Search Results (1,726)

Main roads are usually equipped with traffic flow monitoring devices in the road network to record the traffic flow data of the main roads in real time. Three complex scenarios, i.e., Y-junctions, multi-lane merging, and signalized intersections, are considered in this paper by developing a novel modeling system that leverages only historical main-road data to reconstruct branch-road volumes and identify pivotal time points where instantaneous observations enable robust inference of period-aggregate traffic volumes. Four mathematical models (I–IV) are built using the data given in appendix, with performance quantified via error metrics (RMSE, MAE, MAPE) and stability indices (perturbation sensitivity index, structure similarity score). Finally, the significant traffic flow change points are further identified by the PELT algorithm. Full article

During the period from 2020 to 2024, 900 springs were mapped on the southern slopes of Medvednica Mountain Nature Park. Physicochemical parameters (temperature, pH, and electrical conductivity) were measured at 701 of these springs using a portable multimeter, and results were analyzed in relation to local lithology and human activities. This research provides the first results of this kind in this study area, aiming to expand the knowledge on local springs and to support the future protection and management of spring ecosystems. Springs on the Medvednica mountain showed substantial variation in measured parameters. The temperature ranged from 3.4 to 18.9 °C, reflecting local hydrological conditions, aquifer characteristics, and seasonal variability. Electrical conductivity (EC) ranged between 41 μS/cm and 2062 μS/cm, determined by both hydrogeological settings and anthropogenic impacts such as winter road salting. The pH values showed moderate variability, remaining mostly within neutral levels. These results emphasize the importance of continued monitoring and further research of Medvednica springs, in order to highlight their importance and to preserve their ecological and hydrological roles. Full article

This work focuses on the possibilities of generating synthetic data for machine learning in hazard prediction in area-based speed monitoring systems. The purpose of the research conducted was to develop a methodology for generating realistic synthetic data to support the design of a continuous vehicle speed monitoring system to minimize the risk of traffic accidents caused by speeding. The SUMO traffic simulator was used to model driver behavior in the analyzed area and within a given road network. Data from OpenStreetMap and field measurements from over a dozen speed detectors were integrated. Preliminary tests were carried out to record vehicle speeds. Based on these data, several simulation scenarios were run and compared to real-world observations using average speed, the percentage of speed limit violations, root mean square error (RMSE), and percentage compliance. A new metric, the Combined Speed Accuracy Score (CSAS), has been introduced to assess the consistency of simulation results with real-world data. For this study, a basic hazard prediction model was developed using LoRaWAN sensor network data and environmental contextual variables, including time, weather, location, and accident history. The research results in a method for evaluating and selecting the simulation scenario that best represents reality and drivers’ propensities to exceed speed limits. The results and findings demonstrate that it is possible to produce synthetic data with a level of agreement exceeding 90% with real data. Thus, it was shown that it is possible to generate synthetic data for machine learning in hazard prediction for area-based speed control systems using traffic simulators. Full article

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue in recent years, is lacking. The team proposes a traffic prediction method that leverages K-means clustering, long short-term memory (LSTM) neural network, and Fourier transform (FT) for long-term traffic prediction. The proposed method was evaluated on a real-world dataset from the U.S. Travel Monitoring Analysis System (TMAS) database, which enhances practical relevance and potential impact on transportation planning and management. The forecasting performance is evaluated with real-world traffic flow data in the state of California, in the western USA. Results show good forecasting accuracy on traffic trends and counts over a one-year period, capturing periodicity and variation. Full article

Unmanned aerial vehicles (UAVs) are increasingly used for road infrastructure inspection and monitoring. However, challenges such as scale variation, complex background interference, and the scarcity of annotated UAV datasets limit the performance of traditional segmentation models. To address these challenges, this study proposes UAV-YOLOv12, a multi-scale segmentation model specifically designed for UAV-based road imagery analysis. The proposed model builds on the YOLOv12 architecture by adding two key modules. It uses a Selective Kernel Network (SKNet) to adjust receptive fields dynamically and a Partial Convolution (PConv) module to improve spatial focus and robustness in occluded regions. These enhancements help the model better detect small and irregular road features in complex aerial scenes. Experimental results on a custom UAV dataset collected from national highways in Wuxi, China, show that UAV-YOLOv12 achieves F1-scores of 0.902 for highways (road-H) and 0.825 for paths (road-P), outperforming the original YOLOv12 by 5% and 3.2%, respectively. Inference speed is maintained at 11.1 ms per image, supporting near real-time performance. Moreover, comparative evaluations with U-Net show that UAV-YOLOv12 improves by 7.1% and 9.5%. The model also exhibits strong generalization ability, achieving F1-scores above 0.87 on public datasets such as VHR-10 and the Drone Vehicle dataset. These results demonstrate that the proposed UAV-YOLOv12 can achieve high accuracy and robustness in diverse road environments and object scales. Full article

This study examined the integraftion of airborne Light Detection and Ranging (LiDAR), Simultaneous Localization and Mapping (SLAM)-based handheld LiDAR, and iPhone LiDAR to inspect forest road networks following forest operations. The goal is to overcome the challenges posed by dense canopy cover and ensure accurate and efficient data collection and mapping. Airborne data were collected using the DJI Matrice 300 RTK UAV equipped with a Zenmuse L2 LiDAR sensor, which achieved a high point density of 285 points/m² at an altitude of 80 m. Ground-level data were collected using the BLK2GO handheld laser scanner (HPLS) with SLAM methods (LiDAR SLAM, Visual SLAM, Inertial Measurement Unit) and the iPhone 13 Pro Max LiDAR. Data processing included generating DEMs, DSMs, and True Digital Orthophotos (TDOMs) via DJI Terra, LiDAR360 V8, and Cyclone REGISTER 360 PLUS, with additional processing and merging using CloudCompare V2 and ArcGIS Pro 3.4.0. The pairwise comparison analysis between ALS data and each alternative method revealed notable differences in elevation, highlighting discrepancies between methods. ALS + iPhone demonstrated the smallest deviation from ALS (MAE = 0.011, RMSE = 0.011, RE = 0.003%) and HPLS the larger deviation from ALS (MAE = 0.507, RMSE = 0.542, RE = 0.123%). The findings highlight the potential of fusing point clouds from diverse platforms to enhance forest road mapping accuracy. However, the selection of technology should consider trade-offs among accuracy, cost, and operational constraints. Mobile LiDAR solutions, particularly the iPhone, offer promising low-cost alternatives for certain applications. Future research should explore real-time fusion workflows and strategies to improve the cost-effectiveness and scalability of multisensor approaches for forest road monitoring. Full article

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

Authorities responsible for critical infrastructure, particularly bridges, face significant challenges. Many bridges, constructed in the 1960s and 1970s, are now approaching or have surpassed their intended service life. A report from the German Federal Ministry for Digital and Transport (BMVI) indicates that about 12% of the 40,000 federal trunk road bridges in Germany are in “inadequate or unsatisfactory” condition. Similar issues are observed in other countries worldwide. Economic constraints prevent ad hoc replacements, necessitating continued operation with frequent and costly inspections. This situation creates an urgent need for cost-effective, permanent monitoring solutions. This study explores the potential use of low-cost acceleration sensors for monitoring infrastructure structures. Inclination is calculated from the acceleration data of the sensor, using gravitational acceleration as a reference point. Long-term changes in inclination may indicate a change in the geometry of the structure, thereby triggering alarm thresholds. It is particularly important to consider specific challenges associated with low measurement accuracy and the susceptibility of sensors to environmental influences in a low-cost setting. The results of laboratory tests allow for an estimation of measurement accuracy and an analysis of the various error characteristics of the sensors. The article outlines the methodology for developing low-cost inclination sensor systems, the laboratory tests conducted, and the evaluation of different measures to enhance sensor accuracy. Full article

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

Vegetables and fruits grown in urban areas pose a potential threat to human health due to contamination with heavy metals (HMs). This study aimed to identify and quantify the concentrations of heavy metals (Fe, Mn, Zn, Cu, Pb, Cd) in tomatoes, leafy vegetables, and fruits collected from 16 allotment gardens (AGs) located in Warsaw. A total of 112 samples were analyzed (72 vegetable and 40 fruit samples). Vegetables from AGs accumulated significantly higher levels of HMs than fruits. Leafy vegetables, particularly those cultivated near high-traffic roads, exhibited markedly elevated levels of Pb, Cd, and Zn compared to those grown in peripheral areas. Lead concentrations exceeded permissible limits by six to twelve times, cadmium by one to thirteen times, and zinc by 0.7 to 2.4 times. Due to high levels of Pb and Cd, tomatoes should not be cultivated in urban environments. Regardless of location, only trace amounts of HMs were detected in fruits. The greatest health risk is associated with the consumption of leafy vegetables. Lettuce should be considered an indicator plant for assessing environmental contamination. The obtained Hazard Index (HI) values indicate that only the tested fruits are safe for consumption. Meanwhile, the values of the Hazard Quotient (HQ) indicate no health risk associated with the consumption of lettuce, cherries, and red currants. Among the analyzed elements, Pb showed a higher potential health risk than other metals. This study emphasizes the need for continuous monitoring of HM levels in urban soils and the establishment of baseline values for public health purposes. Remediation of contaminated soils and the implementation of safer agricultural practices are recommended to reduce the exposure of urban populations to the risks associated with the consumption of contaminated produce. In addition, the safety of fruits and vegetables grown in urban areas is influenced by the location of the AGs and the level of industrialization of the agglomeration. Therefore, the safety assessment of plant products derived from AGs should be monitored on a continuous basis, especially in vegetables. Full article

The integration of waste plastics into pavement materials offers a dual benefit of enhancing road performance and mitigating the environmental burden of plastic waste. This review critically examines the opportunities and challenges associated with incorporating waste plastics in pavement construction, with an emphasis on their impact on the mechanical properties, durability, and life cycle performance of pavements. Special attention is given to the environmental implications, particularly the potential generation and release of micro- and nano-plastics during the pavement life cycle. This paper further evaluates current monitoring and analytical methodologies for detecting plastic emissions from road surfaces and explores emerging approaches for minimizing environmental risks. By providing a comprehensive synthesis of existing knowledge, this review seeks to support sustainable practices and inform policy development within the frameworks of circular economy and environmental stewardship. Full article

Ambient PM concentrations are influenced by various emission sources and weather conditions such as temperature, wind speed, and direction. Measurements using optical sensors cannot directly link pollution levels to specific sources. Data from roadside monitoring often show that a significant portion of PM originates from non-traffic sources. Therefore, vehicle-related PM emissions are typically estimated using simulation models based on average emission factors. This study uses the COPERT (Computer Programme to Calculate Emissions from Road Transport) model to estimate emissions from road vehicles under current conditions and future scenarios. These include the introduction of Euro 7 standards and a shift from internal combustion engine (ICE) vehicles to battery electric vehicles (BEVs). The analysis considers exhaust and non-exhaust emissions, as well as indirect emissions from electricity generation for BEV charging. The conducted study showed, among other findings, that replacing internal combustion engine vehicles with electric ones could reduce PM2.5 emissions by approximately 6% (2% when including indirect emissions from electricity generation) and PM10 emissions by about 10% (5% with indirect emissions), compared to the Euro 7 scenario. Full article

Asphalt pavement cracking represents a prevalent form of deterioration that significantly compromises road performance and safety under the combined effects of environmental factors and traffic loading. Crack sealing has emerged as a widely adopted and cost-effective preventive maintenance strategy that restores the pavement’s structural integrity and extends service life. This paper presents a systematic review of the development of crack sealing technology, conducts a comparative analysis of conventional sealing materials (including emulsified asphalt, hot-applied asphalt, polymer-modified asphalt, and rubber-modified asphalt), and examines the existing performance evaluation methodologies. Critical failure mechanisms are thoroughly investigated, including interfacial bond failure resulting from construction defects, material aging and degradation, hydrodynamic scouring effects, and thermal cycling impacts. Additionally, this review examines advanced sensing methodologies for detecting premature sealant failure, encompassing both non-destructive testing techniques and active sensing technologies utilizing intelligent crack sealing materials with embedded monitoring capabilities. Based on current research gaps, this paper identifies future research directions to guide the development of intelligent and sustainable asphalt pavement crack repair technologies. The proposed research framework provides valuable insights for researchers and practitioners seeking to improve the long-term effectiveness of pavement maintenance strategies. Full article

The remote sensing monitoring of land use changes and future scenario simulation hold crucial significance for accurately characterizing urban expansion patterns, optimizing urban land use configurations, and thereby promoting coordinated regional development. Through the integration of multi-source data, this study systematically analyzes the spatiotemporal trajectories and driving forces of land use changes in the Pearl River Delta urban agglomeration (PRD) from 1990 to 2020 and further simulates the spatial patterns of urban land use under diverse development scenarios from 2025 to 2035. The results indicate the following: (1) During 1990–2020, urban expansion in the Pearl River Delta urban agglomeration exhibited a “stepwise growth” pattern, with an annual expansion rate of 3.7%. Regional land use remained dominated by forest (accounting for over 50%), while construction land surged from 6.5% to 21.8% of total land cover. The gravity center trajectory shifted southeastward. Concurrently, cropland fragmentation has intensified, accompanied by deteriorating connectivity of ecological lands. (2) Urban expansion in the PRD arises from synergistic interactions between natural and socioeconomic drivers. The Geographically and Temporally Weighted Regression (GTWR) model revealed that natural constraints—elevation (regression coefficients ranging −0.35 to −0.05) and river network density (−0.47 to −0.15)—exhibited significant spatial heterogeneity. Socioeconomic drivers dominated by year-end paved road area (0.26–0.28) and foreign direct investment (0.03–0.11) emerged as core expansion catalysts. Geographic detector analysis demonstrated pronounced interaction effects: all factor pairs exhibited either two-factor enhancement or nonlinear enhancement effects, with interaction explanatory power surpassing individual factors. (3) Validation of the Patch-generating Land Use Simulation (PLUS) model showed high reliability (Kappa coefficient = 0.9205, overall accuracy = 95.9%). Under the Natural Development Scenario, construction land would exceed the ecological security baseline, causing 408.60 km² of ecological space loss; Under the Ecological Protection Scenario, mandatory control boundaries could reduce cropland and forest loss by 3.04%, albeit with unused land development intensity rising to 24.09%; Under the Economic Development Scenario, cross-city contiguous development zones along the Pearl River Estuary would emerge, with land development intensity peaking in Guangzhou–Foshan and Shenzhen–Dongguan border areas. This study deciphers the spatiotemporal dynamics, driving mechanisms, and scenario outcomes of urban agglomeration expansion, providing critical insights for formulating regionally differentiated policies. Full article

Particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), and heavy metals (HMs) present in polluted air are strongly associated with an increased risk of respiratory diseases. In our study, we grouped cities based on their pollution levels using a method called Ward’s cluster analysis and looked at the increased cancer risk from PM₁₀-bound harmful substances for adult men and women living in Polish cities. The analysis was based on data from 8 monitoring stations where concentrations of PM₁₀, PAHs, and HMs were measured simultaneously between 2018 and 2022. The cluster analysis made it possible to distinguish three separate agglomeration clusters: cluster I (Upper Silesia, Wroclaw) with the highest concentrations of heavy metals and PAHs, with mean levels of lead 14.97 ± 7.27 ng·m⁻³, arsenic 1.73 ± 0.60 ng·m⁻³, nickel 1.77 ± 0.95 ng·m⁻³, cadmium 0.49 ± 0.28 ng·m⁻³, and ∑PAHs 15.53 ± 6.44 ng·m⁻³, cluster II (Warsaw, Łódź, Lublin, Cracow) with dominant road traffic emissions and low emissions, with average levels of lead 8.00 ± 3.14 ng·m⁻³, arsenic 0.70 ± 0.17 ng·m⁻³, nickel 1.64 ± 0.96 ng·m⁻³, and cadmium 0.49 ± 0.28 ng·m⁻³, and cluster III (Szczecin, Tricity) with the lowest concentration levels with favourable ventilation conditions. All calculated ILCR values were in the range of 1.20 × 10⁻⁶ to 1.11 × 10⁻⁵, indicating a potential cancer risk associated with long-term exposure. The highest ILCR values were reached in Upper Silesia and Wroclaw (cluster I), and the lowest in Tricity, which was classified in cluster III. Our findings suggest that there are continued preventive actions and stricter air quality control. The results confirm that PM₁₀ is a significant carrier of airborne carcinogens and should remain a priority in both environmental and public health policy. Full article