Search Results (617)

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 demand for large-scale, heavy-duty autonomous pavement-sweeping robots is rising due to urban growth, hygiene needs, and labor shortages. Ensuring their health and safe operation in dynamic outdoor environments is vital, as terrain unevenness and slope gradients can accelerate wear, increase maintenance costs, and pose safety risks. This study introduces an AI-driven condition monitoring (CM) framework designed to detect terrain unevenness and slope gradients in real time, distinguishing between safe and unsafe conditions. As system vibration levels and energy consumption vary with terrain unevenness and slope gradients, vibration and current data are collected for five CM classes identified: safe, moderately safe terrain, moderately safe slope, unsafe terrain, and unsafe slope. A simple-structured one-dimensional convolutional neural network (1D CNN) model is developed for fast and accurate prediction of the safe to unsafe classes for real-time application. An in-house developed large-scale autonomous pavement-sweeping robot, PANTHERA 2.0, is used for data collection and real-time experiments. The training dataset is generated by extracting representative vibration and heterogeneous slope data using three types of interoceptive sensors mounted in different zones of the robot. These sensors complement each other to enable accurate class prediction. The dataset includes angular velocity data from an IMU, vibration acceleration data from three vibration sensors, and current consumption data from three current sensors attached to the key motors. A CM-map framework is developed for real-time monitoring of the robot by fusing the predicted anomalous classes onto a 3D occupancy map of the workspace. The performance of the trained CM framework is evaluated through offline and real-time field trials using statistical measurement metrics, achieving an average class prediction accuracy of 92% and 90.8%, respectively. This demonstrates that the proposed CM framework enables maintenance teams to take timely and appropriate actions, including the adoption of suitable maintenance strategies. Full article

The rising frequency of heatwaves, combined with the urban heat island effect, increases the population’s exposure to high temperatures, significantly impacting the health of vulnerable groups and the overall well-being of residents. While mesoscale meteorological models can reliably forecast temperatures across urban neighbourhoods, dense networks of in situ measurements offer more precise data at the street scale. In this work, the Random Forest technique was used to predict street-scale temperatures in the downtown area of Thessaloniki, Greece, during a prolonged heatwave in July 2021. The model was trained using data from a low-cost sensor network, meteorological fields calculated by the mesoscale model MEMO, and micro-environmental spatial features. The results show that, although the MEMO temperature predictions achieve high accuracy during nighttime compared to measurements, they exhibit inconsistent trends across sensor locations during daytime, indicating that the model does not fully account for microclimatic phenomena. Additionally, by using only the observed temperature as the target of the Random Forest model, higher accuracy is achieved, but spatial features are not represented in the predictions. In contrast, the most reliable approach to incorporating spatial characteristics is to use the difference between observed and mesoscale temperatures as the target variable. Full article

Particulate matter 2.5 (PM_2.5) pollution poses severe threats to public health, ecosystems, and urban sustainability. With increasing industrialization and urban sprawl, accurate pollutant monitoring and effective mitigation of PM_2.5 have become global priorities. Recent advancements in machine learning (ML) have revolutionized PM_2.5 sensing by enabling high-accuracy predictions, and scalable solutions through data-driven approaches. Meanwhile, sustainable green technologies—such as urban greening, phytoremediation, and smart air purification systems—offer eco-friendly, long-term strategies to reduce PM_2.5 levels. This review, covering research publications from 2021 to 2025, systematically explores the integration of ML models with conventional sensor networks to enhance pollution forecasting, pollutant source attribution, and intelligent pollutant monitoring. The paper also highlights the convergence of ML and green technologies, including nature-based solutions and AI-driven environmental planning, to support comprehensive air quality management. In addition, the study critically examines integrated policy frameworks and lifecycle-based assessments that enable equitable, sector-specific mitigation strategies across industrial, transportation, energy, and urban planning domains. By bridging the gap between cutting-edge technology and sustainable practices, this study provides a comprehensive roadmap for researchers to combat PM_2.5 pollution. Full article

Digital Twins (DTs) are becoming essential tools for real-time decision-making in transportation systems. This paper presents a macroscopic traffic digital twin developed for a 50 km segment of the C-32 interurban highway in Spain. The digital twin replicates highway conditions using real-time data from roadside sensors and connected vehicles via Vehicle-to-Everything (V2X) communications. It supports intelligent decision-making for traffic management, particularly during incident situations, by recommending macroscopic strategies such as variable speed limits and re-routing. Unlike many existing DTs focused on microscopic modeling or urban contexts, our approach emphasizes a macroscopic scale suitable for interurban highways, enabling faster computation and system-wide insights. The decision-making module evaluates candidate strategies using real-time simulations and selects the most effective option based on key performance indicators (KPIs), including congestion, travel time, and emissions. The system has been validated under realistic traffic scenarios using historical data, considering both congestion and pollution use cases. Strategies are communicated back to the physical infrastructure via V2I messages (IVIM) and a mobile application using the cellular communication network, enabling a closed-loop architecture. This paper contributes a scalable, real-time, and field-integrated macroscopic DT framework for highway traffic management. Full article

This study evaluates the potential of IoT-enabled adaptive traffic management systems for mitigating urban congestion, enhancing mobility, and reducing environmental impacts in densely populated cities. Using London as a case study, the research develops a multiagent simulation framework to assess the effectiveness of advanced traffic management strategies—including adaptive signal control and dynamic rerouting—under varied traffic scenarios. Unlike conventional models that rely on static or reactive approaches, this framework integrates real-time data from IoT-enabled sensors with predictive analytics to enable proactive adjustments to traffic flows. Distinctively, the study couples this integration with a multiagent simulation environment that models the traffic actors—private vehicles, buses, cyclists, and emergency services—as autonomous, behaviourally dynamic agents responding to real-time conditions. This enables a more nuanced, realistic, and scalable evaluation of urban mobility strategies. The simulation results indicate substantial performance gains, including a 30% reduction in average travel times, a 50% decrease in congestion at major intersections, and a 28% decline in CO₂ emissions. These findings underscore the transformative potential of sensor-driven adaptive systems for advancing sustainable urban mobility. The study addresses critical gaps in the existing literature by focusing on scalability, equity, and multimodal inclusivity, particularly through the prioritisation of high-occupancy and essential traffic. Furthermore, it highlights the pivotal role of IoT sensor networks in real-time traffic monitoring, control, and optimisation. By demonstrating a novel and practical application of sensor technologies to traffic systems, the proposed framework makes a significant and timely contribution to the field and offers actionable insights for smart city planning and transportation policy. Full article

Urban heritage materials face accelerated decay due to the synergistic effects of air pollution and climate change. Dose–response functions (DRFs) have emerged as a key tool to quantify and predict these risks. This review synthesizes the scientific development of DRFs, their application in Europe and China, and their role in policy and heritage management. European initiatives have refined DRFs to incorporate multi-pollutant and climate interactions, providing spatial risk maps and informing pollution control measures. In China, recent applications adapt European insights to local contexts, revealing strong influences of particulate matter. While DRFs offer clear quantitative estimates, their empirical nature and simplified assumptions necessitate complementary methods, including sensor networks, remote sensing, and machine learning models. Future research should integrate multivariate modelling, expand empirical data, and couple DRFs with real-time monitoring to better protect urban heritage materials amid environmental change. Full article

As urban air mobility moves rapidly toward real-world deployment, accurate vehicle trajectory prediction and early collision risk detection are vital for safe low-altitude operations. This study presents a deep learning framework based on an LSTM–Attention network that captures both short-term flight dynamics and long-range dependencies in trajectory data. The model is trained on fifty-six routes generated from a UAM planned commercialization network, sampled at 0.1 s intervals. To unify spatial dimensions, the model uses Earth-Centered Earth-Fixed (ECEF) coordinates, enabling efficient Euclidean distance calculations. The trajectory prediction component achieves an RMSE of 0.2172, MAE of 0.1668, and MSE of 0.0524. The collision classification module built on the LSTM–Attention prediction backbone delivers an accuracy of 0.9881. Analysis of attention weight distributions reveals which temporal segments most influence model outputs, enhancing interpretability and guiding future refinements. Moreover, this model is embedded within the Short-Term Conflict Alert component of the Safety Nets module in the UAM traffic management system to provide continuous trajectory prediction and collision risk assessment, supporting proactive traffic control. The system exhibits robust generalizability on unseen scenarios and offers a scalable foundation for enhancing operational safety. Validation currently excludes environmental disturbances such as wind, physical obstacles, and real-world flight logs. Future work will incorporate atmospheric variability, sensor and communication uncertainties, and obstacle detection inputs to advance toward a fully integrated traffic management solution with comprehensive situational awareness. Full article

Sustainable traffic management relies on accurate traffic flow prediction to reduce congestion, fuel consumption, and emissions and minimise the external environmental impacts of traffic operations. This study contributes to this objective by developing and evaluating advanced machine learning models that leverage multisource data to predict traffic patterns more effectively, allowing for the deployment of proactive measures to prevent or reduce traffic congestion and idling times, leading to enhanced eco-friendly mobility. Specifically, this paper evaluates the impact of multisource sensor inputs and spatial detector interactions on machine learning-based traffic flow prediction. Using a dataset of 839,377 observations from 14 detector stations along Melbourne’s Eastern Freeway, Bidirectional Long Short-Term Memory (BiLSTM) models were developed to assess predictive accuracy under different input configurations. The results demonstrated that incorporating speed and occupancy inputs alongside traffic flow improves prediction accuracy by up to 16% across all detector stations. This study also investigated the role of spatial flow input interactions from upstream and downstream detectors in enhancing prediction performance. The findings confirm that including neighbouring detectors improves prediction accuracy, increasing performance from 96% to 98% for eastbound and westbound directions. These findings highlight the benefits of optimised sensor deployment, data integration, and advanced machine-learning techniques for smart and eco-friendly traffic systems. Additionally, this study provides a foundation for data-driven, adaptive traffic management strategies that contribute to sustainable road network planning, reducing vehicle idling, fuel consumption, and emissions while enhancing urban mobility and supporting sustainability goals. Furthermore, the proposed framework aligns with key United Nations Sustainable Development Goals (SDGs), particularly those promoting sustainable cities, resilient infrastructure, and climate-responsive planning. Full article

This research presents an IoT-empowered adaptive mobility framework that integrates high-dimensional gesture recognition with edge-cloud orchestration for safety-critical human–machine interaction. The system architecture establishes a three-tier IoT network: a perception layer with 60 GHz FMCW radar and TOF infrared arrays (12-node mesh topology, 15 cm baseline spacing) for real-time motion tracking; an edge intelligence layer deploying a time-aware neural network via NVIDIA Jetson Nano to achieve up to 99.1% recognition accuracy with latency as low as 48 ms under optimal conditions (typical performance: 97.8% ± 1.4% accuracy, 68.7 ms ± 15.3 ms latency); and a federated cloud layer enabling distributed model synchronization across 32 edge nodes via LoRaWAN-optimized protocols (κ = 0.912 consensus). A reconfigurable chassis with three operational modes (standing, seated, balance) employs IoT-driven kinematic optimization for enhanced adaptability and user safety. Using both radar and infrared sensors together reduces false detections to 0.08% even under high-vibration conditions (80 km/h), while distributed learning across multiple devices maintains consistent accuracy (variance < 5%) in different environments. Experimental results demonstrate 93% reliability improvement over HMM baselines and 3.8% accuracy gain over state-of-the-art LSTM models, while achieving 33% faster inference (48.3 ms vs. 72.1 ms). The system maintains industrial-grade safety certification with energy-efficient computation. Bridging adaptive mechanics with edge intelligence, this research pioneers a sustainable IoT-edge paradigm for smart mobility, harmonizing real-time responsiveness, ecological sustainability, and scalable deployment in complex urban ecosystems. Full article

Rapid urbanization and the rising demand for sustainable living have encouraged the growth of smart cities, which incorporate innovative technologies to ameliorate environmental sustainability, optimize resource management, and improve living standards. The convergence of blockchain (BC) technology and the Internet of Things (IoT) presents transformative convenience for managing smart cities and achieving sustainability goals. In fact, blockchain technology combined with IoT devices provides a decentralized, transparent, and safe framework for managing massive volumes of data produced by networked sensors and systems. By guaranteeing accountability, minimizing fraud, and maximizing resource use, blockchain not only facilitates the smooth operation of smart city infrastructures but also encourages sustainable habits. The various uses of blockchain technology in smart city management and its contribution to sustainability objectives are examined in this study. Through an examination of important domains like energy distribution, waste management, transportation systems, healthcare, and governance, the research shows how blockchain promotes effective data exchange and data security, builds stakeholder trust, and makes it possible to establish decentralized organizations to improve decision-making. Full article

This study introduces a digital twin paradigm that uses both stationary and mobile sensors and cutting-edge machine learning for urban air quality monitoring. By boosting $R^2$ values from 0.29 to 0.87–0.95, our two-step calibration method increased the accuracy of low-cost PM sensors, showing the possibility of growing monitoring networks without sacrificing measurement accuracy. Significant temporal and spatial variability in PM concentrations was found by mobile sensor deployments, with variations of up to 300% over short distances, predominantly during heavy traffic. During rush hours, peak concentrations were found on multi-lane boulevards and intersections, indicating important exposure concerns usually overlooked by stationary monitoring networks. According to our Graph Neural Network model, which successfully described pollutant dispersion patterns, road dust resuspension predominates in residential areas, while vehicle emissions account for 65% of PM_2.5 along high-traffic corridors. Urban green areas lower PM levels by 30%, yet when the current low-emission zones were first implemented, they had no discernible effect on air quality. Municipal authorities can use this digital twin strategy to acquire practical insights for focused air quality improvements. The method helps make evidence-based traffic management and urban planning judgments by identifying unidentified pollution hotspots and source contributions. The technique offers a scalable option for establishing healthier urban development and marks a substantial leap in environmental monitoring. Full article

Structural health monitoring (SHM) plays a vital role in ensuring the safety, durability, and performance of civil infrastructure. This review delves into the significant advancements in optical fiber sensor (OFS) technologies such as Fiber Bragg Gratings, Distributed Temperature Sensing, and Brillouin-based systems, which have emerged as powerful tools for enhancing SHM capabilities. Offering high sensitivity, resistance to electromagnetic interference, and real-time distributed monitoring, these sensors present a superior alternative to conventional methods. This paper also explores the integration of OFSs with Artificial Intelligence (AI), which enables automated damage detection, intelligent data analysis, and predictive maintenance. Through case studies across key infrastructure domains, including bridges, tunnels, high-rise buildings, pipelines, and offshore structures, the review demonstrates the adaptability and scalability of these sensor systems. Moreover, the role of SHM is examined within the broader context of civil and urban infrastructure, where IoT connectivity, AI-driven analytics, and big data platforms converge to create intelligent and responsive infrastructure. While challenges remain, such as installation complexity, calibration issues, and cost, ongoing innovation in hybrid sensor networks, low-power systems, and edge computing points to a promising future. This paper offers a comprehensive amalgamation of current progress and future directions, outlining a strategic path for next-generation SHM in resilient urban environments. Full article

Air pollution is a major environmental and public health challenge, especially in urban areas where fine-grained air quality data are essential to effective interventions. Traditional monitoring networks, while accurate, often lack spatial resolution and public engagement. This study presents a novel wearable wireless sensor node (WSN) that was developed within the Horizon Europe SOCIO-BEE project to support air quality monitoring through citizen science (CS). The low-cost, body-mounted WSN measures NO₂, O₃, and PM_2.5. Three pilot campaigns were conducted in Ancona (Italy), Maroussi (Greece), and Zaragoza (Spain), and involved diverse user groups—seniors, commuters, and students, respectively. PM_2.5 sensor data were validated through two approaches: direct comparison with reference stations and spatial clustering analysis using K-means. The results show strong correlation with official PM_2.5 data (R² = 0.75), with an average absolute error of 0.54 µg/m³ and a statistical confidence interval of ±3.3 µg/m³. In Maroussi and Zaragoza, where no reference stations were available, the clustering approach yielded low intra-cluster coefficients of variation (CV = 0.50 ± 0.40 in Maroussi, CV = 0.28 ± 0.30 in Zaragoza), indicating that the measurements had high internal consistency and spatial homogeneity. Beyond technical validation, user engagement and perceptions were evaluated through pre-/post-campaign surveys. Across all pilots, over 70% of participants reported satisfaction with the system’s usability and inclusiveness. The findings demonstrate that wearable low-cost sensors, when supported by a structured engagement and data validation framework, can provide reliable, actionable air quality data, empowering citizens and informing evidence-based environmental policy. Full article

This paper explores the application of artificial intelligence in urban energy infrastructure construction and enhances the operation and maintenance safety of infrastructure through edge computing and advanced sensors. At present, urban manhole covers cover a large number of roads, but there is a lack of effective real-time monitoring methods. In order to effectively solve these problems, this study proposes a domain adaptive network algorithm (EDDNet) based on data fusion. By optimizing the loss function, the attention mechanism is used to make the model pay more attention to the deep features related to the abnormal state of the inspection cover. The algorithm solves the problem of broadband vibration analysis and reduces the misclassification rate in various behavioral scenarios, including pedestrian traffic, slow-moving vehicles, and intentional surface collisions. A data acquisition sensor network is established, and a six-degree-of-freedom coupled vibration model and a structural vibration model of the inspection cover are established. The vibration peak under high load conditions is modeled and simulated using impact load data, and a fitting curve is generated to achieve deep optimization of the model and enhance robustness. The experimental results show that the classification accuracy of the network reaches 95.23%, which is at least 10.2% higher than the baseline model. Full article