Search Results (8,560)

The integration of electric buses into urban transportation networks is a priority for policymakers aiming to promote sustainable public mobility. Among available technologies, electric Bus Rapid Transit (eBRT) systems offer an environmentally friendly and operationally effective alternative to conventional modes. This study introduces a Machine Learning Decision Support Framework designed to assess the feasibility of deploying eBRT systems in urban environments. Using a dataset of 28 routes in the Athens Metropolitan Area, the framework integrates diverse variables such as land use, population coverage, proximity to public transport, points of interest, road characteristics, and safety indicators. The XGBoost model demonstrated strong predictive performance, outperforming traditional approaches and highlighting the significance of points of interest, land use diversity, green spaces, and roadway infrastructure in forecasting travel times. Overall, the proposed framework provides urban planners and policymakers with a robust, data-driven tool for evaluating the practical and environmental viability of eBRT systems. Full article

To address the issue of low efficiency in recovering low-frequency vibration energy during vehicle operation, this paper proposes a piezoelectric energy capture harvester based on wheel vibration. The device employs a parallel configuration of dual cantilever beam piezoelectric transducers in its mechanical structure, with additional mass blocks to optimize its resonant characteristics in the low-frequency range. A synchronous switch energy harvesting circuit was designed. By actively synchronizing the switch with the peak output voltage of the piezoelectric element, it effectively circumvents the turn-on voltage threshold limitations of diodes in bridge rectifier circuits, thereby enhancing energy conversion efficiency. A dynamic model of this device was established, and multiphysics simulation analysis was conducted using COMSOL-Multiphysics to investigate the modal characteristics, stress distribution, and output performance of the energy harvester. This revealed the influence of the piezoelectric vibrator’s thickness ratio and the mass block’s weight on its power generation capabilities. Experimental results indicate that under 20 Hz, 12 V sinusoidal excitation, the system achieves an average output power of 3.019 mW with an average open-circuit voltage reaching 16.70 V. Under simulated road test conditions at 70 km/h, the output voltage remained stable at 6.86 V, validating its feasibility in real-world applications. This study presents an efficient and reliable solution for self-powering in-vehicle wireless sensors and low-power electronic devices through mechatronic co-design. Full article

Automotive components’ exposure to prolonged random loading not only accumulates fatigue damage but also causes material stiffness degradation. The degradation of material mechanical properties leads to stress redistribution within the structure, which in turn affects the structural fatigue life. Conventional frequency-domain fatigue life analysis methods often fail to take into account performance degradation, whereas time-domain approaches are constrained by computational inefficiency in dynamic response calculations. To address this, a time-domain fatigue life analysis is proposed, integrating Long Short-Term Memory (LSTM) networks with performance degradation modeling. First, short-term dynamic response data of engineering structures that contain stiffness degradation parameters are utilized to establish a training set, and an LSTM surrogate model is trained to rapidly predict stress responses in time- and degree-varying structural performance degradation. Second, the time-varying dynamic responses obtained from the LSTM surrogate model are related to the principles the fatigue damage accumulation and Miner’s criterion to quantify the stiffness degradation effects. A computational framework has been developed for fatigue life prediction through iterative alternation between dynamic response calculations and fatigue damage assessments. Case studies on notched plates demonstrate that the LSTM surrogate model approach ensures accuracy while reducing structural fatigue life analysis time by more than three orders of magnitude compared to the finite element method (FEM). Under the application of 20,000s random road loads, the damage value of the reinforced plate obtained by the surrogate model method that takes into account performance degradation is lower by 10–25% compared to that calculated by the frequency-domain or time-domain methods that neglect degradation. 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

Autonomous driving technology is advancing rapidly, particularly in vision-based approaches that use cameras to perceive the driving environment, which is the most human-like perception method. However, one of the key challenges that smart vehicles face is adapting to various weather conditions, which can significantly impact visual perception and vehicular control strategies. The ideal design for the latter is to dynamically adjust in real time to ensure safe and efficient driving, taking into account the prevailing weather conditions. In this study, we propose a lightweight weather perception model that incorporates multi-scale feature learning, channel attention mechanisms, and a soft voting ensemble strategy. This enables the model to capture various visual patterns, emphasize critical information, and integrate predictions across multiple modules for improved robustness. Benchmark comparisons are conducted using several well-known deep learning networks, including EfficientNet-B0, ResNet50, SqueezeNet, MobileNetV3-Large, MobileNetV3-Small, and LSKNet. Finally, using both public datasets and real-world video recordings from roads in Taiwan, our model demonstrates superior computational efficiency while maintaining high predictive accuracy. For example, our model achieves $98.07\%$ classification accuracy with only 0.4 million parameters and 0.19 GFLOPs, surpassing several well-known CNNs in computational efficiency. Compared with EfficientNet-B0, which has a similar accuracy ($98.37\%$) but requires over ten times more parameters and four times more FLOPs, our model offers a much lighter and faster alternative. 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

Natural disasters, particularly floods and landslides, can cause severe losses; however, their impacts can be significantly mitigated through proactive planning. In August 2021, a devastating flood in northern Türkiye resulted in major damage, including the displacement of logs from the Ayancık Forest Management Directorate’s depot, which exacerbated the disaster’s effects. This study aims to identify the most suitable location for a new forest depot in Ayancık, considering disaster risk, logistical needs, and environmental factors. A hybrid geospatial approach was employed by integrating Logistic Regression (LR)-based landslide susceptibility modeling and the Analytic Hierarchy Process (AHP). Key conditioning factors such as altitude, slope, aspect, lithology, land cover, plan and profile curvature, topographic wetness index (TWI), distance to drainage networks, roads, and faults were used to produce the LSM. The AHP weights of the factors used in selecting a suitable depot location were determined based on expert opinions. The integration of physical, logistical, and risk-based parameters allowed for a spatial prioritization of suitable areas. Results indicate that approximately 10.69% of the study area is classified as class 1 (very high suitability), 16.59% as class 2 (high), 20.71% as class 3 (moderate), 23.34% as class 4 (low), and 28.67% as class 5 (very low), corresponding to 27.28% of the area in classes 1–2 and 52.01% in classes 4–5. These results indicate that the study area is predominantly characterized by medium-low suitability conditions. Notably, these areas show significantly lower flood and landslide susceptibility compared to the current depot sites. By aligning forest infrastructure planning with disaster resilience principles, this study offers a replicable model for sustainable forest depot site selection. The findings provide valuable guidance for forest managers and policymakers to enhance the safety, functionality, and long-term viability of forestry operations in hazard-prone regions. Full article

Landslides are among the most significant disasters that threaten communities worldwide. This study sampled 384 respondents, using standardized interviews and field observations, to analyze how they perceived the factors influencing the incidence of landslides in the Kivu catchment of Rwanda, especially in landslide-prone areas. This study employs a mixed-methods approach that combines household surveys and interviews with key informants to assess how residents perceive landslide causes, warning signs, and impacts, which were analyzed statistically using SPSS. For further analysis, a binary logistic regression model and chi-square tests were used. The chi-square test findings highlighted that heavy rainfall, inappropriate agricultural practices, steep slopes, deforestation, road construction, earthquakes, and climate change were strongly correlated with landslide occurrence, with a p < 0.05 level of significance, while mining activities were not correlated with landslides. On the other hand, a binary logistic regression model revealed that, among the selected factors influencing landslide occurrence in the Kivu catchment, road construction (B = −0.644; p = 0.014), inappropriate agriculturalpractices (−1.177; p = 0.000), steep slopes (B = −0.648; p = 0.018), deforestation (B = −0.854; p = 0.007), and earthquakes (B = −1.59; p = 0.008) were negatively correlated, while heavy rainfall (B = 1.686; p = 0.000) and climate change (B = 1.784; p = 0.001) were positively correlated, and this was statistically significant for landslide occurrence at a p-value < 0.05. In contrast, mining activities (B = −0.065; p = 0.917) showed a negative coefficient that was statistically insignificant with respect to landslide occurrence in the study area. Future studies should integrate surveys with landslide hazard modeling tools for better spatial prediction of vulnerability and economic losses. Therefore, the findings from this study will contribute to sustainable natural disaster management planning in the western region of Rwanda. Full article

Mobile Mapping Systems (MMSs) have emerged as indispensable instruments for producing high-precision road maps in recent years. Despite incorporating modern devices, their efficacy may be influenced by operational variables such as vehicle speed or the type of GNSS (Global Navigation Satellite System) differential correction employed. This study assesses the impact of varying vehicle speeds and differential correction settings on the accuracy of point grids acquired with an MMS on a two-lane rural road. The experiment was performed across a 7 km distance, incorporating two speeds (40 and 60 km/h) and two travel directions. Three correction methodologies were examined: a proximate local base (MBS), a network station solution of the National Geographic Institute (NET), and virtual reference stations (VRSs). The methodology encompassed normality analysis, descriptive statistics, mean comparisons, one- and two-factor analysis of variance (ANOVA), and the computation of the root mean square error (RMSE) as a measure of accuracy. The findings indicate that horizontal discrepancies remain steady and unaffected by the correction technique; however, notable changes are seen in the vertical component, with the NET option proving to be the most effective. The acquisition rate is the primary determinant, exacerbating errors at 60 km/h. In conclusion, the dependability of MMS surveys is contingent upon the correction approach and operational conditions, and it is advisable to sustain moderate speeds to guarantee precise three-dimensional models. Full article

Rural Sub-Saharan Africa (SSA) faces limited transport options, with many dispersed settlements dependent on poorly maintained roads. Light delivery vehicles (LDVs) can improve mobility, but conventional internal combustion engine vehicles are costly to operate and contribute to emissions. Electric vehicle (EV) conversions offer a practical alternative by extending vehicle life and reducing energy, maintenance, and environmental costs. This study presents a simulation-based framework to guide LDV conversion design for rural SSA. The framework includes component sizing, subsystem modeling, and full-vehicle benchmarking under representative conditions. Scenario-based simulations include trips ranging from shorter local access routes to longer remote trips on both paved and dirt roads, allowing the conversion’s performance to be quantified under representative conditions. A sensitivity analysis indicates that road grade, aerodynamic drag, and rolling resistance are the primary factors driving energy use variation. Using the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) drive cycle, the conversion energy consumption (∼217 Wh/km) comparable to that of commercial electric vans, though the range is reduced relative to its battery capacity. The framework establishes a benchmark for EV conversion performance in SSA and supports broader adoption of sustainable rural mobility solutions. Full article

This study investigates the impact of roadside building development and vehicle exhaust emissions on atmospheric deterioration in urban highway areas. By integrating satellite-based building coverage data with an equal-pollution vehicle conversion method (based on human toxicity potential), we establish a computational fluid dynamics framework to simulate pollutant dispersion. Key results reveal the following: (1) Street canyon morphology, particularly its geometric symmetry, dominates diffusion patterns. Wide canyons (aspect ratio = 3.3) reduce CO accumulation by over 30% compared to deep canyons (aspect ratio = 0.3), highlighting the role of built form in regulating pollution distribution. (2) Under idealized conditions, photocatalytic pavement mitigates pollutant concentrations at human breathing height by 28.7–56.7%, demonstrating the potential of uniformly applied material solutions. These findings provide a validated theoretical basis for optimizing urban road design and evaluating environmental policies, with considerations for spatial layout and material treatment. Full article

Public transport still uses vehicles powered by fossil fuels. Replacing the fleet with zero-emission vehicles will take many years. During this period, it is still necessary to carry out work aimed at reducing energy consumption and thus the emission of toxic substances into the atmosphere. An important part of this work is the study of the relationship between energy demand of buses with different power plants and urban traffic conditions. These conditions include traffic intensity, average and maximum speeds, and number of stops. The VSP (Vehicle-Specific Power) model is useful in research on this relationship. In this article, such research was carried out using data from public bus monitoring and data provided by the city authorities of Rzeszów. In the first stage, a VSP model was created and tuned for three buses with different power plants operating on selected routes. Then, as a result of a large number of simulation processes, the impact of the average speed on the energy demand was determined. The results of the conducted research can be used in the modernization or planning of public transport networks and the modification of road infrastructure. All these activities should contribute to reducing energy consumption and environmental pollution. 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

This study investigated the occurrence, sources, and health risks of 16 polycyclic aromatic hydrocarbons (PAHs) in commonly consumed fish and crayfish from the Caohai Lake, a typical plateau lake in southwest China. Four dominant species (crucian carp, common carp, yellow catfish, and crayfish) were collected and analyzed. The results showed a generally low level of PAH contamination (mean: 26.7 μg/kg wet weight), with bioaccumulation tendency decreasing as the number of PAH rings increased. Crayfish exhibited the highest total concentration of PAHs, whereas yellow catfish accumulated the most carcinogenic PAHs. Positive matrix factorization (PMF) model identified four primary sources—petroleum leakage, coal combustion, traffic emissions, and biomass burning—with petroleum-derived PAHs being the most significant contributor. The assessment of health risk indicated that while the average hazard index (HI) was below 1, approximately 10% of the samples posed a potential non-carcinogenic risk, particularly from crayfish and yellow catfish. The incremental lifetime cancer risk (ILCR) for DahA, BaP, BaA, and BbF all exceeded the negligible risk level of 10⁻⁶ but remained below 10⁻⁴. Notably, the mean total ILCR (TILCR) approached 10⁻⁴, with yellow catfish presenting the highest carcinogenic risk, highlighting concerns of the carcinogenic risk of PAHs. Source-oriented risk assessment revealed that petroleum leakage was the dominant contributor to non-carcinogenic risk (>55%), while traffic emissions contributed most to carcinogenic risk (>57%). To mitigate carcinogenic risk, implementing stormwater diversion systems along the circular lakeside roads is recommended to reduce the input of traffic-derived PAHs. Full article

This study presents a scenario-based modeling framework to evaluate potential decarbonization pathways for Portugal’s road transport sector. The model simulates the evolution of a light-duty vehicle (LDV) fleet under varying degrees of electrification and biofuel integration, accounting for energy consumption, CO₂ emissions and market shares of alternative propulsion technologies. Coupled with projected energy mix trajectories, the framework estimates final energy demand and well-to-wheel (WTW) emissions for each scenario, benchmarking outcomes against national and European climate targets. A key structural limitation identified is the long vehicle survival rate—averaging 14 years—which constrains fleet renewal and delays the transition to full electrification. Diesel-powered light commercial vehicles exhibit even slower replacement dynamics, rendering the Portuguese targets of full electrification by 2030 highly improbable without targeted scrappage and incentive programs. Scenario analysis indicates that even with accelerated electric vehicle (EV) uptake, battery electric vehicles (BEVs) would comprise only 12% of the fleet by 2030 and 77% by 2050. Electrification scenario raises electricity demand fortyfold by 2050, stressing generation and infrastructure. Scenarios that consider diversification of energy sources reduce this strain but require triple electricity for large-scale green hydrogen and synthetic fuel production. Full article