Search Results (1,626)

The integration of heterogeneous spatio-temporal datasets presents a critical challenge in geospatial data science, particularly when combining large-scale, passively collected “big” data with precise but sparse “small” data. In this study, we propose a novel framework—Multi-Channel Spatio-Temporal Data Fusion (MCST-DF)—that leverages transformer-based deep learning to fuse these data sources for accurate network flow estimation. Our approach introduces a Residual Spatio-Temporal Transformer Network (RSTTNet), equipped with a layered attention mechanism and multi-scale embedding architecture to capture both local and global dependencies across space and time. We evaluate the framework using real-world mobile sensing and loop detector data from the London road network, demonstrating over 89% prediction accuracy and outperforming several benchmark deep learning models. This work provides a generalisable solution for spatio-temporal fusion of diverse geospatial data sources and has direct relevance to smart mobility, urban infrastructure monitoring, and the development of spatially informed AI systems. Full article

This study presents an AI-enhanced pedestrian simulation model for subway stations, combining the Social Force Model (SFM) with LiDAR trajectory data from Samseong Station in Seoul. To reflect time-dependent behavioral differences, RMSProp-based optimization is performed separately for the morning peak, leisure hours, and evening peak, yielding time-specific parameter sets. Compared to baseline models with static parameters, the proposed method reduces prediction errors (MSE) by 50.1% to 84.7%. The model integrates adaptive learning rates, mini-batch training, and L2 regularization, enabling robust convergence and generalization across varied pedestrian densities. Its accuracy and modular design support real-world applications such as pre-construction design testing, post-opening monitoring, and capacity planning. The framework also contributes to Sustainable Urban Mobility Plans (SUMPs) by enabling predictive, data-driven evaluation of pedestrian flow dynamics in complex station environments. Full article

Henri Lefebvre’s “right to the city” has rarely been examined through an intersectional feminist lens, leaving unnoticed the uneven burdens that urban design and policy place on women. This article bridges that gap by combining constitutional analysis, survey data (n = 736), in-depth interviews, and participatory observation to assess how Quito’s public spaces affect women’s safety and mobility. Quantitative results show that 81% of respondents endured sexual or offensive remarks, 69.8% endured obscene gestures, and 38% endured severe harassment in the month before the survey; 43% of these incidents occurred only days or weeks beforehand, underscoring their routine nature. Qualitative narratives reveal behavioral adaptations—altered routes, self-policing dress codes, and distrust of authorities—and identify poorly lit corridors and weak institutional presence as spatial amplifiers of violence. Analysis of Quito’s “Safe City” program exposes a gulf between its ambitious rhetoric and its narrow, transport-centered implementation. We conclude that constitutional guarantees of participation, appropriation, and urban life will remain aspirational until urban planning mainstreams gender-sensitive design, secures intersectoral resources, and embeds women’s substantive participation throughout policy cycles. A feminist reimagining of Quito’s public realm is therefore indispensable to transform the right to the city from legal principle into lived reality. Full article

Traffic congestion poses a significant challenge to modern urban environments, impacting both driver satisfaction and road safety. This paper investigates the effectiveness of a smart traffic light system (STL), a solution developed under the Intelligent Transportation System (ITS) initiative by the Ministry of Works Malaysia, to address these issues in Malaysia. The system integrates a network of sensors, AI-enabled cameras, and Automatic Number Plate Recognition (ANPR) technology to gather real-time data on traffic volume and vehicle classification at congested intersections. This data is utilized to dynamically adjust traffic light timings, prioritizing traffic flow on heavily congested roads while maintaining safety standards. To evaluate the system’s performance, a comprehensive study was conducted at a selected intersection. Traffic patterns were automatically analyzed using camera systems, and the performance of the STL was compared to that of traditional traffic signal systems. The average travel time from the start to the end intersection was measured and compared. Preliminary findings indicate that the STL significantly reduces travel times and improves overall traffic flow at the intersection, with average travel time reductions ranging from 7.1% to 28.6%, depending on site-specific factors. While further research is necessary to quantify the full extent of the system’s impact, these initial results demonstrate the promising potential of STL technology to enhance urban mobility and more efficient and safer roadways by moving beyond traditional traffic signal functionalities. Full article

The air quality within urban public transport is a critical determinant of passenger health. In the crowded and poorly ventilated cabins of Almaty’s metro, buses, and trolleybuses, concentrations of CO₂ and PM_2.5 often accumulate, elevating the risk of respiratory and cardiovascular diseases. This study investigates the air quality along three of the city’s busiest transport corridors, analyzing how the concentrations of CO₂, PM_2.5, and PM₁₀, as well as the temperature and relative humidity, fluctuate with the passenger density and time of day. Continuous measurements were collected using the Tynys mobile IoT device, which was bench-calibrated against a commercial reference sensor. Several machine learning models (logistic regression, decision tree, XGBoost, and random forest) were trained on synchronized environmental and occupancy data, with the XGBoost model achieving the highest predictive accuracy at 91.25%. Our analysis confirms that passenger occupancy is the primary driver of in-cabin pollution and that these machine learning models effectively capture the nonlinear relationships among environmental variables. Since the surveyed routes serve Almaty’s most densely populated districts, improving the ventilation on these lines is of immediate importance to public health. Furthermore, the high-temporal-resolution data revealed short-term pollution spikes that correspond with peak ridership, advancing the current understanding of exposure risks in transit. These findings highlight the urgent need to combine real-time monitoring with ventilation upgrades. They also demonstrate the practical value of using low-cost IoT technologies and data-driven analytics to safeguard public health in urban mobility systems. Full article

Assessing a city’s level of smartness according to global indices is a relatively new area of investigation. It is useful in encouraging a rethinking of urban digital strategies, although the different approaches to global smart city rankings have been subject to criticism. This paper highlights the methodological features of constructing the Smart City Index (SCI) from the IMD (International Institute for Management Development) based on residents’ assessments, their satisfaction with electronic services, and their perception of the priority of urban infrastructure areas. The Central European cities of the Visegrad region (Prague/Czech Republic, Budapest/Hungary, Bratislava/Slovakia, Warsaw and Krakow/Poland) were chosen as the basis for an in-depth analysis. The architectonics, i.e., the internal system of constructing and calculating city rankings by SCI, is analyzed. A comparative analysis of the technology indicators (e-services) in five cities of the Visegrad region, presented in the SCI, showed the smart features of each city. The progressive and regressive trends in the dynamics of smartness in the cities in the Visegrad region were identified in five urban spheres indicated in the Index: Government, Activity, Health and Safety, Mobility, and Opportunities. This also made it possible to identify certain methodological gaps in the SCI in establishing interdependencies between the data on the residents’ perception of the priority of areas of life in a particular city and the residents’ level of satisfaction with electronic services. In particular, the structural indicators “Affordable housing” and “Green spaces” are not supported by e-services. This research aims to bridge this methodological gap by proposing a model for evaluating the e-service according to the degree of coverage of different spheres of life in the city. The application of the project, as well as cross-sectoral and systemic approaches, made it possible to develop basic models for assessing the value of e-services. These models can be implemented by municipalities to assess and monitor e-services, as well as to select IT projects and elaborate strategies for smart sustainable city development. Full article

Salinity intrusion is a growing threat to freshwater resources, particularly in low-lying coastal and estuarine regions, necessitating the development of effective early warning systems (EWS) to support timely mitigation. Although various water quality monitoring technologies exist, many face challenges related to long-term sustainability, ongoing maintenance, and accessibility for local users. This study introduces a novel hybrid real-time salinity intrusion early warning system that uniquely integrates fixed and portable monitoring technologies with strong community participation—an approach not yet widely applied in comparable urban-adjacent delta regions. Unlike traditional systems, this model emphasizes local ownership, flexible data collection, and system scalability in resource-constrained environments. This study presents a real-time salinity intrusion early warning system for Bang Kachao, Thailand, combining eight fixed monitoring stations and 20 portable salinity measurement devices. The system was developed in response to community needs, with local input guiding both station placement and the design of mobile measurement tools. By integrating fixed stations for continuous, high-resolution data collection with portable devices for flexible, on-demand monitoring, the system achieves comprehensive spatial coverage and adaptability. A core innovation lies in its emphasis on community participation, enabling villagers to actively engage in monitoring and decision-making. The use of IoT-based sensors, Remote Telemetry Units (RTUs), and cloud-based data platforms further enhances system reliability, efficiency, and accessibility. Automated alerts are issued when salinity thresholds are exceeded, supporting timely interventions. Field deployment and testing over a seven-month period confirmed the system’s effectiveness, with fixed stations achieving 90.5% accuracy and portable devices 88.7% accuracy in detecting salinity intrusions. These results underscore the feasibility and value of a hybrid, community-driven monitoring approach for protecting freshwater resources and building local resilience in vulnerable regions. Full article

Accurate prediction of drone motion within structured urban air corridors is essential for ensuring safe and efficient operations in Urban Air Mobility (UAM) systems. Although real-world Remote Identification (Remote ID) regulations require drones to broadcast critical flight information such as velocity, access to large-scale, high-quality broadcast data remains limited. To address this, this study leverages a Digital Twin (DT) framework to augment Remote ID spatio-temporal broadcasts, emulating the sensing environment of dense urban airspace. Using Remote ID data, we propose BiDGCNLLM, a hybrid prediction framework that integrates a Bidirectional Graph Convolutional Network (BiGCN) with Dynamic Edge Weighting and a reprogrammed Large Language Model (LLM, Qwen2.5–0.5B) to capture spatial dependencies and temporal patterns in drone speed trajectories. The model forecasts near-future speed variations in surrounding drones, supporting proactive conflict avoidance in constrained air corridors. Results from the AirSUMO co-simulation platform and a DT replica of the Cranfield University campus show that BiDGCNLLM outperforms state-of-the-art time series models in short-term velocity prediction. Compared to Transformer-LSTM, BiDGCNLLM marginally improves the $R^2$ by 11.59%. This study introduces the integration of LLMs into dynamic graph-based drone prediction. It shows the potential of Remote ID broadcasts to enable scalable, real-time airspace safety solutions in UAM. Full article

Understanding automated vehicle (AV) behavior in complex road environments and user attitudes in such contexts is critical for their safe and effective integration into smart cities. Despite growing deployment, limited public data exist on AV performance in construction zones; highly dynamic settings marked by irregular lane markings, shifting detours, and unpredictable human presence. This study investigates AV behavior in these conditions through qualitative, video-based analysis of user-documented experiences on YouTube, focusing on Tesla’s supervised Full Self-Driving (FSD) and Waymo systems. Spoken narration, captions, and subtitles were examined to evaluate AV perception, decision-making, control, and interaction with humans. Findings reveal that while AVs excel in structured tasks such as obstacle detection, lane tracking, and cautious speed control, they face challenges in interpreting temporary infrastructure, responding to unpredictable human actions, and navigating low-visibility environments. These limitations not only impact performance but also influence user trust and acceptance. The study underscores the need for continued technological refinement, improved infrastructure design, and user-informed deployment strategies. By addressing current shortcomings, this research offers critical insights into AV readiness for real-world conditions and contributes to safer, more adaptive urban mobility systems. Full article

Tiny Machine Learning (TinyML) extends edge AI capabilities to resource-constrained devices, offering a promising solution for real-time, low-power intelligence in smart cities. This review systematically analyzes 66 peer-reviewed studies from 2019 to 2024, covering applications across urban mobility, environmental monitoring, public safety, waste management, and infrastructure health. We examine hardware platforms and machine learning models, with particular attention to power-efficient deployment and data privacy. We review the approaches employed in published studies for deploying machine learning models on resource-constrained hardware, emphasizing the most commonly used communication technologies—while noting the limited uptake of low-power options such as Low Power Wide Area Networks (LPWANs). We also discuss hardware–software co-design strategies that enable sustainable operation. Furthermore, we evaluate the alignment of these deployments with the United Nations Sustainable Development Goals (SDGs), highlighting both their contributions and existing gaps in current practices. This review identifies recurring technical patterns, methodological challenges, and underexplored opportunities, particularly in the areas of hardware provisioning, usage of inherent privacy benefits in relevant applications, communication technologies, and dataset practices, offering a roadmap for future TinyML research and deployment in smart urban systems. Among the 66 studies examined, 29 focused on mobility and transportation, 17 on public safety, 10 on environmental sensing, 6 on waste management, and 4 on infrastructure monitoring. TinyML was deployed on constrained microcontrollers in 32 studies, while 36 used optimized models for resource-limited environments. Energy harvesting, primarily solar, was featured in 6 studies, and low-power communication networks were used in 5. Public datasets were used in 27 studies, custom datasets in 24, and the remainder relied on hybrid or simulated data. Only one study explicitly referenced SDGs, and 13 studies considered privacy in their system design. Full article

Promoting behavioral change in mobility is essential for sustainable urban development. This study evaluates the effectiveness of gamified challenges in fostering sustainable travel behaviors among high school students and teachers within the High School Challenge (HSC) 2024 campaign in Lecco, Italy. Over a 13-week period, participants tracked their commuting habits via gamified mobile application, Play&Go, that awarded points for sustainable mobility choices and introduced weekly challenges. Using behavioral (GPS-based tracking) and self-report data, we assessed the influence of challenge types, player characteristics (HEXAD Player Types, Big Five traits), and user experience evaluations on participation, retention, and behavior change. The results show that challenges, particularly those based on walking distances and framed as intra-team goals, significantly enhanced user engagement and contributed to improved mobility behaviors during participants’ free time. Compared to the 2023 edition without challenges, the 2024 campaign achieved better retention. HEXAD Player Types were more predictive of user appreciation than Personality Traits, though these effects were more evident in subjective evaluations than actual behavior. Overall, findings highlight the importance of tailoring gamified interventions to users’ motivational profiles and structuring challenges around SMART principles. This study contributes to the design of behaviorally informed, scalable solutions for sustainable mobility transitions. Full article

This study compares high-frequency mobile observation data collected in the same area of Kunming under two different meteorological conditions—15 January 2020, and 8 January 2023—to analyze changes in the micro-scale urban thermal environment. Vehicle-mounted temperature and humidity sensors, combined with GPS tracking, were used to conduct real-time, high-resolution data collection across various urban functional areas. The results show that in the two tests, the maximum temperature differences were 10.4 °C and 16.5 °C, respectively, and the maximum standard deviations were 0.34 °C and 2.43 °C, indicating a significant intensification in thermal fluctuations. Industrial and commercial zones experienced the most pronounced cooling, while green spaces and water bodies exhibited greater thermal stability. The study reveals the sensitivity of densely built-up areas to cold extremes and highlights the important role of green infrastructure in mitigating urban thermal instability. Furthermore, this research demonstrates the advantages of mobile observation over conventional remote sensing methods in capturing fine-scale, dynamic thermal distributions, offering valuable insights for climate-resilient urban planning. Full article

Urban road traffic congestion poses significant challenges to sustainable mobility in smart cities. Traditional traffic light systems, reliant on static or semi-fixed timers, fail to adapt to dynamic traffic conditions, exacerbating congestion and limiting inclusivity. To address these limitations, this paper proposes H-ATLM (a hybrid adaptive traffic lights management), a system utilizing the deep deterministic policy gradient (DDPG) reinforcement learning algorithm to optimize traffic light timings dynamically based on real-time data. The system integrates advanced sensing technologies, such as cameras and inductive loops, to monitor traffic conditions and adaptively adjust signal phases. Experimental results demonstrate significant improvements, including reductions in congestion (up to 50%), increases in throughput (up to 149%), and decreases in clearance times (up to 84%). These findings open the door for integrating accessibility-focused features such as adaptive signaling for accessible vehicles, dedicated lanes for paratransit services, and prioritized traffic flows for inclusive mobility. Full article

Urban traffic congestion poses persistent challenges to mobility, public safety, and governance efficiency in metropolitan areas. This study proposes an intelligent traffic flow forecasting framework based on an extended Long Short-Term Memory (xLSTM) model, specifically designed for real-time congestion prediction and proactive police dispatch support. Utilizing a real-world dataset collected from over 300 vehicle detector (VD) sensors, the proposed model integrates vehicle volume, speed, and lane occupancy data at five-minute intervals. Methodologically, the xLSTM model incorporates matrix-based memory cells and exponential gating mechanisms to enhance spatio-temporal learning capabilities. Model performance is evaluated using multiple metrics, including congestion classification accuracy, F1-score, MAE, RMSE, and inference latency. The xLSTM model achieves a congestion prediction accuracy of 87.3%, an F1-score of 0.882, and an average inference latency of 41.2 milliseconds—outperforming baseline LSTM, GRU, and Transformer-based models in both accuracy and speed. These results validate the system’s suitability for real-time deployment in police control centers, where timely prediction of traffic congestion enables anticipatory patrol allocation and dynamic signal adjustment. By bridging AI-driven forecasting with public safety operations, this research contributes a validated and scalable approach to intelligent transportation governance, enhancing the responsiveness of urban mobility systems and advancing smart city initiatives. Full article

This study investigates the application of the Dueling Double Deep Q-Network (3DQN) algorithm to optimize traffic signal control at a major urban intersection in Danang City, Vietnam. The objective is to enhance signal timing efficiency in response to mixed traffic flow and real-world traffic dynamics. A simulation environment was developed using the Simulation of Urban Mobility (SUMO) software version 1.11, incorporating both a fixed-time signal controller and two 3DQN models trained with 1 million (1M-Step) and 5 million (5M-Step) iterations. The models were evaluated using randomized traffic demand scenarios ranging from 50% to 150% of baseline traffic volumes. The results demonstrate that the 3DQN models outperform the fixed-time controller, significantly reducing vehicle delays, with the 5M-Step model achieving average waiting times of under five minutes. To further assess the model’s responsiveness to real-time conditions, traffic flow data were collected using YOLOv8 for object detection and SORT for vehicle tracking from live camera feeds, and integrated into the SUMO-3DQN simulation. The findings highlight the robustness and adaptability of the 3DQN approach, particularly under peak traffic conditions, underscoring its potential for deployment in intelligent urban traffic management systems. Full article