Search Results (354)

This study analyzed the impact of human drivers’ Keep Right Rule noncompliance on sustainable freeway operations in mixed traffic. Using the microscopic traffic simulation tool, a total of 36 scenarios were examined based on variations in driving behavior, presence of slow vehicles in the passing lane, desired speed, and number of lanes. The Wiedemann-99 car-following model and autonomous driving logic were applied for simulation. Simulation results revealed that the occupation of the passing lane by a human-driven slow vehicle increased the recovery time and variability in right-side rule compared to free lane selection. Also, 20 km/h was a threshold desired speed gap that activated the bottleneck by the slow vehicle in a passing lane. Lastly, as the number of lanes increased, bottleneck formation was diminished. The findings point to a mixed traffic systemic paradox. Human drivers can alleviate bottleneck formation by flexibly performing right-side overtaking even though it is illegal, whereas autonomous vehicles cannot perform right-side overtaking, which unintentionally activates a bottleneck under strict rule compliance. These results show that in mixed traffic conditions, even minor violations of traffic rules by human drivers can lead to congestion. Therefore, to achieve sustainable and safe road traffic by harmonizing mixed traffic, institutional improvements are necessary alongside advances in autonomous driving technology. Full article

The analysis of road traffic accidents often reveals asymmetric patterns, providing insights that support the development of preventive measures, reduce fatalities, and improve road safety interventions. The Rayleigh distribution, a continuous distribution with inherent asymmetry, is well suited for modeling right-skewed data and is widely used in scientific and engineering fields. It also shares structural characteristics with other skewed distributions, such as the Weibull and exponential distributions, and is particularly effective for analyzing right-skewed accident data. This study considers several approaches for constructing confidence intervals, including the percentile bootstrap, bootstrap with standard error, generalized confidence interval, method of variance estimates recovery, normal approximation, Bayesian Markov Chain Monte Carlo, and Bayesian highest posterior density methods. Their performance was evaluated through Monte Carlo simulation based on coverage probabilities and expected lengths. The results show that the HPD method achieved coverage probabilities at or above the nominal confidence level while providing the shortest expected lengths. Finally, all proposed confidence intervals were applied to fatalities recorded during the seven hazardous days of Thailand’s Songkran festival in 2024 and 2025. Full article

Dryland grasslands face elevated risks of rapid threshold crossing under a regime of warming, precipitation redistribution, and intensified interannual hydrothermal variability. Using the Ebinur Lake Basin (ELB) as a case, we developed an integrated structure × function assessment—linking land-use/cover change (LUCC) transitions with functional indicators of net primary productivity (NPP), net ecosystem production (NEP), soil conservation (SC), and grass supply (GS)—and coupled it with Bayesian-optimized XGBoost, SHAP, and partial dependence plots (PDPs) at a 30 m pixel scale to identify dominant drivers and ecological thresholds, subsequently translating them into governance zones. From 2003 to 2023, overall grassland status was dominated by degradation (20,160.62 km²; 69.42%), with restoration at 8878.85 km² (30.57%) and stability at 2.79 km² (0.01%). NPP/NEP followed a rise–decline–recovery trajectory, while SC exhibited marked bipolarity. Precipitation and temperature emerged as primary drivers (interaction X3 × X4 = 0.0621), whose effects, together with topography and accessibility, shaped a spatial paradigm of piedmont sensitive–oasis sluggish–lakeshore vulnerable. Key thresholds included an annual precipitation recovery threshold of ~200 mm and an optimal window of 272–429 mm; a road-density divide near ~0.06 km km⁻²; and sustainable grazing windows of ~2.2–4.2 and ~4.65–5.61 livestock units (LU) km⁻². These thresholds underpinned four management units—Priority Control (52.53%), Monitoring and Alert (21.53%), Natural Recovery (20.40%), and Optimized Maintenance (5.55%)—organized within a “two belts–four zones–one axis” spatial framework, closing the loop from threshold detection to adaptive governance. The approach provides a replicable paradigm for climate-adaptive management and ecological risk mitigation of dryland grasslands under warming. Full article

Electric vehicles rely on regenerative braking as a means of improving energy efficiency and extending driving range. However, the optimization of torque distribution between regenerative and mechanical braking remains a challenging aspect. This study investigates machine learning techniques for predicting braking torque in light EVs with a view to improving energy recovery and reducing mechanical brake usage. For this purpose, a simulation model was developed in MATLAB/Simulink to generate a data set of 113,622 points based on speed, acceleration, road grade, vehicle weight, and road condition. Four supervised ML algorithms—Linear Regression, K-Nearest Neighbors, Decision Tree, and Random Forest—were trained and evaluated using R², MSE, RMSE, and MAE metrics. To verify the results under WLTP Class 1 driving conditions, a test was conducted on a hardware test platform for the best model. The findings indicate that Random Forest achieved the highest level of accuracy with an R² value of 0.97 in the simulation and an R² value of 0.98 in the experimental validation. These findings support the hypothesis that ML-based torque prediction is a promising approach for real-time EV braking control. Also, this study supports sustainable transportation by improving energy recovery and reducing environmental impact through advanced AI-based braking strategies. Full article

Multi-axle electric heavy-duty trucks face significant challenges in maintaining braking stability and achieving real-time control during regenerative braking due to their large mass and complex inter-axle coupling dynamics. To address these issues, this paper proposes an improved model predictive control (IMPC) strategy that enhances computational efficiency and control responsiveness through an instantaneous response mechanism. The approach integrates a first-order error attenuation term within the MPC framework and employs an extended Kalman filter to estimate tire–road friction in real time, enabling adaptive adjustment between energy recovery and stability objectives under varying road conditions. A control barrier function constraint is further introduced to ensure smooth and safe regenerative braking. Simulation results demonstrate improved energy recovery efficiency and faster convergence, while real-vehicle tests confirm that the IMPC maintains superior real-time performance and adaptability under complex operating conditions, reducing average computation time by approximately 14% compared with conventional MPC and showing strong potential for practical deployment. Full article

The resilience of urban transportation networks refers to the system’s ability to resist, absorb, and recover performance when facing external shocks. Traditional methods have obvious limitations in temporal granularity, data fusion, and predictive capabilities. To address this, this study proposes a minute-level real-time resilience measurement model driven by ride-hailing big data. First, the spatio-temporal characteristics of urban ride-hailing data are analyzed, and a transportation cost indicator is introduced to construct a multidimensional road network resilience measurement framework encompassing transport supply–demand, efficiency, and cost. Second, a high-precision hybrid LSTM-Transformer prediction model integrating spatio-temporal attention mechanism is developed, and a time-varying node identification method based on RMSE curves is proposed to accurately capture the disturbance onset time and recovery completion time. Finally, empirical validation shows that, taking Taixing City as an example, the model achieves minute-level resilience measurement with an average prediction accuracy of 96.8%, making resilience assessment more precise and sensitive. The research results provide a scientific basis for urban traffic management departments to formulate emergency response strategies and improve road network recovery efficiency. Full article

Traditional path planning methods primarily optimize distance or time, without fully considering the impact of slope gradients in park road networks, variations in vehicle load capacity, and braking energy recovery characteristics on the energy consumption of pure electric commercial vehicles. To address these issues, this paper proposes a globally optimized path planning method based on energy consumption minimization. The proposed method introduces a multi-factor coupled energy consumption model for pure electric commercial vehicles, integrating slope gradients, load capacity, motor efficiency, and energy recovery. Using this vehicle energy consumption model and the park road network topology map, an energy consumption topology map representing energy consumption between any two nodes is constructed. An energy-optimized improved ant colony optimization algorithm (E-IACO) is proposed. By introducing an exponential energy consumption heuristic factor and an enhanced pheromone update mechanism, it prioritizes energy-saving path exploration, thereby effectively identifying the optimal energy consumption path within the constructed energy consumption topology map. Simulation results demonstrate that in typical three-dimensional industrial park scenarios, the proposed energy-optimized path planning method achieves maximum reductions of 10.57% and 4.90% compared to the A* algorithm and ant colony optimization (ACO), respectively, with average reductions of 5.14% and 1.97%. It exhibits excellent stability and effectiveness across varying load capacities. This research provides a reliable theoretical framework and technical support for reducing logistics operational costs in industrial parks and enhancing the economic efficiency of pure electric commercial vehicles. Full article

We developed a robust tri-platform co-simulation framework that integrates Simulink, AMESim, and RecurDyn to address the dynamic inconsistencies observed in traditional tensioning models for tracked vehicles. The proposed framework synchronizes nonlinear hydraulic dynamics, closed-loop control, and track–ground interactions within a unified time step, thereby ensuring causal consistency along the pressure–flow–force–displacement power chain. Five representative operating conditions—including steady tension tracking, random road excitation, steering/braking pulses, supply-pressure drops, and parameter perturbations—were analyzed. The results show that the tri-platform model reduces tracking error by up to 60%, shortens recovery time by 35%, and decreases energy consumption by 12–17% compared with dual-platform models. Both simulations and full-scale experiments confirm that strong cross-domain coupling enhances system stability, robustness, and energy consistency under variable supply pressure and parameter uncertainties. The framework provides a high-fidelity validation tool and a transferable modeling paradigm for electro-hydraulic actuation systems in tracked vehicles and other multi-domain machinery. Full article

Occupational demands, such as load carriage in tactical professions, do not discriminate based on sex. The aim of this study was to explore the differences in metabolic cost of a loaded pack march between the sexes in both absolute and relative terms. Twelve Army personnel (six males and six females) volunteered to complete three identical load carriage marches (5 km at 5.5 km/h, carrying 30 kg), across flat (on road) and undulating (gravelled path) terrain as part of a larger equipment trial. Heart rate (HR) response (HR average and maximum) was monitored with a Polar Team Pro unit and oxygen consumption with VO Master Pro (VO₂ average and maximum) with the level of significance set at 0.05. There were no significant differences in age, years of experience, absolute loads carried, or completion time for each of the three events. Male soldiers were significantly taller (182.3 ± 6.2 cm vs. 167.4 ± 6.9 cm), heavier (88.2 ± 8.7 kg vs. 70.9 ± 10.6 kg), carried significantly less relative load (34.3 ± 3.4% vs. 43.2 ± 7.5%), and had significantly greater predicted VO_2max (56.7 ± 6.1 mL/kg/min vs. 45.0 ± 2.9 mL/kg/min). A linear mixed model identified a significant main effect of sex on both average HR (β = −1.10) and peak HR (β = −1.27), and on average VO₂ (β = −0.68), but not peak VO₂. While the study was not powered to detect sex differences, the large effect sizes observed suggest meaningful physiological differences warranting further investigation. Female soldiers faced significantly greater metabolic costs when carrying the same loads and moving at the same speed and across the same terrain as their male counterparts. Adequate recovery and pacing strategies should be considered for these events, especially during training. Full article

As critical national energy arteries, long-distance large-scale cross-regional gas transmission networks are characterized by high operating pressures, extensive spatial coverage, and complex topological structures. Thus, the multi-hazard profiles threatening its safety and reliability operation differ significantly from those of local urban gas distribution networks. This research develops a resilience assessment framework capable of quantifying resistance, adaptation, and recovery capacities of such energy systems. The framework establishes performance indicator systems based on design parameters, installation environments, and construction methods for long-distance trunk pipelines and key facilities such as storage facilities. Furthermore, based on complex network theory, the size of the largest connected component and global efficiency of the transmission network are selected as core topological metrics to characterize functional scale retention and transmission efficiency under disturbances, respectively, with corresponding quantification methods proposed. A cross-regional pipeline transmission network within a representative municipal-level administrative region in China is used as a case for empirical analysis. The quantitative assessment results of pipeline and network resilience are analyzed. The research indicates that trunk pipeline resilience is significantly affected by characteristic parameters, the laying environment, and installation methods. It is notably observed that installation methods like jacking and directional drilling, used for road or river crossings, offer greater resistance than direct burial but considerably lower restoration capacity due to the complexity of both the environment and the repair processes, which increases time and cost. Moreover, simulation-based comparison of recovery strategies demonstrates that, in this case, a repair-time-prioritized strategy more effectively enhances overall adaptive capacity and restoration efficiency than a node-degree-prioritized strategy. The findings provide quantitative analytical tools and decision-support references for resilience assessment and optimization of cross-regional energy transmission networks. Full article

Background: Foam rolling has become an increasingly popular self-myofascial release (SMR) technique among athletes to prevent injuries, improve recovery, and increase athletic performance. This study investigated how SMR improves mechanical and movement efficiency in recreational road cyclists. Methods: We conducted an exploratory randomized controlled trial (RCT) to investigate the effects of SMR using a foam roller on biomechanical and physiological performance parameters over a six-month period. A total of 32 male participants, aged 26–57 years, with a mean Body Mass Index (BMI) of 24.0 kg/m² (SD = 2.2), were randomly assigned to either an intervention group (n = 16), which incorporated a standardized SMR program into their post-exercise recovery, or a control group (n = 16), which followed the same cycling protocol without SMR. The training program included heart rate-controlled strength endurance intervals. As the primary target, the variables we investigated included torque effectiveness, leg force symmetry, and pedal smoothness. Secondary measurements included submaximal oxygen uptake (VO₂) as well as bioelectrical variables, which we analyzed using classic, repeated-measures ANOVA models and descriptive statistical methods. Results: The analysis revealed significant interaction effects in favor of the intervention group for torque effectiveness (η²_p = 0.434), leg strength symmetry (η²_p = 0.303), and pedal smoothness (η²_p = 0.993). No significant group × time interactions were found for submaximal VO₂ or bioelectrical parameters. Conclusions: Our findings indicate that foam rolling may serve as an effective adjunct to endurance training by enhancing functional neuromuscular performance in cyclists, particularly in torque control and pedal coordination. Its impact on aerobic efficiency and muscle composition appears to be minimal. The results support theoretical models that attribute SMR benefits to proprioceptive, circulatory, and neuromuscular mechanisms rather than structural tissue adaptations. Full article

Cooperative and Autonomous Vehicle (CAV) platoons offer significant potential for improving road safety, traffic efficiency, and energy consumption, but maintaining precise inter-vehicle spacing and synchronized velocity under disturbances while ensuring string stability remains challenging. This paper presents a fully decentralized two-layer architecture for homogeneous platoons whose identical vehicle dynamics and information flow produce an inherent symmetrical system structure. Operating under a predecessor-following topology with a constant time headway policy, the upper layer generates a smooth velocity reference based on local spacing and relative-velocity errors, while the lower layer employs a two-degree-of-freedom (2-DOF) digital RST controller designed through discrete-time pole placement and sensitivity-function shaping. The 2-DOF structure enables independent tuning of tracking and disturbance-rejection dynamics and provides a computationally lightweight solution suitable for embedded automotive platforms. The paper develops a stability analysis demonstrating internal stability and $L_2$ string stability within this symmetrical closed-loop architecture. Simulations confirm string-stable behavior with attenuated spacing and velocity errors across the platoon during aggressive leader maneuvers and under input disturbances. The proposed method yields smooth control effort, fast transient recovery, and accurate spacing regulation, offering a robust and scalable control strategy for real-time longitudinal motion control in connected and automated vehicle platoons. Full article

The fatigue healing mechanisms of steel slag asphalt concrete remain unclear and involve complex influencing factors. When used as an asphalt pavement material in actual road engineering projects, there is a risk of significant deviations in fatigue life predictions and insufficient stability in long-term service performance. In this study, traditional diabase asphalt concrete was used as a reference. Mix design was carried out for various steel slag asphalt mixtures, where steel slag coarse aggregates partially or entirely replaced diabase coarse aggregates. By using four-point bending fatigue testing, the fatigue life and stiffness modulus recovery capacity of steel slag asphalt concrete were analyzed after simulating low-temperature winter fatigue damage followed by healing at different temperatures (20 °C, 35 °C, 60 °C, and 75 °C). The test results indicated that the addition of steel slag coarse aggregates significantly affected the fatigue life and stiffness modulus of asphalt concrete. The use of coarser steel slag and autoclaved steel slag aggregates was beneficial for improving fatigue life. After experiencing low-temperature fatigue damage, increasing the healing temperature enhanced the modulus recovery effect but had a relatively low effect on life recovery. Overall, the stiffness modulus healing index of steel slag asphalt concrete exceeded 90%, while the fatigue life healing index ranged between 19% and 55%. After five fatigue healing cycles, the total fatigue life can be extended by 1.7 to 2.3 times. A life prediction model under multiple fatigue healing tests can be established using the stiffness modulus healing index and fatigue damage rate. Model predictions and measured results confirmed that the total fatigue healing life of asphalt concrete with the complete replacement of diabase coarse aggregates by steel slag coarse aggregates was greater than that of traditional diabase asphalt concrete. Full article

This study investigates the transferability of measured road-damage data between distinct geographic domains using the YOLOv8s deep-learning framework. A comparative evaluation was performed on two datasets: the locally developed RDD2024_SA (South Africa) and the publicly available RDD2022_India (India). Five training–testing scenarios were designed to analyze intra- and inter-dataset generalization, emphasizing the influence of dataset scale, annotation consistency, and class structure on detection performance. When trained and tested within the same domain, YOLOv8s achieved high accuracy (mAP@0.5 > 0.95), confirming the strength of localized feature learning. However, performance degraded substantially under cross-domain testing, revealing a sensitivity to differences in road texture, illumination, and labeling style. Reducing the number of classes from six to four dominant types improved stability (mAP@0.5 ≈ 0.78) by mitigating annotation noise and class imbalance. Furthermore, a transfer-learning configuration, in which the India-trained model was fine-tuned on 20% of the South-African dataset, achieved mAP@0.5 = 0.86, demonstrating effective recovery of cross-domain detection performance. These findings highlight the importance of domain-aligned data preparation, targeted fine-tuning, and balanced class representation in building robust and transferable AI systems for sustainable, data-driven road maintenance. Full article

Effective pavement repair planning is vital for sustaining performance and minimizing lifecycle costs. At the network level, most agencies still rely on deterministic repair-effect assumptions, where repair outcomes are defined by fixed restoration values derived from experience or experimental averages. However, such assumptions often deviate from actual field performance, leading to overestimated repair efficiency and suboptimal investment decisions. This study develops a framework that integrates stochastic repair effects estimated from historical repair data using a probabilistic model for estimating repair effects. The effects of different repairs are represented as probability distributions derived from the latent-variable projection of stochastic deterioration hazard functions, which define the repair transition probabilities. These stochastic transitions are embedded within a Markov Decision Process to optimize the selection of repair types according to condition state, repair effect, cost, and serviceability thresholds, all within a constrained budget. The framework’s application to Addis Ababa’s 150 km urban road network resulted in a five-year optimal strategy that prioritized cost-effective treatments, such as patching, leading to an improvement in network serviceability from 65.7% to 81.2% at a total cost of USD 11.12 million. A comparative analysis of the deterministic restoration approach, commonly used by the agency, overestimated network-level performance by approximately 19%, as it ignored the variability of recovery captured by the stochastic model. Hence, the proposed stochastic framework enables agencies to achieve realistic, data-driven, and sustainable repair optimization, avoiding overestimation of repair benefits while maintaining serviceability within budget constraints. Full article