Search Results (76)

The efficient management of urban parking systems has emerged as a pivotal issue in today’s smart cities, where increasing vehicle populations strain limited parking infrastructure and challenge sustainable urban mobility. Aligned with the United Nations 2030 Agenda for Sustainable Development and the strategic goals of smart city planning, this study presents a sustainability-driven, multiagent simulation-based framework to model, analyze, and optimize smart parking dynamics in congested urban settings. The system architecture integrates ground-level IoT sensors installed in parking spaces, enabling real-time occupancy detection and communication with a centralized system using low-power wide-area communication protocols (LPWAN). This study introduces an intelligent parking guidance mechanism that dynamically directs drivers to the nearest available slots based on location, historical traffic flow, and predicted availability. To manage real-time data flow, the framework incorporates message queuing telemetry transport (MQTT) protocols and edge processing units for low-latency updates. A predictive algorithm, combining spatial data, usage patterns, and time-series forecasting, supports decision-making for future slot allocation and dynamic pricing policies. Field simulations, calibrated with sensor data in a representative high-density urban district, assess system performance under peak and off-peak conditions. A comparative evaluation against traditional first-come-first-served and static parking systems highlights significant gains: average parking search time is reduced by 42%, vehicular congestion near parking zones declines by 35%, and emissions from circling vehicles drop by 27%. The system also improves user satisfaction by enabling mobile app-based reservation and payment options. These findings contribute to broader sustainability goals by supporting efficient land use, reducing environmental impacts, and enhancing urban livability—key dimensions emphasized in sustainable smart city strategies. The proposed framework offers a scalable, interdisciplinary solution for urban planners and policymakers striving to design inclusive, resilient, and environmentally responsible urban mobility systems. Full article

Identifying the coupling effect mechanisms of particulate matter (PM) in different functional areas on the atmospheric environment will help to carry out graded precision prevention and control measures against pollution within mining cities. This study monitored the pollution of three different functional areas in Wuhai, a typical mining city in Inner Mongolia. PM₁, PM_2.5, PM₁₀, and TSP were sampled and analyzed for chemical fractions both in the daytime and at night in spring, summer, autumn, and winter. The results showed that the average daily concentrations of PM were generally higher in the mining area than in the urban and sandy areas in different seasons. The results of the Kerriging analysis showed that the urban area was affected the most when specific ranges of high PM concentrations were detected in the mining area and specific ranges of low PM concentrations were detected in the sandy area. PMF results indicated that the source of pollutants in different functional areas and seasons were dust, industrial and traffic emissions, combustion, and sea salt. The contributions of dust in PM with different particle sizes in the mining and sandy areas were as high as 49–72%, while all the pollutant sources accounted for a large proportion of pollution in the urban area. In addition, dust was the largest source of pollution in summer and winter, and the contribution of combustion sources to pollution was higher in winter. Health risks associated with Cr were higher in the sandy area, and non-carcinogenic risks associated with Mn were higher in the mining area during spring and summer, while there was a greater impact on human health in the urban area during autumn and winter. The results of this study revealed the coupling effect mechanisms of different functional areas on the local atmospheric environment and contribute to the development of regional atmospheric defense and control policies. Full article

Dynamic weighing systems, an advanced technology for traffic management, are designed to measure the weight of moving vehicles without obstructing traffic flow. These systems play a critical role in monitoring freight vehicle overloading, collecting weight-based tolls, and assessing the structural health of roads and bridges. However, due to the complex road traffic environment in real-world applications of dynamic weighing systems, some vehicles cannot be accurately weighed, even though precise parameter calibration was conducted prior to the system’s official use. The variation in driving behaviors among different drivers contributes to this issue. When different types and sizes of vehicles pass through the dynamic weighing area simultaneously, changes in the vehicles’ motion states are the main factors affecting weighing accuracy. This study proposes an improved SSD vehicle detection model to address the high sensitivity to vehicle occlusion and frequent vehicle ID changes in current multi-target tracking methods. The goal is to reduce detection omissions caused by vehicle occlusion. Additionally, to obtain more stable trajectory and speed data, a Gaussian Smoothing Interpolation (GSI) method is introduced into the DeepSORT algorithm. The fusion of dynamic weighing data is used to analyze the impact of changes in vehicle size and motion states on weighing accuracy, followed by compensation and experimental validation. A compensation strategy is implemented to address the impact of speed fluctuations on the weighing accuracy of vehicles approximately 12.5 m in length. This is completed to verify the feasibility of the compensation method proposed in this paper, which is based on vehicle information. A dataset containing vehicle length, width, height, and speed fluctuation information in the dynamic weighing area is constructed, followed by an analysis of the key factors influencing dynamic weighing accuracy. Finally, the improved dynamic weighing model for extracting vehicle motion state information is validated using a real dataset. The results demonstrate that the model can accurately detect vehicle targets in video footage and shows strong robustness under varying road illumination conditions. Full article

The air traffic growth expected for future years will likely cause an imbalance between traffic demand and available capacity. This could lead to increased airspace congestion, heightened complexity, and a higher workload for controllers attempting to manage the situation. Nowadays, available tools can identify 4D Area of Relatively High Air Traffic Control Complexity (4DARHAC) events up to 20 min before they occur. Nonetheless, state-of-the-art Artificial Intelligence applications can significantly increase this prediction horizon. Powered by a combination of different Machine Learning models, the ASTRA solution aims to both detect and provide resolution strategies for 4DARHACs up to 1 h before onset. To validate ASTRA’s operational concept, a series of workshops and interviews with Flow Management Position operators were conducted, focusing on assessing the initial concept and identifying end user needs. The feedback collected was validated by a board of Subject Matter Experts (SMEs) and transformed into a concrete set of functional and non-functional requirements. Overall, ASTRA’s operational concept was endorsed as a promising solution for reducing airspace complexity while alleviating operator workload during the tactical phase of operations. Experts further highlighted the importance of integrating ASTRA with existing Flow Management Position software tools to maximize its operational impact and facilitate adoption. Full article

Noise coming from road traffic represents a major contributor to the high levels of noise to which people are continuously exposed—especially in urban areas—throughout all of Europe. Since it represents a very detrimental pollutant, the assessment of such noise is an important procedure. Noise levels can be measured or simulated, and, in this second case, for the building of a valid model, a proper collection of input data cannot be left out of consideration. In this paper, the authors present the development of a methodology for the collection of the main inputs for a road traffic noise model, i.e., vehicle number, category, and speed, from a video recording of traffic on an Italian highway. Starting from a counting and recognition tool already available in the literature, a self-written Python routine based on image inference has been developed for the instantaneous detection of the position and speed of vehicles, together with the categorization of vehicles (light or heavy). The obtained data are coupled with the CNOSSOS-EU model to estimate the noise power level of a single vehicle and, ultimately, the noise impact of traffic on the selected road. The results indicate good performance from the proposed model, with a mean error of −1.0 dBA and a mean absolute error (MAE) of 3.6 dBA. Full article

Numerous design cases of abandoning auxiliary lanes for freeway dual-lane ramps with low traffic volumes exist, adapting to complex engineering conditions and reducing construction costs, but the national specifications have not posed specific setup conditions for auxiliary lanes. Thus, this paper uses traffic flow theory and simulation tools to study the critical traffic conditions applicable to auxiliary lanes on dual-lane exit ramps of freeways. Initially, the vehicle operation data in the UAV (unmanned aerial vehicle) aerial video were extracted using an object detection algorithm. Subsequently, the VISSIM simulation calibration procedure was developed based on traffic flow theory and the orthogonal experimental method. The impact of auxiliary lanes on the capacity of the freeway diverging area was analyzed through the simulation results based on traffic flow theory. Eventually, the critical traffic conditions applicable to auxiliary lanes were proposed. The results show that the maximum traffic volume applicable to non-auxiliary lane designs decreases with increasing diverging ratios. The research findings define the application conditions for auxiliary lanes on dual-lane ramp exits, contributing to the sustainable development of transportation design and operations. The VISSIM simulation calibration procedure based on data collection and traffic flow theory developed in this paper also provides an innovative and sustainable approach to road design issues. Full article

Intelligent safety and security is significant for preventing risks, ensuring information security and promoting sustainable social development, making it an indispensable part of modern society. Current research primarily focuses on the knowledge base and research hotspots in the field of intelligent safety and security. However, a comprehensive mapping of its overall knowledge structure remains lacking. A total of 1400 publications from the Web of Science Core Collection (2013–2023) are analyzed using VOSviewer and CiteSpace, through which co-occurrence analysis, keyword burst detection, and co-citation analysis are conducted. Through this approach, this analysis systematically uncovers the core themes, evolutionary trajectories, and emerging trends in intelligent safety and security research. Unlike previous bibliometric studies, this study is the first to integrate multiple visualization techniques to construct a holistic framework of the intelligent safety and security knowledge system. Additionally, it offers an in-depth analysis of key topics such as IoT security, intelligent transportation systems, smart cities, and smart grids, providing quantitative insights to guide future research directions. The results show that the most significant number of publications are from China; the top position on the list of papers published by related institutions is occupied by King Saud University from Saudi Arabia. Renewable and Sustainable Energy Reviews, Sustainable Cities and Society, and IEEE Transactions on Intelligent Transportation Systems are identified as the leading publications in this field. The decentralization of blockchain technology, the security and challenges of the Internet of Things (IoT), and research on intelligent cities and smart homes have formed the knowledge base for innovative security research. The four key directions of intelligent safety and security research mainly comprise IoT security, intelligent transportation systems, traffic safety and its far-reaching impact, and the utilization of smart grids and renewable energy. Research on IoT technology, security, and limitations is at the forefront of interest in this area. Full article

Traffic safety remains a pressing global concern, with traffic signs playing a vital role in regulating and guiding drivers. However, environmental factors like lighting and weather often compromise their visibility, impacting human drivers and autonomous vehicle (AV) systems. This study addresses critical traffic sign detection (TSD) and classification (TSC) gaps by leveraging the YOLOv8 algorithm to evaluate the detection accuracy and sign quality under diverse lighting conditions. The model achieved robust performance metrics across day and night scenarios using the novel ZND dataset, comprising 16,500 labeled images sourced from the GTSRB, GitHub repositories, and real-world own photographs. Complementary retroreflectivity assessments using handheld retroreflectometers revealed correlations between the material properties of the signs and their detection performance, emphasizing the importance of the retroreflective quality, especially under night-time conditions. Additionally, video analysis highlighted the influence of sharpness, brightness, and contrast on detection rates. Human evaluations further provided insights into subjective perceptions of visibility and their relationship with algorithmic detection, underscoring areas for potential improvement. The findings emphasize the need for using various assessment methods, advanced algorithms, enhanced sign materials, and regular maintenance to improve detection reliability and road safety. This research bridges the theoretical and practical aspects of TSD, offering recommendations that could advance AV systems and inform future traffic sign design and evaluation standards. Full article

The paper addresses the spatiotemporal variability of anthropogenic film pollution (AFP) in Avacha Gulf near the Kamchatka Peninsula based on satellite synthetic-aperture radar (SAR) imagery. Coastal waters of the study area are subject to significant anthropogenic impacts associated with intensive marine traffic, as well as the flow of household and industrial wastewater from factories located on the coast. A quantitative approach to the registration and quantitative analysis of spatiotemporal AFP distributions was applied. This approach is based on the processing of long-term time series of SAR imagery, taking into account inhomogeneous observation coverage and changing hydrometeorological conditions of different regions of water areas in various time periods. In total, 318 cases of AFP were detected in 2014–2023 in Avacha Gulf, covering 332 km² of the total area (~3% of the water area) based on the 1134 processed radar Sentinel-1A/B scenes. The average value of AFP exposure, e, was about 93 ppm, evidencing the high level of AFP in the studied water area (comparable to areas of the Black Sea with intensive marine traffic, for which e was previously determined to be between ~90 and ~130 ppm). An interannual positive trend was revealed, indicating that over the 10-year period under study, the exposure of the waters of Avacha Bay (the most polluted part of Avacha Gulf) to AFP increased ~3-fold. An analysis of AFP spatial distributions and marine traffic maps indicates that this type of activity is a significant source of anthropogenic film pollution in Avacha Gulf (including Avacha Bay). It was shown that the generated quantitative information products using the introduced AFP exposure concept can be interpreted and used, for example, for making management decisions. Full article

Avalanches present substantial hazard risk in mountainous regions, particularly when avalanches obstruct roads, either hitting vehicles directly or leaving traffic exposed to subsequent avalanches during cycles. Traditional detection methods often are designed to cover only a limited section of a road stretch, hampering effective risk management. This research introduces a novel approach using Distributed Acoustic Sensing (DAS) for avalanche detection. The monitoring site in Northern Norway is known to be frequently impacted by avalanches. Between 2022–2024, we continuously monitored the road for avalanches blocking the traffic. The automated alert system identifies avalanches affecting the road and estimates accumulated snow. The system provides continuous, real-time monitoring with competitive sensitivity and accuracy over large areas (up to 170 km) and for multiple sites on parallel. DAS powered alert system can work unaffected by visual barriers or adverse weather conditions. The system successfully identified 10 road-impacting avalanches (100% detection rate). Our results via DAS align with the previous works and indicate that low frequency part of the signal (<20 Hz) is crucial for detection and size estimation of avalanche events. Alternative fiber installation methods are evaluated for optimal sensitivity to avalanches. Consequently, this study demonstrates its durability and lower maintenance requirements, especially when compared to the high setup costs and coverage limitations of radar systems, or the weather and lighting vulnerabilities of cameras. Furthermore the system can detect vehicles on the road as important supplemental information for search and rescue operations, and thus the authorities can be alerted, thereby playing a vital role in urgent rescue efforts. Full article

The monitoring technology for targets such as aircraft and vehicles has rapidly developed in recent years and is widely used in national airspace security supervision, urban traffic supervision, and the tracking of special targets. However, the sparse trajectories of targets, primarily caused by the insufficient density of monitoring points, significantly reduce their usability. Therefore, it is important to reconstruct the target trajectories. Existing methods for the reconstruction of target trajectories often rely on topological data and convert trajectory reconstruction into a trajectory matching problem. Such methods heavily rely on topological data and cannot reconstruct trajectories in free space. To address this issue, we proposed a trajectory reconstruction method, named Prob-Attn, which does not rely on topological data and can accurately reconstruct target trajectories in free space. This method can be divided into two steps: first, a spatial trajectory construction module is proposed to determine the spatial trajectories of targets. Then, based on the reconstructed spatial trajectory of the target, this paper proposes a time series prediction model based on historical trajectories and an attention mechanism, which considers the impact of the target’s activity cycle and the surrounding status to predict the time series inside the trajectory. Finally, the proposed method is evaluated on real automatic vehicle detection datasets collected in Chongqing, China. The experimental results show that, compared with traditional methods, the proposed method can reconstruct the spatiotemporal trajectory of the target more accurately. The reconstructed trajectory data can be used for critical applications such as the intent and behavior analysis of key targets in national airspace and ground areas, providing valuable insights into security and safety. Full article

Road markings are vital to the infrastructure of roads, conveying extensive guidance and information to drivers and autonomous vehicles. However, road markings will inevitably wear out over time and impact traffic safety. At the same time, the inspection and maintenance of road markings is an enormous burden on human and economic resources. Considering this, we propose a road marking inspection system using computer vision and deep learning techniques with the aid of street view images captured by a regular digital camera mounted on a vehicle. The damage ratio of road markings was measured according to both the undamaged region and region of road markings using semantic segmentation, inverse perspective mapping, and image thresholding approaches. Furthermore, a road marking damage detector that uses the YOLOv11x model was developed based on the damage ratio of road markings. Finally, the mean average precision achieves 73.5%, showing that the proposed system successfully automates the inspection process for road markings. In addition, we introduce the Road Marking Damage Detection Dataset (RMDDD), which has been made publicly available to facilitate further research in this area. Full article

This study examines the impact of rockfalls and floods on road and rail traffic in the Jiului Gorge, Romania, a critical transportation corridor. Using Sentinel-1 radar imagery processed through ESA SNAP and ArcGIS Pro, alongside traffic detection facilitated by YOLO models, we assessed susceptibility to both rockfalls and floods. The primary aim was to enhance public safety for traffic participants by providing accurate hazard mapping. Our study focuses on the area from Bumbești-Jiu to Petroșani, traversing the Southern Carpathians. The results demonstrate the utility of integrating remote sensing with machine learning to improve hazard management and inform more effective traffic planning. These findings contribute to safer, more resilient infrastructure in areas vulnerable to natural hazards. Full article

Despite various interventions in road safety work, fatal and severe road traffic accidents ($\mathrm{R}\mathrm{T}\mathrm{A}\mathrm{s}$) remain a significant challenge, leading to human suffering and economic costs. Understanding the multicausal nature of $\mathrm{R}\mathrm{T}\mathrm{A}\mathrm{s}$, where multiple conditions and factors interact, is crucial for developing effective prevention measures in road safety work. This study investigates the multivariate statistical analysis of co-occurring conditions in $\mathrm{R}\mathrm{T}\mathrm{A}\mathrm{s}$, focusing on single-vehicle accidents with single occupancy and personal injury on Austrian roads outside built-up areas from 2012 to 2019. The aim is to detect recurring combinations of accident-related variables, referred to as blackpatterns ($\mathrm{B}\mathrm{P}\mathrm{s}$), using the Austrian $\mathrm{R}\mathrm{T}\mathrm{A}$ database. This study proposes Fisher’s exact test to estimate the relationship between an accident-related variable and fatal and severe $\mathrm{R}\mathrm{T}\mathrm{A}\mathrm{s}$ (severe casualties). In terms of pattern recognition, this study develops the maximum combination value ($\mathrm{M}\mathrm{C}\mathrm{V}$) of accident-related variables, a procedure to search through all possible combinations of variables to find the one that has the highest frequency. The accident investigation proceeds with the application of pattern recognition methods, including binomial logistic regression and a newly developed method, the PATTERMAX method, created to accurately detect and analyse variable-specific $\mathrm{B}\mathrm{P}\mathrm{s}$ in $\mathrm{R}\mathrm{T}\mathrm{A}$ data. Findings indicate significant $\mathrm{B}\mathrm{P}\mathrm{s}$ contributing to severe accidents. The combination of binomial logistic regression and the PATTERMAX method appears to be a promising approach to investigate severe accidents, providing both insights into detailed variable combinations and their impact on accident severity. Full article

To assess the impact of air pollution on human health in multiple urban areas in Greece, hourly concentrations of common air pollutants (CO, NO₂, O₃, SO₂, PM₁₀, and PM_2.5) from 11 monitoring stations in six major Greek cities (Athens, Thessaloniki, Patra, Volos, Ioannina, and Kozani), were used to implement the U.S. EPA’s Air Quality Index (AQI) during a seven-year period (2016–2022). In Athens, the capital city of Greece, hourly PM₁₀ and PM_2.5 concentrations were also studied in relation to the prevailing wind patterns, while major PM₁₀ episodes exceeding the official daily EU limit (50 μg/m³) were analyzed using the Potential Source Contribution Function (PSCF) in terms of the air mass origin. According to the AQI results, PM₁₀ and PM_2.5 were by far the most hazardous pollutants associated with moderate and unhealthy conditions in all the studied areas. In addition, in Athens, Thessaloniki, and Patra, where the benzene levels were also studied, a potential inhalation cancer risk (>1.0 × 10⁻⁶) was detected. In Athens, Saharan dust intrusions were associated with downgraded air quality, whilst regional transport and the accumulation of local emissions triggered increased PM₁₀ and PM_2.5 levels in traffic sites, especially during cold periods. Our study highlights the need for the development of early warning systems and emission abatement strategies for PM pollution in Greece. Full article