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In this study, we propose a novel approach for estimating the relationship between neighborhood characteristics and students' academic performance. We propose the concept of urban morphology by Urban Structure Types (USTs). USTs are spatial indicators that describe the urban system through its physical, environmental, and functional characteristics. Our academic performance data includes 344,175 students from 256 public schools in the Federal District (FD), Brazil. This is student-level academic achievement data from 2017 to 2020. We performed the UST mapping in the FD by using visual interpretation. We classified 21 different types of UST. We fit mixed-effects regression models with a student-specific random intercept and slope. The model was adjusted for temporal factors, SES factors, and variables representing the characteristics and the location of each school. Our findings suggest associations between several types of USTs surrounding schools and academic performance. Overall, areas characterized as low population density, with high green index, and high standard residences were associated with an increase in student performance. In contrast, areas that include old buildings near streets, with significant traffic density, and areas with significant exposed soil (areas devasted) were associated with a decrease in student performance. The results of our study support the creation of effective educational and urban planning policies for local interventions. These interventions are likely to translate into healthier schools and improvements in children's behavioral development and learning performance. [ABSTRACT FROM AUTHOR]

Road dust samples were collected from 6 cities and provinces in northern Vietnam to evaluate contamination levels and distribution characteristics of 5 toxic elements, including Cu, Zn, Pb, Cd, and As. Total concentrations of 5 elements in road dust samples ranged from 18.4 to 470 (median 96.4) mg/kg, in declining order of Zn > Cu > Pb > As > Cd. Pollution levels in urban road dust from Hanoi (median 266; range 205–366 mg/kg) were generally higher than those measured in samples taken from other industrial areas (median 91.1; range 18.4–470 mg/kg). Our results showed that the emission of some heavy metals from urban areas with high population density and heavy traffic was comparable to or even higher than in manufacturing areas of electronic components, construction materials, and mining. The chemical fractions of heavy metals in road dust were also evaluated. More than 60% of heavy metals are present in the potential mobile phases, indicating that the anthropogenic metals are still in a relatively weakly bound form. These metals can be mobile and bioaccumulative, affecting ecosystems and humans. The geological accumulation indexes indicate moderate contamination of heavy metals in many locations of this study. Cd poses an ecological threat in several urban and industrial sites. There were no serious human health risks associated with heavy metals in road dust. However, relatively high levels of Cu and Pb in road dust imply a health risk for children living in some urban and industrial areas. Further monitoring and risk assessment studies on toxic elements in road dust should be conducted, especially in developing countries' highly urbanised and industrialised areas. [ABSTRACT FROM AUTHOR]

In recent days, the Internet of Vehicles (IoV) and its network of connected automobiles have revealed several new security risks. Classical intrusion detection systems face challenges in identifying intrusions due to the growing number of vehicles, the dynamic nature of IoV, and limited resources. A hierarchical clustering method allows dividing the IoV network into clusters. The elements that determine the outcome are the geographical proximity and the traffic density. It is called the Dynamic Hierarchical Intrusion Detection Framework (DHIDF) for the IoV. To protect infrastructure and passengers, an IoV‐specific DHIDF using edge computing has been proposed. Because of this, anomaly detection and localised assessment of danger will become less required. The application of DHIDF on a large scale inside the ecosystem of IoV is not entirely out of the question. The term encompasses several subfields, including intelligent transportation networks (ITNs), smart city infrastructure, fleet management, transportation, and autonomous vehicle systems. The efficacy of DHIDF is assessed through simulations that replicate current and potential future threats, including those related to the Internet of Things. Analysis of key performance parameters, including response time, detection accuracy, asset utilization, and scalability, has been conducted to assess the system's feasibility and durability. [ABSTRACT FROM AUTHOR]

Traffic sign detection is essential to an intelligent driving assistance system. The deep learning‐based algorithm proposed in this paper aims to address the issue of low detection accuracy caused by the small size and high density of traffic signs in real‐world traffic scenarios. First, to improve the feature extraction module of the backbone network and to increase the model's ability to capture contextual information, partial convolution (PConv) is introduced. Second, to prevent information loss during the downsampling process, a cross‐stage atrous spatial pyramid (ASPPFCSPC) is constructed using atrous convolution. This method combines feature map information from various scales and expands the receptive field. Lastly, the small‐target detection precision is improved by incorporating an additional small‐target detection head, which uses high‐resolution feature maps for shallow features. The detection head is decoupled to extract the location and class information of the predicted target separately, thereby enhancing the generalization ability of the proposed model. Experiments have demonstrated the superiority of the proposed algorithm, as testing on the TT100K dataset resulted in a mAP@0.5 of 91.2% and a mAP@0.5:0.95 of 71.8% using the proposed algorithm. [ABSTRACT FROM AUTHOR]

Air quality monitoring networks regulated by law provide accurate but sparse measurements of PM2.5 mass concentrations. High-spatial resolution maps of the PM2.5 mass concentration values are necessary to better estimate the citizen exposure to outdoor air pollution and the sanitary consequences. To address this, a field campaign was conducted in Teltow, a midsize city southwest of Berlin, Germany, for the 2021–2023 period. A network of optical sensors deployed by Pollutrack included fixed monitoring stations as well as mobile sensors mounted on the roofs of buses and cars. This setup provides PM2.5 pollution maps with a spatial resolution down to 100 m on the main roads. The reliability of Pollutrack measurements was first established with comparison to measurements from the German Environment Agency (UBA) and modelling calculations based on high-resolution weather forecasts. Using these validated data, maps were generated for 2023, highlighting the mean PM2.5 mass concentrations and the number of days per year above the 15 µg.m−3 value (the daily maximum recommended by the World Health Organization (WHO) in 2021). The findings indicate that PM2.5 levels in Teltow are generally in the good-to-moderate range. The higher values (hot spots) are detected mainly along the highways and motorways, where traffic speeds are higher compared to inner-city roads. Also, the PM2.5 mass concentrations are higher on the street than on the sidewalks. The results were further compared to those in the city of Paris, France, obtained using the same methodology. The observed parallels between the two datasets underscore the strong correlation between traffic density and PM2.5 concentrations. Finally, the study discusses the advantages of integrating such high-resolution sensor networks with modelling approaches to enhance the understanding of localized PM2.5 variability and to better evaluate public exposure to air pollution. [ABSTRACT FROM AUTHOR]

Urbanization has significantly altered the ecological resources, functions, and services, thereby imposing specific constraints on particulate matter (PM) mitigation through green spaces. To investigate the effect of green spaces on mitigating PM10,2.5 under multiple urban stressors, this study employed combined remote sensing imagery and small-scale quantitative measurements to identify the PM within green space and street tree, and their PM differences with the square underlying surface according to a continuous scale of 60~3000 m. The results indicated that urban stressors significantly influenced air PM10 and PM2.5 mitigation, with stressors LST (land surface temperature) and RD (traffic road density) as key stressors on air PM10, while LST, ISA (impervious surface area), BH (building height), NDVI (normalized difference vegetation index), GA (green space area), and WA (water body area) were key stressors on air PM2.5. Furthermore, stressors exhibited a significant scale effect on air PM10,2.5 mitigation; for air PM2.5, stressors ISA, RD, BH and BD (building density) had a notable impact on air PM2.5 mitigation at 1500~3000 m scales, while NDVI, GA, and WA showed a significant impact at 450~600 m. For air PM10, stressors ISA, BH, NDVI, and GA revealed a continuous scale effect, with the key scales occurring at 450 m and 3000 m. In summary, urbanization stressors can combine to affect air PM10 and PM2.5 mitigation by green spaces, especially at different spatial scales, to provide practical guidance for urban planning. [ABSTRACT FROM AUTHOR]

In this research work, an Adaptive Multi-Scale Dual Attention Network with ZOA for Multi-Objective CHS with energy-aware routing in 6G wireless Communication (CHS-EAR-AM-SDAN-6G) is proposed to secure the data transmission by selecting optimum cluster heads in the 6G Wireless Communication network. Initially, the nodes are gathered together to form a cluster using an Adaptive Multi-Scale Dual Attention Network (AM-SDAN). The Zebra Optimization Algorithm (ZOA) strategically selects Cluster Heads (CHs) in wireless networks based on a multi-objective fitness function (MoFF) that minimizes energy consumption while considering factors, like distance, delay, and traffic density. The path and minimum value of fitness are recognized as the routing path and statistics are promoted to the sink node through the cluster head. The proposed scheme has been applied in Python and productivity of the proposed method is predictable with the help of several performances they are energy consumption, detection rate, computational time, packet delivery rate, number of alive nodes, and security. The performance of the proposed CHS-EAR-AMSDAN-6G method attains 25.93%, 24.81%, and 23.38% of alive nodes, 24.45%, 26.71% and 21.32% lower packet delivery rate, 27.56%, 26.43%, and 28.61% low computational period, related with three current methods, such as Generative Adversarial Learning for ITM in 6G Wireless Communication Networks (CHS-EAR-GAL-ITM-6G), ML Algorithms for the Future 6G WCN (CHS-EAR-ML-6G), Energy Efficient Distributed Federated Learning to the 6G Wireless Communication Networks (CHS-EAR-EEDFL-6G), respectively. [ABSTRACT FROM AUTHOR]

Effective driving decision-making significantly enhances the safety of automated commercial vehicles. Different from small passenger vehicles mainly focusing on anti-collision, the inducements of collision and rollover for commercial vehicles are coupled with each other. However, these factors are not considered together which results in a limitation in the safety performance. This paper proposes a novel comprehensive driving decision-making methodology based on deep reinforcement learning (CDDM-DRL) for automated commercial vehicles in expressway scenarios. The CDDM-DRL consists of two parts. First, a feature encoding network is designed to encode hierarchical features from traffic situations and driving conditions, which can provide more useful feature information. Then an actor–critic network incorporating ensemble methods is developed to learn and provide effective driving actions, such as whether to turn and when to turn. Finally, extensive simulations in common and challenging scenarios with different traffic densities were performed. Experimental results show that our proposed method is better than some classical DRL methods in terms of time headway, backward clearance, lateral acceleration, etc. Moreover, it can prevent collision and rollover simultaneously, and realize safe driving decision-making for automated commercial vehicles. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper focuses on the design of LED luminary intensity control (LIC) modules that are energy efficient. This work suggests various luminary intensity control techniques to develop a flexible outdoor lighting system that is cost-effective, energy-efficient, and eco-friendly. The different modules for varying luminary intensity (LI) proposed and facilitate the lighting system to turn ON its luminaries only when lighting is required based on various parameters such as time zone, traffic density, traffic mobility, and authentic user control. The proposed intensity control modules are (i) Zone timer-based intensity control (ZTIC), (ii) ZTIC with motion detection (ZTICMD), (iii) Authorized User LIC (AULIC) (iv) Hybrid mode of LIC (HMLIC), (v) HMLIC with motion detection (HMLICMD) (vi) Intensity control based on priority (ICP). The modules use simple circuitry. Its functioning is verified. The developed system exhibits an overall energy savings of 60–85%. The proposed circuits are appropriate for energy-efficient outdoor street lighting applications. [ABSTRACT FROM AUTHOR]

The Black Sea, a unique semi-enclosed marine ecosystem, is the eastern maritime boundary of the European Union and holds significant ecological importance. The present study investigates anthropogenic noise pollution in the context of the Marine Strategy Framework Directive's Descriptor 11, with a particular emphasis on the criteria for impulsive sound (D11C1) and continuous low-frequency sound (D11C2) in Romanian ports, which handle a substantial share of regional cargo traffic, and impact maritime activities and associated noise levels. The noise levels from shipping activity vary across Romanian waters, including territorial waters, the contiguous zone, and the Exclusive Economic Zone. These areas are classified by high, medium, and low ship traffic density. Ambient noise levels at frequencies of 63 Hz and 125 Hz, dominated by shipping noise, were established, along with their hydrospatial distribution for the 2019–2020 period. Furthermore, predictive modeling techniques are used in this study to assess underwater noise pollution from human sources. This modeling effort represents the first initiative in the region and utilizes the BELLHOP ray-tracing method for underwater acoustic channel modeling in shallow-water environments. The model incorporates realistic bathymetry, oceanography, and geology features for environmental input, allowing for improved prediction of acoustic variability due to time-varying sea variations in shallow waters. The study's findings have important implications for understanding and mitigating anthropogenic noise pollution's impact on the Black Sea marine ecosystem. [ABSTRACT FROM AUTHOR]

The expected number of drone operations in the coming decades, together with the fact that most of them will take place in very-low-level airspace, will lead to a density of drone flights much greater than that of conventional manned aviation. In this context, the number of conflicts (i.e., 4D convergence of drone trajectories below the safe separation minima) will be much more frequent than in manned aviation and, therefore, conventional air traffic management methods or even the specific proposed mechanisms for drone traffic management are unlikely to be able to solve them safely. This paper considers a set of simulated drone trajectories in a high-density urban environment to analyze the applicability of machine learning regression and classification techniques to detect conflicts among such trajectory times in advance of their occurrence in order to provide new methods to manage the expected drone traffic density safely and efficiently. This would not be possible with current drone traffic management solutions. The obtained results suggest that the Random Forest, Artificial Neural Networks and Logistic Regression algorithms could detect nearly all near-collisions up to 10 s before they occur, and the first two algorithms could also detect a significant number of near-collisions more than 60 s earlier. [ABSTRACT FROM AUTHOR]

The scarcity of marine life caused by shallow waters and pollution has pushed fishermen to venture up to 10 km offshore, where clearer waters offer richer fish resources, but at much higher fuel costs. This situation has caused increased unemployment, so many fishermen have changed professions. This is exacerbated by seasonal variations and resource limitations. This research introduces a fish sensor network (FSN), which is designed to equip floating fish houses (FADs) with network-connected fish sensors. The proposed network allows fishermen to find locations with high fish populations, thereby reducing fuel costs and increasing fishing efficiency. This article presents preliminary findings, identifies potential challenges including natural factors such as wind, waves, currents, corrosion, and radio propagation, as well as man-made obstacles such as traffic density and physical measurements at the research site in Belawan, Indonesia. Additionally, this paper briefly discusses the energy availability that poses further challenges. [ABSTRACT FROM AUTHOR]

ABSTRACT In modern cities, vehicle ad hoc networks (VANETs) hold great promise for improving safety on the roads, traffic control, and communicating. The constantly changing and intricate nature of VANETs hampers adequate routing due to a variety of factors. The work examines the use of a multitiered metaheuristic algorithm that combines Bellman–Ford routing with the Fire Hawk, Gannet Optimization, and Mayfly Optimization algorithms to find optimal routing solutions for VANETs. The suggested work addresses network topological dynamics, actual time limitations, flexibility, connectivity problems, and security issues, in addition to the different quality of service (QoS) requirements in VANETs. Bellman–Ford serves as the foundational routing layer and maintains communication, whereas Fire Hawk, Gannet Optimization, and Mayfly Optimization improve and optimize routing patterns at subsequent levels. The multitier approach aims to strike a balance between the rational and probabilistic aspects of routing so as to satisfy the minimal latency needs of applications that are safety‐critical and respond quickly to the rapid changes in VANETs. The suggested work is evaluated with a traffic density ranging from 500 to 5000 with a step size of 500, and it has been observed that for the highest traffic density of 5000, the proposed work obtains a latency of 3.88 ms, data delivery rate of 0.944, a throughput of 2.94 Mbps, and energy efficiency of 97.14%. The proposed work remains robust compared to integrating state‐of‐the‐art metaheuristic algorithms such as ECRA, HOPRP, and CM. Overall, the proposed routing techniques increase routing efficiency in VANETs and make it possible to create vehicle communication networks that are more trustworthy and safe. [ABSTRACT FROM AUTHOR]

Traffic shockwaves demonstrate the formation and spreading of traffic fluctuation on roads. Existing methods mainly detect the shockwaves and their propagation by estimating traffic density and flow, which presents weaknesses in applications when traffic data is only partially or locally collected. This paper proposed a four-step data-driven approach that integrates machine learning with the traffic features to detect shockwaves and estimate their propagation speeds only using partial vehicle trajectory data. Specifically, we first denoise the speed data derived from trajectory data by the Fast Fourier Transform (FFT) to mitigate the effect of spontaneous random speed fluctuation. Next, we identify trajectory curves' turning points where a vehicle runs into a shockwave and its speed presents a high standard deviation within a short interval. Furthermore, the Density-based Spatial Clustering of Applications with Noise algorithm (DBSCAN) combined with traffic flow features is adopted to split the turning points into different clusters, each corresponding to a shockwave with constant speed. Last, the one-norm distance regression method is used to estimate the propagation speed of detected shockwaves. The proposed framework was applied to the field data collected from the I-80 and US-101 freeway by the Next Generation Simulation (NGSIM) program. The results show that this four-step data-driven method could efficiently detect the shockwaves and their propagation speeds without estimating the traffic densities and flows nearby. It performs well for both homogenous and nonhomogeneous road segments with trajectory data collected from total or partial traffic flow. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are essential to future transportation systems, potentially reducing traffic congestion. This study examines the impact of different vehicle control strategies on traffic flow through simulations. We propose a novel stochastic cellular automaton model, the controlled stochastic optimal velocity (CSOV) model, which incorporates vehicle control effects. Within the CSOV model, two control strategies are implemented: gap-based control (GC), which adjusts vehicle velocity to balance the gaps between adjacent vehicles, and flow-based control (FC), which aims to maintain a consistent local flow between the front and rear vehicles. Results show that both control strategies improve traffic flow. However, under weaker control, the GC sometimes resulted in lower flow compared to no control. In contrast, the FC consistently enhanced flow across control strengths, yielding more robust outcomes. Furthermore, when both strategies achieved comparable flow rates, the FC provided a more stable velocity distribution under varying traffic densities than the GC. [ABSTRACT FROM AUTHOR]

Efficient traffic safety management necessitates real‐time crash risk prediction using expressway characteristics. With the emergence of autonomous vehicles (AVs), the development and evaluation of variable speed limit (VSL) strategies, a key active traffic management technique, become crucial for enhancing safety and mobility in mixed traffic flows. This underscores the need for optimized VSL strategies to accommodate both conventional and AVs. This paper presents a study on the development of VSL control algorithms using deep reinforcement learning in a microscopic traffic simulation. As the rewards function, time‐to‐collision and speed were considered. To enhance traffic safety, VSL strategies were refined across various market penetration of connected AVs. Analysis revealed that safety and traffic density are improved by 53% and 59%, respectively, in market penetration rate (MPR) 50, marking significant safety improvements in congested and low MPR scenarios. These findings present the importance of developing and evaluating VSL strategies for mixed traffic flow, particularly in the context of increasing the prevalence of connected and AVs. [ABSTRACT FROM AUTHOR]

Traffic flow uncertainty quantification is important for making reliable decisions in transportation operations. Compared with well-studied level prediction or point prediction models, the study of uncertainty quantification that can capture the second-order fluctuations of traffic observations is still in its infancy. Current traffic flow uncertainty quantification approaches can be classified in general into distribution- or nondistribution-based. For the former, generalized autoregressive conditional heteroscedasticity (GARCH) model and stochastic volatility (SV) have been widely applied to quantify traffic flow uncertainty in terms of prediction interval, usually under a parametric Gaussian distribution assumption. However, a parametric model relies on a prespecified model structure and cannot meet the requirement raised by the time-varying traffic condition patterns. Therefore, this paper proposed a real-time traffic condition uncertainty quantification approach based on a nonparametric probability density function (PDF) estimation. For this approach, the real-time nonparametric kernel density estimation method is applied to capture the time-varying probability density of traffic flow data based on which prediction intervals are constructed in real time using the quantiles computed from the estimated time-varying nonparametric PDF. Real-world traffic flow data are applied to validate the proposed approach. The results show that the proposed approach outperforms the comparative models of an online GARCH filter and three lower and upper bound estimation (LUBE) models based on multilayer perceptron (MLP), spiking neural network (SNN), and long short-term memory networks (LSTM). The findings indicate that the quantification of traffic condition uncertainty is complementary to the conventional traffic condition level modeling, and combined, traffic level modeling and traffic uncertainty quantification can support the development of proactive and reliable transportation applications. [ABSTRACT FROM AUTHOR]

Meteorological factors, specifically wind direction and magnitude, influence the dispersion of atmospheric pollutants due to road traffic by affecting their spatial and temporal distribution. In this study, we are interested in the effect of the evolution of horizontal wind components, i.e., in the plane perpendicular to the altitude axis. A two-dimensional numerical model for solving the coupled traffic flow/pollution problem, whose pollutants are generated by vehicles, is developed. The numerical solution of this model is computed via an algorithm combining the characteristics method for temporal discretization with the finite-element method for spatial discretization. The numerical model is validated through a sensitivity study on the diffusion coefficient of road traffic and its impact on traffic density. The distribution of pollutant concentration, computed based on a source generated by traffic density, is presented for a single direction and different magnitudes of the wind velocity (stationary, Gaussian, linearly increasing and decreasing, sudden change over time), taking into account the stretching and tilting of plumes and patterns. The temporal evolution of pollutant concentration at various relevant locations in the domain is studied for two wind velocities (stationary and sudden change). Three regimes were observed for transport pollution depending on time and velocity: nonlinear growth, saturation, and decrease. [ABSTRACT FROM AUTHOR]

The problem of traffic density is complex in the world of land transportation, especially in urban areas, including Bandung City. Buah Batu Road, one of the main roads in Bandung City is 13 meters wide and 1.70 kilometers long, connecting Bandung City and Bandung Regency. This study examines the relationship between vehicle speed and traffic density on Buah Batu Road, Bandung. Using the macroscopic Lighthill-Whitham Richards (LWR) model, Second Order Polynomial Regression, and Lax-Wendroff scheme simulation. This study aims to obtain the speeddensity function for traffic. The introduction emphasizes the importance of understanding traffic flow dynamics to reduce congestion, especially in areas with significant vehicle growth. The methodology used is direct observation of the Buah Batu Road section with an observed length of 18 meters, with data collected through cellphone camera recordings at various times. These observation data provide insight into vehicle density and speed under various conditions. [ABSTRACT FROM AUTHOR]

Special vehicles (SVs) are vehicles which conduct tasks such as the maintenance of urban roads and are typically characterized by travelling at a lower speed at a constant rate of speed within the same lane. In order to reduce the influence of SVs, guidance zone is designed and provides traffic guidance suggestions (TGS) for human‐driven vehicles (HVs) helping drivers for better decision between car‐following (CF) and lane‐changing (LC). To verify the effectiveness of TGS, an improved Dogit‐agent‐based model is established to simulate the captive and not captive choice of CF and LC for different driver types under TGS, and build the rules for mixed traffic flow of SV and HVs. Finally, a numerical simulation with a three‐lane system is conducted to analyze the traffic efficiency through a set of indicators, and the results show that the TGS can reduce the influence of SVs on traffic flow in a specific occupancy rates range, increase the cross‐section traffic volume by about 5%. The TGS also can increase the average speed of HVs in the lane behind SV by about 5% to 30%, and increase traffic density to 200% on the underutilized lane in the raw space in front of the SV. [ABSTRACT FROM AUTHOR]

The rapid development of maritime transport and the gradual increase in the number of ports, ships and shipping routes can produce direct economic benefits for regional development, and a profound grasp of the actual situation of maritime transport makes it possible to make practical plans for economic development and thus rationally develop and use marine resources. However, there is a lack of research on maritime transport, so this study is based on the AIS, waterways, routes and ports data in the near-shore sea area of Guangdong Province. Using GIS spatial analysis technology and various mathematical models, we refer to the evaluation method of road traffic dominance. It analyses and proposes the evaluation indexes of maritime traffic advantages, such as the density of maritime traffic network and the proximity of ports. Based on the actual situation of the study regions, the indicators were quantitatively evaluated and analyzed. The economic development level of each region was also quantitatively evaluated using principal component analysis, and the study regions were classified based on the coupling-coordination model. The results show that the coastal area of Guangdong Province is divided into four types of zones according to the coupling-coordination type of zoning criteria. Corresponding development suggestions are put forward for different zones, and the research results provide certain practical guidelines for promoting the benign cycle development of maritime traffic and economy, and have important guiding significance and application value for the organization and safety of maritime traffic. [ABSTRACT FROM AUTHOR]

The editorial "Oxidative Stress Induced by Air Pollution" discusses the health risks associated with exposure to particulate matter, especially PM2.5, in urban areas. Studies highlighted in the article explore the impact of air pollution on various biological systems, including cardiovascular health, respiratory function, and fetal development. Promising protective strategies, such as marine algae-derived compounds, show potential in reducing cellular damage caused by air pollutants. The need for targeted regulatory measures and biologically relevant health indicators is emphasized to mitigate environmental risks, particularly for vulnerable groups like those with higher BMI. [Extracted from the article]

Urban traffic emissions have significant environmental and health implications. Diverging from traditional research that primarily aims to improve traffic flow and efficiency, this study specifically focuses on the environmental impact on traffic emissions, conducting a comprehensive analysis within the Manhattan urban network through four route guidance strategies. The performance of these strategies is examined across various vehicle densities, and their impact on four traffic pollutant emissions (Carbon-dioxide, Nitrogen Oxides, Volatile Organic Compounds and Particulate Matter) is assessed. Moreover, our innovative approach analyzes emissions from the perspectives of both travel distance and trip frequency, placing special emphasis on trip frequency to provide practical insights with high real-world applicability. The results highlight the potential and limitation of the Congestion Coefficient Strategy. Under equal travel demands, the Congestion Coefficient Strategy showed promise in reducing carbon emissions. However, at lower vehicle densities, it led to a significant increase in emissions. This revelation pointed to the need for modifications to the strategy when applied in scenarios with lower traffic density. Recognizing this limitation, we introduced a modified strategy that achieved remarkable reductions in emissions across diverse vehicle densities, effectively overcoming the challenges posed by the original Congestion Coefficient Strategy. These findings offer valuable insights for policymakers and transportation planners in selecting optimal route guidance strategies to reduce pollutant emissions. Future studies will explore the efficacy of these strategies in road networks characterized by different topological configurations. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the association between social determinants, environmental exposure metrics, and the risk of asthma emergency department (ED) visits in the New York State (NYS) Medicaid population using small-area analysis. Traffic densities for each census tract in NYS were calculated using the length of road segments within each tract and total area of the tract to produce a measure of average number of vehicles per square meter per day. Data on social determinants of health including internal and external environments and other demographic factors were obtained from various sources. Poisson regression analyses were conducted to identify significant factors associated with asthma ED visits in Medicaid claim and encounter data for years 2005–2015. High traffic density in NYS excluding New York City (NYC) correlated with increased risk of asthma ED visits (RR 1.69; 95% CI: 1.42, 2.00), mitigated by adjusting for environmental and social determinants (RR 1.00; 95% CI: 0.85, 1.19). Similar trends were observed in NYC only (RR 1.19; 95% CI: 1.00, 1.41), with the adjusted risk remaining elevated (RR 1.14; 95% CI: 0.98, 1.33) albeit not statistically significant. Living in census tracts with high concentrated disadvantage index, high proportions of minorities, and less green space predicted higher asthma ED visits. We mapped predicted rates and model residuals to identify areas of high risk. Our results support previous findings that environmental and social risk factors in poor and urban areas contribute to asthma exacerbations in the NYS Medicaid population, even if they may not necessarily contribute to its development.

5G New Radio (NR)-V2X, standardized by 3GPP Release 16, includes a distributed resource allocation Mode, known as Mode 2, that allows vehicles to autonomously select transmission resources using either sensing-based semi-persistent scheduling (SB-SPS) or dynamic scheduling (DS). In unmanaged 5G-NR-V2X scenarios, SB-SPS loses effectiveness with aperiodic and variable data. DS, while better for aperiodic traffic, faces challenges due to random selection, particularly in high traffic density scenarios, leading to increased collisions. To address these limitations, this study models the Cellular V2X network as a decentralized multi-agent networked Markov decision process (MDP), where each vehicle agent uses the Shared Experience Actor–Critic (SEAC) technique to optimize performance. The superiority of SEAC over SB-SPS and DS is demonstrated through simulations, showing that the SEAC with an N-step approach achieves an average improvement of approximately 18–20% in enhancing reliability, reducing collisions, and improving resource utilization under high vehicular density scenarios with aperiodic traffic patterns. [ABSTRACT FROM AUTHOR]

The advent of connected vehicles holds significant promise for enhancing existing traffic signal and vehicle speed control methods. Despite this potential, there has been a lack of concerted efforts to address issues related to vehicle fuel consumption and emissions during travel across multiple intersections controlled by traffic signals. To bridge this gap, this research introduces a novel technique aimed at optimizing both traffic signals and vehicle speeds within transportation networks. This approach is designed to contribute to the improvement of transportation networks by simultaneously addressing issues related to fuel consumption and pollutant emissions. Simulation results vividly illustrate the pronounced the effectiveness of the proposed traffic signal and vehicle speed control methods of alleviating vehicle delay, reducing stops, lowering fuel consumption, and minimizing CO2 emissions. Notably, these benefits are particularly prominent in scenarios characterized by moderate traffic density, emphasizing the versatility and positive impact of the method across varied traffic conditions. [ABSTRACT FROM AUTHOR]

• Drivers neglected vehicles positioned behind and behind side during takeovers. • High oncoming traffic worsened situational awareness of surrounding vehicles. • Situational awareness moderated impacts of surrounding vehicles on safety margins. • Higher situational awareness was not always associated with takeover performance. This study aimed to understand the influence of surrounding vehicle configuration, driving lane, and traffic density on drivers' situational awareness (SA), takeover performance, and eye-tracking behaviors in conditionally automated driving. An experiment was conducted with the participation of 40 university students using a fixed-base driving simulator configured to simulate SAE Level 3 automation. During the experiment, participants were engaged in playing Tetris on a tablet as a non-driving related task in automated driving mode. Upon hearing an auditory takeover request, participants were instructed to take control of the vehicle, and then complete a scene reconstruction task to report their SA after transferring control back to the automated driving system. Our findings showed that drivers often neglected vehicles at their sides and rear during the takeover, which was associated with higher collision risks. Higher oncoming traffic density led to drivers' worse SA of surrounding vehicles but more cautious driving behavior. Driving in the right lane generally resulted in smoother takeovers with lower collision risks. Interestingly, while SA did moderate the impacts of driving conditions on safety margins, a higher level of SA did not consistently relate to improved performance, especially in complex scenarios. This suggests the need for support systems that guide drivers to focus on safety–critical objects rather than simply amplifying SA in general. These insights have significant implications for the design of driver monitoring and support systems in automated vehicles. [ABSTRACT FROM AUTHOR]

Summary: Electric vehicles (EVs) are the emerging environmentally friendly approach that is used to minimize greenhouse gases and carbon dioxide (CO2) emissions in the atmosphere. A clear strategy is required for scheduling charging stations (CS) to EVs based on their applications. In this research work, a robust routing and effective charge scheduling approach are devised using a cloud‐assisted Vehicular Ad Hoc NETwork (VANET) for charging EVs. Here, the multi‐objectives, like predicted traffic density, battery power, and distance, are used to identify the optimal routing of EV to CS. The predicted traffic density is evaluated using Deep Long Short Term Memory (DLSTM) and is trained using a developed Jaya Election‐Based Optimization Algorithm (JEBOA), which is the incorporation of Jaya Optimization (Jaya) and Election‐Based Optimization Algorithm (EBOA). Next to optimal routing, the charge scheduling process is carried out using the Fractional Jaya Election‐Based Optimization Algorithm (Fractional JEBOA) by considering the priority, response time, and latency of the EV. The designed Fractional JEBOA is the integration of Fractional Calculus (FC) and the developed JEBOA. Moreover, the various evaluation metrics are considered to calculate the performance of the designed method, which attained a delay of 0.243 ms, distance of 35 km, power of 95 W, response time of 0.441 s and traffic density of 0.664. [ABSTRACT FROM AUTHOR]

Copyright of Asian Journals of Ecotoxicology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cadmium (Cd), a significant environmental pollutant, is highly toxic to humans, animals, and plants. Its harmful effects are notable even at low concentrations, and it persists in biological systems for extended periods. Given its classification as a type I carcinogen, monitoring changes in the Cd concentration in the air is highly important. This study explored the variation in Cd concentrations in specific plant species and plant organs at different vehicular traffic densities to identify the most effective species and organs for the biomonitoring of Cd concentrations in the air. The Cd concentration changes in different organs of five plant species were analyzed at various vehicular traffic densities. The findings suggest that among the species examined, Nerium oleander is most suitable for use as a biomonitor for Cd, with unwashed organs being recommended for biomonitoring purposes. [ABSTRACT FROM AUTHOR]

This study aims to improve vessel trajectory prediction in the inner harbor of Busan Port using Automatic Identification System (AIS) data and deep-learning techniques. The research addresses the challenge of irregular AIS data intervals through linear interpolation and focuses on enhancing the accuracy of predictions in complex port environments. Recurrent neural network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU models were developed, with LSTM delivering the highest performance. The primary scientific question of this study is how to reliably predict vessel trajectories under varying conditions in inner harbors. The results demonstrate that the proposed method not only improves the precision of predictions but also identifies critical areas where Vessel Traffic Service Operators (VTSOs) can better manage vessel movements. These findings contribute to safer and more efficient vessel traffic management in ports with high traffic density and complex navigational challenges. [ABSTRACT FROM AUTHOR]

Objective: Child pedestrian injuries are a significant public health problem, largely because children have underdeveloped cognitive-perceptual capacity to judge traffic unsupervised. This study used a virtual reality (VR) environment to examine the impact of children's age, as well as sex and sensation-seeking personality, on pedestrian behavior in different risk contexts. Methods: 405 Norwegian children (7–10-year-olds) engaged in street-crossing scenarios within a VR environment. Children crossed a bicycle path and urban roadway six times, each with increasing density and complexity of traffic. Hits and near hits were recorded. Self-reported sensation-seeking personality was assessed. Results: Children were more likely to experience crashes in the tasks that offered higher probability risk. Overall, 106 children crossed safely in all tasks. Dangerous crossings were associated with male sex, higher thrill and intensity seeking personality, and denser traffic. Age was not related to any traffic safety outcomes. Conclusion: As expected, children were struck by vehicles more often in complex traffic contexts than in less complex ones. The results support previous findings and suggest that boys and sensation seekers have elevated risk of pedestrian injury, and that individual differences in children, rather than age alone, must be considered when determining if children are capable of safely negotiating traffic unsupervised. [ABSTRACT FROM AUTHOR]

Fatigue is one of the crucial factors in human error-related traffic accidents. Despite the development of highly advanced intelligent transport systems, fatigue-related traffic accidents have not decreased. The factors inducing driver fatigue are classified into mental and physical categories. Physical fatigue results from muscle strain due to prolonged driving and operations. Mental fatigue, on the other hand, results from the continuous mental effort required for driving, including repeated perception and decision-making regarding driving situations and route planning. Monitoring driver fatigue can help prevent fatigue-related traffic accidents. Therefore, researchers have studied its relationship with various biomarkers such as sleep state, eye movement, facial expression, and electroencephalography (EEG) activation levels. Moreover, studies have revealed the relationship between fatigue and cognitive performance, which is affected by factors such as extended periods of driving. Furthermore, the strategy, quantity, and quality of driving operations and perception differ in various traffic environments. For instance, driving stress levels vary depending on factors such as the number of vehicles on the road, traffic congestion, and road conditions. However, the brain activity associated with mental and physical workload due to the traffic environment and its factors remains unknown. In particular, the relationship between mental and physical stress resulting from varying levels of operation and perception in different driving environments, the accumulation of driver fatigue caused by such stress, and the related brain activity are still unclear. In this study, we focused on investigating the mental and physical workload that accumulates in drivers and induces physical and mental fatigue, as well as the related brain activity caused by different traffic environments. We investigate these aspects through driving experiments, measuring EEG in driving environments created by varying the traffic environment and density using a driving simulator. The results confirmed differences in theta- and alpha-band spectral responses, which are associated with driver fatigue, across different traffic environments. Further examination of the causal relationship showed that mental and physical workload were associated with fatigue-related spectral responses depending on the traffic environment. These findings imply that the level of cognitive and operational load inherent in driving environments plays a crucial role in driver fatigue. [ABSTRACT FROM AUTHOR]

Heavy metal concentrations, which have increased continuously in the environment this century due to anthropogenic factors, severely threaten human and environmental health. Antimony (Sb) is one of the most toxic and harmful heavy metals in terms of human and environmental health. Therefore, the priority research subjects are monitoring the alteration of Sb pollution in the airborne and reducing pollution. This study was conducted to conclude the proper species to monitor and drop airborne Sb contamination on trees grown in Düzce, the 5th most polluted European city. This study examined samples taken from Pseudotsuga menziesii, Cupressus arizonica, Pinus pinaster, Picea orientalis, and Cedrus atlantica, and the Sb concentration changes based on tree species, route, tissue, and age range in the last 40 years were evaluated. The study hypothesizes that Sb concentration varies depending on (1) tree species, (2) direction, (3) plant tissue, and (4) age range, all confirmed in this study. In conclusion, the maximum concentrations were achieved in the outer bark and east (5.45 µg g−1) and north directions (6.72 µg g−1), with high traffic density. In addition, the mining and industrial places (sources of metal pollution) are not close to the study area. Therefore, it was concluded that traffic pollution was the primary source of Sb pollution in the study area. The study revealed that C. arizonica is the most suitable species for monitoring and reducing the change in Sb pollution because the highest Sb concentration (4.47 µg g−1) in wood (the largest organ) was obtained in C. arizonica. [ABSTRACT FROM AUTHOR]

Summary: In the past few years, restricted wireless sensor networks (WSNs) enabled the Internet of Things (IoT) have attracted significant attention and expansion to enhance service delivery and resource efficiency. Dissemination is a service offered by WSN that uses radio transmission and over‐the‐air programming for updating the deployed sensor nodes through online. The centralized data dissemination methods are replaced by the distributed approaches because they affect the drawbacks of a single point of failure, no scalability, and insecurity. Therefore, an Energy Efficient Protocol for Data Dissemination in Wireless Sensor network‐enabled IoT using Domain‐Adaptive Message Passing Graph Neural Network (EEP‐WSN‐IoT‐DMPGNN) is proposed in this paper. The nodes are formed as clusters utilizing the Deep Fuzzy Curriculum Clustering (DFCC) technique that rewards nodes belonging to a given cluster. By using the Crayfish Optimization Algorithm (COA), the Cluster Head (CH) selection optimally chose the ideal CH and satisfies the multiple objective functions, such as energy, delay, traffic density, and distance. Afterward, domain‐adaptive Message Passing Graph Neural Network (DMPGNN) based routing protocol is developed, the input given to the routing protocol includes a sink, action history, future node, and maximum‐distance node, which attains enhanced data transfer in the chosen path. The proposed technique attains a lower no. of dead nodes, lower energy consumption, and higher Network Lifetime while analyzed with existing techniques, such as routing technique depending on deep learning for effectual data transmission in 5G WSN communication (DL‐RPDT‐WSN), Reinforcement‐Learning base energy effectual optimized routing protocol in WSN (RL‐EERP‐WSN), and Energy‐efficient intellectual routing method for IoT‐enabled WSN (EIR‐IoT‐WSN), respectively. [ABSTRACT FROM AUTHOR]

Double parking has many negative effects on traffic indicators such as traffic congestion, traffic flow conditions, and traffic safety. Double parking includes parameters that affect drivers' behavioral and traffic habits. Various inspection activities and penal sanctions are implemented to prevent parking violations. Within the scope of this study, it is aimed to detect double parking with the YOLOv8 model, one of the deep learning algorithms. In this direction, a data set consisting of a total of 891 images was created, taking into account the streets with high traffic density in İzmit and Erzurum. As a result of the YOLO model, the measurement parameter F1 score value was obtained as 0.83. The mAP@0.5 values of the model for double parking, normal parking and the entire data set were obtained as 0.851, 0.922 and 0.886, respectively. When other performance parameters were examined, it was concluded that the model successfully detected the double parking situation. According to the model performance results, 89% of double and normal parking situations were detected correctly. A data set infrastructure has been created for studies on the detection of double parking. With this study, the initial work of the systems for automatic detection of parking violations and instant warning of drivers was carried out. [ABSTRACT FROM AUTHOR]

This study was aimed at improving the energy efficiency of the distribution of liquefied natural gas (LNG) as shipping fuel in the southern Baltic Sea. The objective of this study was to determine the demand for LNG for maritime shipping by analyzing the distribution of the resource from the water side (ship-to-ship). LNG was chosen due to the location of the LNG terminal in Świnoujście within the analyzed water area, where a problem has arisen in the southern part of the Baltic Sea regarding fuel supply for vessels due to the lack of developed infrastructure along the coast. An analysis was conducted to optimize the size of the LNG fleet and infrastructure facilities. Seeking compliance with Annex VI to the MARPOL 73/78 Convention, adopted by the International Maritime Organization (IMO), shipowners see potential in the switch from conventional fuels to LNG. As one of the alternative solutions, it will contribute to reducing harmful emissions. Determination of the LNG distribution volume requires the identification of LNG storage facility locations, specifying the number of LNG-powered ships (broken down by type) and the number of LNG bunkering ships. The first part of this study contains a detailed analysis of the number of sea-going ships that provide services in the southern part of the Baltic Sea and the world's number of LNG bunkering ships. The database contains a set of the characteristics required to determine the optimal demand for LNG, where LNG bunkering vessels are capable of supplying fuel within the shortest possible time and covering the shortest possible distance to LNG-powered ships. The characteristics include the type of ship, requested LNG volume, the speed of LNG bunkering ships, the distance between LNG facilities, and the loading rate (the volume of fuel received per time unit). Based on the collected data, the volume of LNG distribution was determined using MATLAB R2019a software. The remainder of this study contains a description of the conducted research and results of an analysis of the traffic density in the Baltic Sea. The results were obtained on the basis of data from the Statistical Yearbook of Maritime Economy and IALA IWRAP Mk2 2020 software. The number of LNG-powered ships and number of LNG bunkering ships were specified, and the demand for LNG for the area under analysis was determined. [ABSTRACT FROM AUTHOR]

• Speeding contributes to many lane departure collisions on horizontal curves and ramps. • We developed models to analyze speeding on Interstate horizontal curves and ramps. • The odds of speeding increases on horizontal curves with larger radii and superelevation. • The odds of speeding increases on horizontal curves and ramps with improved level of service. • The odds of speeding increases on horizontal curves and ramps during the morning and evening hours, and on weekends. Introduction: Lane departure collisions account for many roadway fatalities across the United States. Many of these crashes occur on horizontal curves or ramps and are due to speeding. This research investigates factors that impact the odds of speeding on Interstate horizontal curves and ramps. Method: We collected and combined two unique sources of data. The first database involves comprehensive curve and ramp characteristics collected by an automatic road analyzer (ARAN) vehicle; the second database includes volume, average speed, and speed distribution gathered from probe data provided by StreetLight Insight®. We evaluated the impacts of level of service (LOS), which reflects traffic density or level of congestion, time of the day (morning, evening, and off-peak hours), time of the week (weekdays and weekends), and month of the year (Jan-Dec), and various information about geometric characteristics, such as curve radius, arc angle, and superelevation, on odds of speeding. Results: The results show that the odds of speeding increases at horizontal curves with improved levels of service, as well as those with larger radii and superelevation. The odds of speeding decreases on curves with larger arc angles and during the winter months of the year. The findings indicate a reduction in odds of speeding at diagonal/loop ramps with larger arc angles and narrower lane widths. Conclusion: The results show the importance of using speed enforcement and other countermeasures to reduce speeding on curves with low traffic volumes, high speed limits, and large radius and superelevation, especially for those in rural areas. Practical application: The results could be used to prioritize locations for the installation of speed countermeasures or dispatch enforcement resources to high-priority locations and times. [ABSTRACT FROM AUTHOR]

Traffic accidents are one of the serious problems that are often faced in the context of law enforcement, especially in countries with high traffic density such as Indonesia. This study aims to evaluate how discretionary decisions taken by police investigators can affect the case settlement process, taking into account aspects of justice and public interest. The study utilized policy documents as the primary data source, focusing on analyzing these documents through a systematic document analysis and content analysis approach. The document analysis technique will help in organizing and summarizing these documents to identify relevant information and protocols regarding the application of discretion. The content analysis will then be employed to examine the specifics of how discretion is framed and regulated within these policies, revealing the underlying principles and standards guiding police decision-making in traffic accident cases. This research would contribute to refining policies and practices related to police discretion, ensuring they effectively balance flexibility with adherence to legal and ethical standards. [ABSTRACT FROM AUTHOR]

This study aimed to compare the ability of five plant species, including (Conocarpus erectus, Acacia sensu lato (s.l.), Melaleuca viminalis, Dodonaea viscosa and Lantana camara) to absorb and accumulate heavy elements in their tissues, which were grown in the central islands in the city of Diwaniyah. This included areas of street in front of the medical college, Umm Al Khail First Street, Umm Al-Khail Street, near Abbas Attiwi Bridge, Al-Adly Street in the Euphrates District, and Clock Field Street, respectively. Results showed that soil samples S1 and S3 were contaminated by Pb, and the rest of the sites were contaminated with nickel only. This indicates through the table findings a rise in these heavy metals' concentrations with a rise in traffic momentum. Thus, the Pb concentrations in the growing plants' shoot parts with respect to this research had surpassed the allowed critical limit of 5.00 mg.kg-1 dry matter, in which the highest value was recorded at the site with respect to S3 as well as S2. Meanwhile, the findings indicate that Cd concentrations in S3 and S1 had increased and exceeded the allowable limit of 0.20 mg.kg-1 dry matter. In the meantime, the nickel concentrations were within the permissible limits of 67.90 mg.kg-1 dry matter. The Zn concentration exceeded the permitted limits of 60.00 mg.kg-1 dry matter except for plants (Acacia s.l. and Lantana camara) in sites S5 and S2. The results confirmed that the values of Heavy Metals Bioaccumulation Coefficient (BAC) for most of the study elements had recorded the highest value in the Dodonaea plant for Zn, Cd, and Pb, except for Ni. It was more accumulated in the Melaleuca viminalis plant, which indicates the superiority of the Dodonaea plant in accumulating Pb, Cd, and Zn over the rest of the study plants, as they took the following order: Lantana camara < Acacia s.l. < Conocarpus erectus < Melaleuca viminalis < Dodonaea viscosa. The best plants accumulated nickel in the following order: Acacia s.l. < Lantana camara < Conocarpus erectus < Dodonaea viscosa < Melaleuca viminalis. [ABSTRACT FROM AUTHOR]

As the population in cities increases, traffic problems have emerged, especially at intersections with high traffic density. Increasing traffic density leads to longer transportation times, higher fuel consumption, and elevated levels of environmental pollution. Various techniques have been employed to decrease traffic congestion. In order to apply these methods, the degree of traffic density must first be determined. This is typically done through vehicle counting studies in the field using camera images. However, manually counting vehicles from camera images is a very detailed process. Therefore, various automated methods based on image processing techniques are preferred today to perform these operations faster and more accurately. In this study, we designed virtual zones using different vehicle counting methods at intersections based on image processing techniques. We obtained vehicle count data from four methods, including manual counting and three methods based on image processing techniques. We evaluated the accuracy of the counting results using transportation engineering parameters such as density and traffic volume. Additionally, we modeled the signalized intersection in the AIMSUN simulation program. The study found that the "New Type Virtual Zone" method resulted in vehicle counts that were 95% accurate, and the average success rate of the AIMSUN simulation analysis results performed with this data was 83.71% accurate. [ABSTRACT FROM AUTHOR]

This research proposes a modified Crank–Nicolson finite difference scheme with local fractional derivatives to approximate the solutions of local fractional LWR traffic flow model. The stability and consistency of the scheme are examined. Further, convergence of the scheme is assured by using Lax’s equivalence theorem. Some exemplary instances are discussed along with their simulations to validate the proposed method. The obtained numerical solutions show the dynamical evolution of traffic density with respect to time and space. The results derived using the proposed numerical scheme establish that they are quite effective in obtaining the numerical solution to the fractal vehicular traffic flow problem. [ABSTRACT FROM AUTHOR]