-scale events, such as festivals and public gatherings, pose serious problems in terms of traffic congestion, slow transaction processing, and security risks to transportation planning. This study proposes a blockchain-based solution for enhancing the efficiency and security of intelligent transport systems (ITS) by utilizing state channels and rollups. Throughput is optimized, enabling transaction speeds of 800 to 3500 transactions per second (TPS) and delays of 5 to 1.5 s. Prevent data tampering, strengthen security, and enhance data integrity from 89% to 99.999%, as well as encryption efficacy from 90% to 98%. Furthermore, our system reduces congestion, optimizes vehicle movement, and shares real-time, secure data with stakeholders. Practical applications include fast and safe road toll payments, faster public transit ticketing, improved emergency response coordination, and enhanced urban mobility. The decentralized blockchain helps maintain trust among users, transportation authorities, and event organizers. Our approach extends beyond large-scale events and proposes a path toward ubiquitous, Artificial Intelligence (AI)-driven decision-making in a broader urban transit network, informing future operations in dynamic traffic optimization. This study demonstrates the potential of blockchain to create more intelligent, more secure, and scalable transportation systems, which will help reduce urban mobility inefficiencies and contribute to the development of resilient smart cities. Full article

Japan’s 8.5 million vacant homes (Akiyas) represent a paradox of scarcity amid surplus: while rural depopulation leaves properties abandoned, housing shortages and bureaucratic inefficiencies hinder their reuse. This study proposes AKI2ALL, an AI-blockchain framework designed to automate the circular repurposing of Akiyas into ten high-value community assets—guesthouses, co-working spaces, pop-up retail and logistics hubs, urban farming hubs, disaster relief housing, parking lots, elderly daycare centers, exhibition spaces, places for food and beverages, and company offices—through smart contracts and data-driven workflows. By integrating circular economy principles with decentralized technology, AKI2ALL streamlines property transitions, tax validation, and administrative processes, reducing operational costs while preserving embodied carbon in existing structures. Municipalities list properties, owners select uses, and AI optimizes assignments based on real-time demand. This work bridges gaps in digital construction governance, proving that automating trust and accountability can transform systemic inefficiencies into opportunities for community-led, low-carbon regeneration, highlighting its potential as a scalable model for global vacant property reuse. Full article

Efficient detection of road surface defects is vital for timely maintenance and traffic safety. This study introduces a novel AI-powered web framework, TriRoad AI, that integrates multiple versions of the You Only Look Once (YOLO) object detection algorithms—specifically YOLOv8 and YOLOv11—for automated detection of potholes and cracks. A user-friendly browser interface was developed to enable real-time image analysis, confidence-based prediction filtering, and severity-based geolocation mapping using OpenStreetMap. Experimental evaluation was conducted using two datasets: one from online sources and another from field-collected images in Ankara, Turkey. YOLOv8 achieved a mean accuracy of 88.43% on internet-sourced images, while YOLOv11-B demonstrated higher robustness in challenging field environments with a detection accuracy of 46.15%, and YOLOv8 followed closely with 44.92% on mixed field images. The Gemini AI model, although highly effective in controlled environments (97.64% detection accuracy), exhibited a significant performance drop of up to 80% in complex field scenarios, with its accuracy falling to 18.50%. The proposed platform’s uniqueness lies in its fully integrated, browser-based design, requiring no device-specific installation, and its incorporation of severity classification with interactive geospatial visualization. These contributions address current gaps in generalization, accessibility, and practical deployment, offering a scalable solution for smart infrastructure monitoring and preventive maintenance planning in urban environments. Full article

This study aimed to investigate the future trajectories of last-mile delivery (LMD), and their implications for sustainable urban logistics and smart city planning. Through a Delphi-based scenario analysis targeting the year 2030, this research draws on inputs from a two-round Delphi study with 52 experts representing logistics, academia, and government. Four key thematic areas were explored: consumer demand and behavior, emerging delivery technologies, innovative delivery services, and regulatory frameworks. The projections were structured using fuzzy c-means clustering, and analyzed through the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT), supporting a systemic understanding of innovation adoption in urban logistics systems. The findings offer strategic insights for municipal planners, policymakers, logistics service providers, and e-commerce stakeholders, helping align infrastructure development and regulatory planning with the evolving needs of last-mile logistics. This approach contributes to advancing resilient, low-emission, and inclusive smart city ecosystems that align with global sustainability goals, particularly those outlined in the UN 2030 Agenda for Sustainable Development. Full article

Activity space offers a valuable perspective for analyzing urban travel behavior and evaluating the performance of transportation systems in increasingly complex urban environments. However, the research on measuring activity spaces in multimodal transportation contexts remains limited. This study investigates multimodal transportation activity spaces in Hangzhou using 2023 smart card data. Multimodal travel chains are extracted, and residents’ activity spaces are quantified using 95% confidence ellipses. By applying the XGBoost and GeoShapley models, this study reveals the nonlinear effects and geospatial heterogeneity in how built environment and socioeconomic factors influence activity spaces. The key findings show that the distance to the nearest metro station, commercial POIs, and GDP significantly shape activity spaces through nonlinear relationships. Moreover, the interaction between the distance to the nearest metro station and geographical location generates pronounced geospatial effects. The results highlight the importance of multimodal integration in urban transport planning and provide empirical insights for enhancing system efficiency and sustainability. Full article