Search Results (2,393)

Urban air quality is a key component of environmental sustainability and public health in large metropolitan areas. Following the substantial but temporary improvements in air quality observed during the COVID-19 lockdowns, it remains unclear whether structural changes in urban air pollution have persisted in the post-pandemic period. This study analyzes the temporal dynamics of major atmospheric pollutants in Mexico City between 2021 and 2024, including CO, NO₂, NO_x, O₃, PM₁₀, PM_2.5, and SO₂, using hourly data from the Mexico City Atmospheric Monitoring System (SIMAT). Annual and monthly median concentrations were computed to reduce the influence of extreme values and short-term pollution episodes. Station-level monotonic trends were evaluated using the non-parametric Mann–Kendall test, complemented by the use of Sen’s slope estimator to quantify the magnitude and direction of change. Absolute and relative changes between 2021 and 2024 were also analyzed to capture incremental variations not reflected by trend significance tests and performed together with hourly monthly analyses to characterize diurnal and seasonal patterns. Results indicate that no statistically significant monotonic trends were detected for any pollutant across the analyzed stations (p > 0.05), suggesting an overall stabilization of air quality levels during the post-pandemic period. Nevertheless, moderate increases in annual median concentrations were observed at specific locations, particularly for PM₁₀, PM_2.5, NO₂, and NO_x, with relative changes ranging from approximately 5% to 35%. Persistent diurnal and seasonal patterns were identified, closely associated with traffic activity, photochemical processes, and meteorological conditions. These findings suggest that, although no robust long-term trends are evident, incremental increases and stable temporal structures remain relevant from a sustainability perspective. Continued monitoring and targeted air quality management strategies are therefore necessary to support long-term urban environmental sustainability. Full article

Complex structural health monitoring (SHM) systems are rarely installed on typical bridges, likely because of an expected low return on investment; however, low-cost, passive sensors made from a retroreflective sheeting material (RRSM) offer an economical alternative for SHM of typical bridges. Most departments of transportation (DOTs) fabricate and maintain traffic signs made from RRSMs. By using a material familiar to DOTs, the technology transfer from signs to strain sensing is streamlined. This paper focuses on the development of a passive strain sensor made from an RRSM. A standard Type XI fluorescent yellow-green RRSM is tested in tension to establish the relationship between retroreflectivity (RR) and induced strain. Results show RR decreases linearly with increasing strain after an initial plateau of ~1000 × 10⁻⁶ m/m. To function as a strain sensor, the RRSM is pre-strained beyond the plateau. A production sensor is designed to attach to the tension face of a structural element for monitoring. Periodic RR measurements are used to estimate the likely maximum strain change at the sensor location. The sensor has the potential to provide a practical, low-cost, and easily implementable solution to improve the monitoring of typical bridges, enhancing their safety and longevity. Full article

On heavily traveled highway corridors, traffic congestion, lane merges, toll facilities, and complex interchanges frequently trigger sudden and aggressive deceleration, commonly referred to as harsh braking (HB). Such maneuvers reflect near-miss driving conditions that may precede crashes. Traditional traffic safety analyses rely primarily on historical crash records, a reactive approach that limits agencies’ ability to identify and address emerging risks in a timely manner. Because HB events are continuously captured by connected-vehicle telematics, they provide an opportunity to evaluate roadway safety risk more proactively. This study investigates the applicability of harsh braking events as a surrogate indicator of crash risk on New Jersey interstate highways. The analysis uses more than 8.5 million connected-vehicle telemetry records from Drivewyze and approximately 45,000 police-reported crashes collected between July and December 2024. HB events were identified using a deceleration threshold of 6 ft/s² (approximately 0.2g) and spatially matched to one-mile highway segments along with crash data. Spatial analysis shows that both HB events and crashes are highly concentrated along major corridors, including I-95, I-80, I-78, and I-287, with notable clustering near toll plazas and complex interchange areas. Temporal patterns indicate that harsh braking activity increases substantially during late fall, likely reflecting seasonal congestion and adverse weather conditions. To quantify the relationship between HB events and crash frequency, Negative Binomial (NB) and Zero-Inflated Negative Binomial (ZINB) regression models were estimated at the segment level. Results reveal a positive and statistically significant association between HB events and crash counts. In the preferred ZINB model, each additional HB event is associated with approximately a one percent increase in expected crash frequency. While the effect of individual events is small, repeated harsh braking activity corresponds to a meaningful increase in crash risk; for example, an increase of 10 HB events corresponds to an expected crash frequency of about 10% higher. Overall, the findings suggest that connected-vehicle HB data can complement traditional crash records by providing early indications of elevated risk. Incorporating HB monitoring into highway safety programs may support proactive identification of hazardous locations and more timely deployment of targeted countermeasures. Full article

The data plane and the control plane are targets for Distributed Denial of Service (DDoS) attacks in the Software-Defined Internet of Things (SDIoT). Currently available studies rely on observations from a single network layer which limits the cross-layer attack analysis. This paper presents a synchronized, phase-aware, and a multi-layer traffic collection framework mimicking SDIoT environments under diverse DDoS attack scenarios. The data collected are the metrics captured at host, switch, and controller layers during normal, attack, and post-attack phases with strict temporal alignment. For capturing diverse DDoS attack behaviors in SDIoT environments, representative data plane attacks including volumetric flooding and switch-level flow table saturation were used. Control plane level attack targeting the SDN controller was implemented. The evaluation was done using a Mininet-based SDIoT testbed with a POX controller. Each scenario is executed across five independent runs with statistical validation. The proposed framework enables reproducible and time-aligned multi-layer analysis through standardized orchestration and automated logging. Results indicate that SDIoT DDoS behavior demonstrates differently across traffic, state, and resource-level metrics, and that accurate characterization benefits from temporally aligned multi-layer monitoring rather than relying solely on packet rate analysis. Full article

Sustainable urban development increasingly depends on understanding how digital activity is distributed across space and time, yet the spatiotemporal dynamics of the urban digital landscape remain poorly mapped by conventional data sources. This study uses Beijing as an empirical testbed, applying a multi-dimensional analytical framework to massive mobile network traffic data to decode the metabolic rhythms, distributional laws, and functional organization of the urban digital landscape. The results reveal three findings. First, the urban digital landscape exhibits a sleepless trapezoidal temporal rhythm characterized by continuous saturation without a midday trough and a quantifiable weekend activation lag, indicating that digital metabolism is structurally decoupled from physical mobility patterns. Second, digital traffic follows a skew-normal distribution consistent with a 20/70 rule of spatial polarization, in which the top 20% of super-connector nodes sustain approximately 70% of total urban digital flow, yielding a Gini coefficient of 0.68 as a measurable indicator of infrastructure inequality and systemic vulnerability. Third, four distinct functional prototypes are identified—ranging from continuously active metropolitan cores to inverse-tidal ecological peripheries—empirically validating Beijing’s polycentric transformation through the lens of digital flows. These findings demonstrate that large-scale mobile network traffic data offers a replicable and structurally distinct lens for sustainable urban digital governance, supporting resilient network planning, equitable allocation of digital resources, and evidence-based monitoring of urban functional transformation in rapidly growing megacities. Full article

Surface texture plays a key role in pavement safety and performance, yet its degradation is influenced by multiple interacting factors that vary across road networks. This study developed statistical models to characterize and predict surface texture evolution on Portuguese highways using linear mixed-effects modeling. Texture measurements collected on 7204 pavement sections, each 100 m in length, over three monitoring cycles were analyzed alongside traffic, climatic, pavement structural, geometric, and spatial variables. The hierarchical structure of the data, with repeated measurements nested within pavement sections, was explicitly accounted for via random intercepts and random slopes. At the same time, temporal correlation was modeled via an autoregressive error structure. Two model specifications were evaluated: a model including only traffic and climatic variables and an extended model incorporating pavement and geometric characteristics. Results indicate that texture evolution is statistically associated with cumulative traffic loading, temperature-related indicators, precipitation, surface course type, lane position, vertical alignment, and altitude. The extended model showed a significantly better fit and superior predictive performance, as confirmed by information criteria and cross-validation metrics. The findings highlight the importance of accounting for section-level heterogeneity and roadway characteristics when modeling texture degradation. The proposed modeling framework provides a statistically scalable and robust tool for texture prediction, accounting for regional-specificities and long-term pavement management decisions. Full article

The growth of human activity in cities is a key factor in the degradation of air quality. Numerous studies have demonstrated the link between air quality and the existence of dangerous and chronic diseases that are extremely costly for individuals and society. This study presents an analytical framework that compares fixed and mobile air-quality monitoring approaches in cities with limited resources, using Mohammedia city, Morocco, as an example. The framework centers on mobile monitoring units mounted on vehicles and equipped with affordable sensors, GPS technology, and wireless communication systems to track important pollutants, including fine particulate matter (PM_2.5 and PM₁₀) and harmful gaseous compounds (NO₂, SO₂, CO, O₃). The evaluation relies on scenario-based modeling, performance data from existing literature, and calculations of costs throughout the system’s lifetime. To enhance measurement reliability, the researchers developed a correction system that addresses measurement errors caused by temperature, humidity, vehicle speed, vibrations, traffic-related interference, operational interruptions, and communication limitations. The findings indicate that fixed monitoring stations deliver superior measurement precision, with estimated uncertainty ranging from ±1.2–2.5%, though their coverage area is restricted to 0.534 km² (representing 1.6% of Mohammedia). In comparison, the suggested mobile setup could potentially monitor 9.8 km², covering approximately 30% of the city, while decreasing infrastructure needs and setup time (2–4 h compared to 2–4 weeks). Over 10 years, the total cost is EUR 252,000 for mobile monitoring, compared with EUR 3.6 million for a network of 20 fixed stations. These results demonstrate that corrected mobile monitoring systems offer significant promise as an economical and sustainable approach for managing urban environmental conditions. Full article

Accurate assessment of the skid resistance of asphalt pavement is crucial for traffic safety. However, traditional detection methods suffer from inefficiency, high costs, and limited coverage, making them inadequate for large-scale road network monitoring. This paper proposes a method for assessing the skid resistance of asphalt pavements based on hyperspectral remote sensing. First, hyperspectral data of asphalt pavements with different aging degrees were acquired through ground-based spectral measurements, and feature bands correlated with the aging process were selected using the successive projections algorithm. Based on these results, the feature bands were applied to unmanned aerial vehicle (UAV)-based hyperspectral images to construct an aging spectral index capable of characterizing pavement aging conditions. Combined with the decision tree method, assessment of pavement aging conditions was achieved, with an overall accuracy of 96.52% and a Kappa coefficient of 0.948. Finally, a quantitative relationship model between the aging spectral index and skid resistance was established using regression analysis, with the coefficient of determination (R²) and root mean square error (RMSE) of the model being 0.869 and 3.26, respectively. The proposed method enables efficient, contactless and large-scale assessment of pavement skid resistance, expanding the application of UAV remote sensing technology in road maintenance. Full article

Residential heating is a major contributor to atmospheric pollution, especially in populated areas. Traditional methods for measuring emissions, such as chimney probes, are limited due to the need for prior owner consent, which can compromise the reliability of results—particularly when detecting the illegal burning of materials like plastic or waste oil. This study introduces a mobile air pollution monitoring system using compact sensor modules installed on vehicles and drones. These autonomous modules are equipped with gas, particulate matter, and environmental sensors, along with Global Positioning System (GPS) tracking to record pollutant concentrations in real time and associate them with specific geographic locations. Field experiments conducted in Hungary and Uzbekistan demonstrated the system’s effectiveness in detecting elevated pollutant levels in rural areas with solid fuel heating and in urban zones affected by industrial activity and traffic. For instance, PM2.5 concentrations ranged from 15 μg/m³ in forested areas to as high as 160 μg/m³ in industrial zones, while CO₂ levels near chimneys exceeded background values by 15–25 ppm. Drone-based measurements enabled vertical profiling and direct analysis of emissions from individual chimneys, providing detailed spatial distribution data. The proposed mobile sensing approach allows for the accurate localization of pollution sources and the assessment of air quality variations within small-scale environments. This method overcomes limitations of stationary or pre-announced inspections and supports proactive environmental monitoring and enforcement. Full article

Rapid urbanization and increasing traffic volumes necessitate effective road safety measures, particularly in metropolitan areas. Enhancing road safety is a fundamental pillar of social sustainability as it directly reduces the socio-economic burden of traffic accidents and promotes resilient urban environments. This article analyzes the impact of infrastructural traffic-calming devices on road safety parameters using a GIS-based method. This study provides a quantitative tool for monitoring and measuring the effectiveness of sustainable transport infrastructure. The study examines six different types of devices across 44 locations within the GZM Metropolis, Poland, utilizing official police data (Accident and Collision Records System—SEWIK) from a period of two years before and two years after implementation. The primary parameters analyzed include the frequency of incidents, the severity of injuries, and the structure of accident types. The results demonstrate a substantial positive association following the interventions, with an average 41.33% reduction in road incidents across all tested devices. Specifically, speed bumps proved most effective, reducing incidents by over 66%. However, the analysis revealed a critical anomaly: While pedestrian refuge islands decreased the overall number of minor injuries, they correlated with an increase in the number of severe injuries, suggesting a need for careful consideration. Furthermore, the study confirms a positive shift in the structure of incidents, notably a substantial decrease in rear-end and side-impact collisions. The findings offer practical evidence for evidence-based urban policies, contributing to the development of safe, inclusive, and sustainable transport systems in line with global sustainability goals. Full article

Evaluating urban mobility transitions is essential to determine whether local transport interventions support broader sustainability goals. Cities increasingly implement initiatives to promote public transport, active mobility, and low-carbon transport systems. Still, assessing their impact on city-scale structural change remains challenging. Existing evaluation approaches often rely on project-level monitoring or fragmented indicators, which limits cross-city comparison and the assessment of long-term system transformation. This paper proposes a dual-track methodology to evaluate sustainable urban mobility interventions. The first track uses city-defined key performance indicators to capture local implementation processes, governance dynamics, and perceived outcomes. The second track relies on publicly available open data to assess city-scale changes in mobility indicators, including public transport accessibility, cycling infrastructure provision, and traffic-related air pollution. The methodology is applied to ten European cities using open data and satellite-based environmental indicators. Results indicate that while cities report progress at the project level, external indicators show limited short-term structural change in city-wide mobility systems. These findings highlight the value of open data as an independent evaluation layer that contextualises local results and supports transparent assessment of urban mobility transitions. Full article

Accurate characterization of inter-vehicle spacing is fundamental for safety assessment and sustainable operation of road networks, particularly on two-lane rural roads where monitoring infrastructure is limited. Unlike temporal headways, vehicle spacing directly reflects physical vehicle interactions and roadway occupancy, making it a more appropriate variable for evaluating collision risk and operational efficiency. This study develops a probabilistic framework for modeling vehicle spacing based on the statistical isomorphism between Event Flows and Linear Fields of Random Points. Using a calibrated microscopic simulation model, spacing distributions are generated for unidirectional traffic over flow rates from 100 to 1300 veh/h. A Pearson Type III distribution is shown to consistently reproduce the observed asymmetry, kurtosis, and non-zero minimum spacing across traffic regimes. Distribution parameters are estimated via maximum likelihood and validated using a heuristic Kolmogorov–Smirnov procedure suitable for large samples. Results demonstrate systematic relationships between spacing distribution parameters and macroscopic traffic variables, enabling estimation of the probability of unsafe spacing conditions from commonly available traffic data. The proposed framework supports sustainability-oriented traffic management by providing a quantitative basis for safety evaluation and operational control without requiring extensive sensing infrastructure. Full article

Remote tower (rTWR) operations are reshaping air traffic control but introduce significant human-factor risks, notably cognitive fatigue induced by prolonged screen-based visual surveillance. To mitigate these risks in a safety-critical domain where missed detections can be catastrophic, we propose a non-intrusive, multimodal fatigue detection framework fusing ocular and cardiac signals. A high-fidelity simulation study with 36 controllers was conducted to collect eye-tracking and electrocardiogram (ECG) data, from which a 12-dimensional feature vector—integrating gaze entropy and heart rate variability (HRV)—was extracted. Addressing the severe class imbalance and scarcity of fatigue samples in physiological data, we developed a cost-sensitive XGBoost classifier combining SMOTE oversampling with a dynamically weighted loss function. Experimental results show that the proposed framework performed well under mixed-subject evaluation and improved sensitivity to fatigue events. Although a marked performance drop was observed under LOSO evaluation, personalized calibration partially alleviated this limitation, indicating the potential of the framework for real-time fatigue monitoring in remote tower operations. Full article

Urban transport is a major contributor to particulate matter (PM) pollution, yet information on the spatial distribution of fine and ultrafine particle fractions remains limited in medium-sized tropical cities. This study examines the spatial variability of transport-related particulate matter across eleven urban districts in Padang, Indonesia, using Nano Sampler-based measurements. Size-segregated PM concentrations (PM₁₀, PM_2.5, PM₁, and PM_0.5) were obtained from 24 h sampling campaigns conducted between June and July 2025 at locations selected based on urban density, proximity to major roadways, and land-use characteristics. Descriptive statistics, correlation analysis, and principal component analysis were applied to evaluate spatial patterns and traffic-related influences. The results show pronounced spatial heterogeneity in PM concentrations. Traffic-intensive and mixed-use districts exhibited higher PM levels, particularly for coarse and ultrafine fractions, whereas coastal districts showed lower concentrations due to enhanced atmospheric ventilation. Strong correlations were observed between traffic volume and coarse PM fractions, with moderate associations for fine and ultrafine particles, indicating combined exhaust and non-exhaust emissions. These findings highlight the importance of district-specific mitigation strategies and size-resolved monitoring to support effective urban air-quality management. Full article

Vehicle detection is a core task in smart city perception management and an important technical support for sustainable urban development and intelligent transportation optimization. In high-resolution unmanned aerial vehicle (UAV) remote sensing images, it faces challenges such as variable target scales, severe occlusion, and difficulty in modeling long-range dependencies. To address these issues, this study proposes the MCViM-YOLO algorithm, which integrates the local perception advantage of convolution with the global modeling capability of the state space model (Mamba). Based on YOLOv12, the algorithm reconstructs the neck network: it introduces the Mix-Mamba module (parallel multi-scale convolution and selective state space model) to simultaneously capture local details and global spatial dependencies, adopts the dual-factor calibration fusion module (DCFM) to adaptively fuse heterogeneous features, and employs a dual-branch attention detection head (DADH) to optimize the prediction of difficult samples (e.g., occluded, small-scale vehicles). Experiments on the VEBAI dataset demonstrate that our proposed model achieves an mAP@0.5 of 92.391% and a recall rate of 86.070%, with a computational complexity of 10.41 GFLOPs. The results show that the proposed method effectively improves the accuracy and efficiency of vehicle detection in complex remote sensing scenarios, provides technical support for traffic flow monitoring, low-carbon urban planning, and other sustainable applications, and offers an innovative paradigm for the deep integration of CNN and state space models with both theoretical research value and engineering application prospects. Full article