Search Results (43)

Road traffic crashes remain a major global public health and economic challenge, with heavy vehicle drivers disproportionately involved in severe incidents, particularly in low- and middle-income countries. In Ethiopia, limited access to continuous professional training constrains efforts to improve drivers’ safety-related knowledge and awareness. This study explored the impact potential and user acceptance of gamified e-learning modules designed to enhance heavy vehicle drivers’ knowledge and awareness of fatigue management, speed-related behavior, and eco-driving practices. A randomized pretest–post-test control-group design was employed, in which professional drivers were assigned to either an intervention group that completed three gamified e-learning modules or a control group that received no training. Data were analyzed using mixed repeated-measures analysis of variance. The results revealed significant time × group interaction effects across all domains (p < 0.001), with substantially greater improvements in the intervention group and large effect sizes. Participants also reported high perceived usefulness, behavioral intention, and trust in the system. These findings provide preliminary evidence that gamified e-learning may be a feasible and promising approach for improving short-term safety-related knowledge among professional heavy vehicle drivers. Further research is needed to determine whether these improvements are sustained over time and translate into behavioral change and measurable road safety outcomes before broader implementation can be recommended. Full article

Traffic noise adversely affects residents near expressways, calling for sustainable noise mitigation solutions. This study developed three eco-friendly sound-absorbing panels from sand, industrial slag, and microporous ceramics. By optimizing aggregate gradation, the influence of porosity and flow resistivity on absorption coefficients was analyzed to determine optimal mix ratios. The panels were integrated into perforated metal noise barriers and evaluated through reverberation room and sound insulation tests. Field simulations using SoundPLAN for a residential project in Taizhou validated real-world performance. Results showed that slag panels achieved a Noise Reduction Coefficient (NRC) of 0.70, while sand and ceramic panels both reached 0.55. All configurations maintained a weighted sound reduction index (R_w) of 25–26 dB. Empirical simulations confirmed that a 2.5 m high barrier keeps noise levels within the 60 dB limit. Compared with traditional glass wool, these inorganic panels offer comparable noise reduction, superior non-combustibility, and better weather resistance, making them effective for frequency-specific noise control in urban engineering applications. Full article

Agricultural soil fertility is a key determinant of crop productivity and long-term sustainability. However, intensive farming practices often require repeated passes of heavy machinery, which can lead to soil compaction. This study examines the interplay between tractor traffic, tire inflation pressure, and their effects on soil physical properties and fertility indicators. Tire pressure management emerges as a crucial mitigation strategy: high inflation pressures concentrate the load and exacerbate subsoil compaction, whereas reduced pressures (within safe limits) enlarge the tire–soil contact area, distributing the vehicle’s weight more evenly. This in turn improves traction, lowers ground pressure, and reduces energy losses. As a result, both the depth and severity of soil compaction are reduced. Further advances may be achieved through innovative tires manufactured with eco-sustainable materials and tread patterns specifically designed to enhance traction and minimize slippage-related energy loss. In this context, CREA conducted comparative field tests on two tractor tire models from the same manufacturer: a conventional design and an evolved version featuring an innovative tread and larger footprint. The trials assessed the impact of each tire on soil compaction, traction performance, and energy efficiency. Tests were performed on a silty-clay agricultural soil naturally settled for a year, using a dynamometric vehicle to apply different controlled traction force levels, combined with two inflation pressure settings. To highlight performance differences between the two models, the tractor was rear-ballasted, and the study focused on the rear axle, which carried most of the traction stress. Results indicated that, under the specific test conditions, at high inflation pressure both tires performed similarly (with the innovative model slightly reducing fuel use and the conventional yielding marginally higher maximum tractive force), whereas at low pressure the innovative tire clearly outperformed the traditional model in traction efficiency and caused less soil compaction. The extent of the benefits associated with using the innovative tire model across various soil conditions, moisture levels, and in the absence of rear ballasting will be evaluated in further tests based on traction force control using the proposed testing system. Full article

Porous concrete is widely recognized as an eco-friendly pavement material; however, existing studies mainly focus on its use as a base course, and systematic investigations on porous concrete specifically designed for heavy-traffic pavements and multifunctional surface performance remain limited. In this study, a novel multifunctional porous concrete with integrated noise reduction and drainage performance (PCNRD) was developed as a top-layer pavement material, addressing the performance gap in current applications. A comprehensive evaluation of the surface properties of porous concrete was performed based on tests of the sound absorption, void ratio, permeability, and wear resistance. The results demonstrate that the porous concrete exhibits excellent sound absorption (sound absorption coefficient 0.22–0.35) and high permeability (permeability coefficient 0.63–1.13 cm/s), and superior abrasion resistance (abrasion loss ≤ 20%) within an optimized porosity range of 17–23%. Furthermore, an optimized pavement thickness (8–10 cm) was proposed, and functional correlations among key surface performance indicators were revealed for the first time. Based on a uniform experimental design, four key mix parameters (water–cement ratio, cement content, silica fume content, and cement strength grade) were examined using strength and effective porosity as dual control indices, leading to the development of a novel mix design method tailored for PCNRD. This study not only fills the technical gap in high-performance porous concrete for heavy-traffic pavement surfaces but also provides a practical scientific framework for its broader engineering application. Full article

To achieve the optimal energy allocation between the auxiliary power unit (APU) and battery of connected automated range-extended electric vehicle (CAR-EEV), the hierarchical eco-driving control with dynamic game energy management were investigated and the optimization design of APU working mode was carried out from a multi-objective perspective. Initially, the acceleration and speed of the host vehicle were adjusted in real time, based on the driving status of the preceding vehicle, and the ecological driving speed was obtained in the adaptive car-following eco-driving mode. The dynamic game energy management strategy was proposed, leveraging the real-time interactive information between the vehicle and the traffic environment, and intelligently allocating and scheduling the energy flow within the powertrain. Dynamic game optimization was adopted to achieve dynamic decision-making and control optimization on whether to switch the APU operating speed or not. The multi-objective optimization analyses are carried out based on the weight coefficient matrix. The hierarchical dynamic game energy management strategy based on eco-driving speed (HDGEMS) is implemented through dynamic games and exhibits excellent performance. This strategy enables dynamic adjustment of power distribution between the APU and the battery, thereby allowing the APU to operate efficiently under optimal operating conditions. Meanwhile, it effectively reduces secondary charging losses and the dynamic switching time of the APU, and ultimately achieves energy optimization. Eventually, the results of simulation and experimental thoroughly indicated that economy improvement, emission reduction, and battery life enhancement of CAR-EEV were effectively kept in balance under the control of the proposed HDGEMS with intelligent optimization mode. New research ideas and technical directions are provided for the field of EMS, which is expected to promote technological progress in the industry. Full article

Urban air pollution caused by vehicular emissions remains one of the most pressing environmental challenges, negatively affecting both public health and climate processes. In Kokshetau, Kazakhstan, where electric vehicle (EV) adoption accounts for only 0.019% of the total fleet and charging infrastructure is nearly absent, reducing transport-related emissions requires short-term and cost-effective solutions. This study proposes an integrated approach combining urban ecology principles with computational modeling to optimize traffic signal control for emission reduction. An artificial neural network (ANN) was trained using intersection-specific traffic data to predict emissions of carbon monoxide (CO), nitrogen oxides (NO_x), sulfur dioxide (SO₂), and particulate matter (PM_2.5). The ANN was incorporated into a nonlinear optimization framework to determine traffic signal timings that minimize total emissions without increasing traffic delays. The results demonstrate reductions in emissions of CO by 12.4%, NO_x by 9.8%, SO₂ by 7.6%, and PM_2.5 by 10.3% at major congestion hotspots. These findings highlight the potential of the proposed framework to improve urban air quality, reduce ecological risks, and support sustainable transport planning. The method is scalable and adaptable to other cities with similar urban and environmental characteristics, facilitating the transition toward eco-friendly mobility and integrating data-driven traffic management into broader climate and public health policies. Full article

Biopolymers have recently been introduced as eco-friendly alternatives to other chemical cementitious additives for sandy soil stabilization, especially in pavement construction. The resilient modulus (M_R) is a key metric considered in the mechanistic design of pavement layers that ensures a safe and economic design based on guaranteed accurate values. This study investigated the effects of dehydration on the M_R of biopolymer-treated sand. Prepared specimens were subjected to two different curing conditions. The first set underwent closed-system curing (CSC) for periods of 7, 14, and 28 days. The second set of specimens was cured at different levels of suction by controlling relative humidity (RH) using different salt solutions (0.27, 1.0, 9.7, 21.0, 54.6, 113.7, and 294 MPa), referred to as dehydration curing (DC). The soil water retention curve (SWRC) was measured over the entire suction range to evaluate the dehydration curing and to link the results of suction levels and dehydration regime. M_R tests were conducted on both sets of specimens using a dynamic triaxial system to simulate different confining, traffic, and dynamic stresses. The results showed a significant increase in M_R (i.e., up to eight times) for specimens cured under DC conditions that was proportional to the suction level across different zones of the SWRC. Scanning electron microscopy revealed a phase change from hydrogel to film, which enhanced cementation and bonding between particles. in addition, CSC treatment resulted in a 10–30% reduction in M_R. A new regression model is proposed to predict the M_R of biopolymer-treated sand as a function of confining stresses, dynamic stresses, and suction. These findings will assist pavement engineers and designers in achieving safe, sustainable, and economic designs. Full article

To address environmental pollution issues and optimize the utilization of waste biomass resources, this study proposes a novel eco-friendly sound-absorbing material based on maple leaf waste and tests its sound absorption performance. The fibers were extracted from maple leaf waste through a wet decomposition and grinding process. Metallurgical microscopy was employed to observe the microstructural characteristics of maple leaf fibers to identify the potential synergistic effect. The effects of two key factors—sample thickness and mass density—on sound absorption performance were investigated. The sound absorption coefficients were measured using the transfer function method in a dual-microphone impedance tube to evaluate their sound-absorbing performance. Experimental results demonstrate that the prepared maple leaf fibers, as acoustic materials, exhibit excellent acoustic performance across a wide frequency range, with an average sound absorption coefficient of 0.7. Increasing sample thickness improves the sound absorption coefficient in low- and mid-frequency ranges. Additionally, increased sample mass density was found to enhance acoustic performance in low- and mid-frequency bands. This study developed an eco-friendly material with lightweight and efficient acoustic absorption properties using completely biodegradable maple leaf waste. The results provide high-performance, economical, and ecologically sustainable solutions for controlling building and traffic noise while promoting the development of eco-friendly acoustic materials. Full article

This study proposes a Cooperative Traffic Controller System (CTS), an urban eco-network control system that leverages Multi-Agent Reinforcement Learning (MARL), to address urban road congestion and environmental pollution. The proposed system synergizes traffic signal timing optimization and speed guidance control, simultaneously enhancing network efficiency, reducing carbon emissions, and minimizing energy consumption. A Beta-enhanced Soft Actor-Critic (SAC) algorithm is applied to achieve the joint optimization of the traffic signal phasing and vehicle speed coordination. Experimental results show that in large-scale networks, the improved SAC reduces the average delay time per vehicle by approximately one minute, reduces CO₂ emissions by more than double, and reduces fuel consumption by 56%. Comparative analyses of the algorithm versus the PPO and standard SAC demonstrate its superior performance in complex traffic scenarios—specifically in congestion mitigation and emissions reduction. Full article

Rehabilitation plans are based on pavement condition assessments, which are crucial to modern pavement management systems. However, some of the disadvantages of conventional approaches for road maintenance and repair include the time consumption, high costs, visual errors, seasonal limitations, and low accuracy. Continuous and efficient pavement monitoring is essential, necessitating reliable equipment that can function in a variety of weather and traffic conditions. UAVs offer a practical and eco-friendly alternative for tasks including road inspections, dam monitoring, and the production of 3D ground models and orthophotos. They are more affordable, accessible, and safe than traditional field surveys, and they reduce the environmental effects of pavement management by using less fuel and producing less greenhouse gas emissions. This study uses UAV technology in conjunction with ground control points (GCPs) to assess the kind and amount of damage in flexible pavements. Vertical photogrammetric mapping was utilized to produce 3D road models, which were then processed and analyzed using Agisoft Photoscan (Metashape Professional (64 bit)) software. The sorts of fractures, patch areas, and rut depths on pavement surfaces may be accurately identified and measured thanks to this technique. When compared to field exams, the findings demonstrated an outstanding accuracy with errors of around 3.54 mm in the rut depth, 4.44 cm² for patch and pothole areas, and a 96% accuracy rate in identifying cracked locations and crack varieties. This study demonstrates how adding GCPs may enhance the UAV image accuracy, particularly in challenging weather and traffic conditions, and promote sustainable pavement management strategies by lowering carbon emissions and resource consumption. Full article

This study proposes an AI-powered digital twin (DT) platform designed to support real-time traffic risk prediction, decision-making, and sustainable mobility in smart cities. The system integrates multi-source data—including static infrastructure maps, historical traffic records, telematics data, and camera feeds—into a unified cyber–physical platform. AI models are employed for data fusion, anomaly detection, and predictive analytics. In particular, the platform incorporates telematics–video fusion for enhanced trajectory accuracy and LiDAR–camera fusion for high-definition work-zone mapping. These capabilities support dynamic safety heatmaps, congestion forecasts, and scenario-based decision support. A pilot deployment on Madison’s Flex Lane corridor demonstrates real-time data processing, traffic incident reconstruction, crash-risk forecasting, and eco-driving control using a validated Vehicle-in-the-Loop setup. The modular API design enables integration with existing Advanced Traffic Management Systems (ATMSs) and supports scalable implementation. By combining predictive analytics with real-world deployment, this research offers a practical approach to improving urban traffic safety, resilience, and sustainability. Full article

To address the needs of enhancing adaptive control and reducing emissions at intersections within intelligent traffic signal systems, this study innovatively proposes a deep reinforcement learning signal control model tailored for mixed traffic flows. Addressing shortcomings in existing models that overlook mixed traffic scenarios, neglect optimization of CO₂ emissions, and overly rely on high-performance algorithms, our model utilizes vehicle queue length, average speed, numbers of gasoline and electric vehicles, and signal phases as state information. It employs a fixed-phase strategy to decide between maintaining or switching signal states and incorporates a reward function that balances vehicle CO₂ emissions and waiting times, significantly lowering intersection carbon emissions. Following training with reinforcement learning algorithms, the model consistently demonstrates effective control outcomes. Simulation results using the SUMO platform reveal that our designed reward mechanism facilitates the rapid and stable convergence of intelligent agents. Compared with Fixed Time Control (FTC), Actuated Traffic Signal Control (ATSC), and Fuel-ECO TSC (FECO-TSC) methods, our model achieves superior performance in average waiting times and CO₂ emissions. Even across scenarios with gasoline–electric vehicle ratios of 25–75%, 50–50%, and 75–25%, the model exhibits significant advantages. These simulations validate the model’s rationality and effectiveness in promoting low-carbon travel and efficient signal control. Full article

Cooperative driving systems can coordinate individual vehicles on the road in a platoon, holding significant promise for enhancing traffic efficiency and lowering the energy consumption of vehicle movements. For an extended period, vehicles on the road will consist of a mix of traditional gasoline and electric vehicles. To explore the economic driving strategies for diverse vehicles on the road, this paper introduces a collaborative eco-driving system that takes into account the energy consumption traits of vehicles. Unlike prior research, this paper puts forward a lane change decision-making approach that integrates energy modeling and speed prediction. This method can effectively capture the speed variations in the vehicle ahead and facilitate lane changes with energy efficiency in mind. The system encompasses three vital functions: vehicle cooperative architecture, ecological trajectory planning, and power system control. Specifically, eco-speed planning is carried out in two stages: the initial stage is executed globally, with cooperative speed optimization performed based on the energy consumption characteristics of different vehicles to determine the economical speed for vehicle platoon driving. The subsequent stage involves local speed adaptation, where the vehicle platoon dynamically adjusts its speed and makes lane change decisions according to local driving conditions. Ultimately, the generated control information is fed into the powertrain control system to regulate the vehicle. To assess the proposed collaborative eco-driving system, the algorithms were tested on highways, and the results substantiated the system’s efficacy in reducing the energy consumption of vehicle driving. Full article

Predicting Signal Phase and Timing (SPaT) information and confidence levels is needed to enhance Green Light Optimal Speed Advisory (GLOSA) and/or Eco-Cooperative Adaptive Cruise Control (Eco-CACC) systems. This study proposes an architecture based on transformer encoders to improve prediction performance. This architecture is combined with different deep learning methods, including Multilayer Perceptrons (MLP), Long-Short-Term Memory neural networks (LSTM), and Convolutional Long-Short-Term Memory neural networks (CNNLSTM) to form an ensemble of predictors. The ensemble is used to make data-driven predictions of SPaT information obtained from traffic signal controllers for six different intersections along the Gallows Road corridor in Virginia. The study outlines three primary tasks. Task one is predicting whether a phase would change within 20 s. Task two is predicting the exact change time within 20 s. Task three is assigning a confidence level to that prediction. The experiments show that the proposed transformer-based architecture outperforms all the previously used deep learning methods for the first two prediction tasks. Specifically, for the first task, the transformer encoder model provides an average accuracy of 96%. For task two, the transformer encoder models provided an average mean absolute error (MAE) of 1.49 s, compared to 1.63 s for other models. Consensus between models is shown to be a good leading indicator of confidence in ensemble predictions. The ensemble predictions with the highest level of consensus are within one second of the true value for 90.2% of the time as opposed to those with the lowest confidence level, which are within one second for only 68.4% of the time. Full article

As a fundamental part of water management, water sampling treatments have recently been integrated into unmanned aerial vehicle (UAV) technologies and offer eco-friendly, cost-effective, and time-saving solutions while reducing the necessity for qualified staff. However, the majority of applications have been conducted with rotary-wing configurations, which lack range and sampling capacity (i.e., payload), leading scientists to search for alternative designs or special configurations to enable more comprehensive water assessments. Hence, in this paper, the conceptual design of a novel long-range and high-capacity WIGE UAV capable of autonomous water sampling is presented in detail. The design process included a vortex lattice solver for aerodynamic investigations, while analytical and empirical methods were used for weight and dimensional estimations. Since the mission involved operation inside maritime traffic, potential obstacle avoidance scenarios were discussed in terms of operational safety, and the aim was for autonomous trajectory tracking performance to be improved by means of a stochastic optimization algorithm. For this purpose, an artificial intelligence-integrated concurrent engineering approach was applied for autonomous control system design and flight altitude determination, simultaneously. During the optimization, the stability and control derivatives of the constituted longitudinal and lateral aircraft dynamic models were predicted via a trained artificial neural network (ANN). The optimization results exhibited an aerodynamic performance enhancement of 3.92%, and a remarkable improvement in trajectory tracking performance for both the fly-over and maneuver obstacle avoidance modes, by 89.9% and 19.66%, respectively. Full article