Search Results (871)

Understanding the dose of ambient PM_2.5 inhaled by middle school students during active commuting between home and school is essential for optimizing their travel routes and reducing associated health risks. However, accurately modeling this remains challenging due to the difficulty of measuring ambient PM_2.5 concentrations along commuting routes at a population scale. In this study, we overcome this limitation by employing spatiotemporally dense observations of on-road ambient PM_2.5 concentrations collected through a massive mobile monitoring fleet consisting of around 200 continuously operating taxis installed with air quality monitoring instruments. Leveraging these rich on-road PM_2.5 observations combined with a GIS-terrain-based PM_2.5 dosage modeling approach, we (1) assess middle school students’ PM_2.5 dosages during morning (7:00 am–8:00 am) home–school walking commuting along the shortest-distance route; (2) examine the feasibility of identifying an alternative route for each student that minimizes PM_2.5 dosages during commuting; (3) investigate the trade-off between the relative reduction in PM_2.5 dosage and the relative increase in route length when opting for the alternative lowest-dosage route; and (4) examine whether exposure inequalities exist among students of different family socioeconomic statuses (SES) during their home–school commutes. The results show that (1) 18.8–57.6% of the students can reduce the dose of PM_2.5 by walking along an alternative lowest-dose route; (2) an alternative lowest-dose route could be found by walking along a parallel, less-polluted local road or walking on the less-trafficked side of the street; (3) seeking an alternative lowest-dose route offers a favorable trade-off between effectiveness and cost; and (4) exposure inequities do exist in a portion of students’ walking commutes and those students from higher-SES are more likely to experience higher exposure risks. The findings in our study could offer valuable insights into commuter exposure and inspire future research. Full article

The evolution of the generation mix towards deep decarbonization poses pressing questions about the role of hydropower and its possible share in the future mix. Most technical–economic analyses of deeply decarbonized systems either rule out hydropower growth due to lack of additional hydro resources or take it into account in terms of additional reservoir capacity. This paper analyzes a generation mix made of photovoltaic, wind, open-cycle gas turbines, electrochemical storage and hydroelectricity, focusing on the optimal generation mix’s reaction to different methane gas prices, hydroelectricity availabilities, pumped hydro reservoir capacities, and mean filling durations for hydro reservoirs. The key feature of the developed model is the sizing of both optimal peak power and reservoir energy content for hydropower. The results of the study point out two main insights. The first one, rather widely accepted, is that cost-effective decarbonization requires the greatest possible amount of hydro reservoirs. The second one is that, even in the case of totally exploited reservoirs, there is a strong case for increasing hydro peak power. Application of the model to the Italian generation mix (with 9500 MWp and 7250 MWp of non-pumped and pumped hydro fleets, respectively) suggests that it is possible to achieve methane shares of less than 10% if the operating costs of open-cycle gas turbines exceed 160 EUR/MWh and with non-pumped and pumped hydro fleets of at least 9200 MWp and 28,400 MWp, respectively. Full article

For large-scale tropical tuna purse-seine fisheries, it is prohibitively costly to obtain adequate sampling coverage to estimate fleet-level catch composition solely from sample data. Logbook or observer data, with complete fleet coverage, are often available but may be considered unreliable for species composition. Previous studies have developed models, trained with sample data, to predict set-level species compositions based on environmental and operational covariates. Here, models were developed to predict well-level species composition from uncorrected observer data and covariates affecting the observers’ view of the catch during loading, with port-sampling data as the response variable. The analysis used paired, well-level data from sets made on floating objects by the Eastern Pacific Ocean tuna purse-seine fleet during 2023–2024. Results indicated that, overall, observer data proportions of bigeye (BET) and yellowfin tunas tended to be greater than the model-estimated proportions, with the opposite occurring for skipjack tuna (SKJ). However, vessel effects sometimes modified these tendencies. Model complexity was greatest for BET and least for SKJ. For BET, observer data proportions and model-estimated proportions were more similar when the vessel had a hopper. They were also more similar in 2023 as compared to 2024, suggesting sample data for bias adjustments should be collected annually. The approach shows potential for predicting the species composition of unsampled wells. Full article

Against the backdrop of global emissions reduction and transportation electrification, electric vehicles are gradually replacing traditional fuel vehicles for delivery. However, issues such as limited range and charging times often conflict with time window service requirements. To balance economic and environmental performance, mixed fleets and multi-method charging strategies have emerged as viable approaches. This study addresses the problem by developing a mixed-integer programming model that incorporates multiple charging methods and carbon emission accounting. An Improved Adaptive Large Neighborhood Search (IALNS) algorithm is proposed, featuring multiple Removal and Insertion operators tailored for customers and charging stations, along with two local optimization operators. The algorithm’s superiority and applicability are validated through simulation and comparative analysis on benchmark instances and real-world data from an urban courier network. Sensitivity analysis further demonstrates that the proposed algorithm effectively coordinates vehicle type and charging mode selection, reducing total costs and carbon emissions while ensuring service quality. This approach provides practical reference value for operational decision-making in mixed fleet delivery. Full article

Sensor-rich Automated Guided Vehicles (AGVs) are increasingly deployed in logistics, yet large fleets relying on fixed tracks face high maintenance costs and frequent route conflicts. This study targets rail-based material handling and proposes an end-to-end multi-AGV navigation pipeline under realistic operational constraints. A conflict-aware global planner, extended from the A* algorithm, generates feasible routes, while a multi-sensor perception stack integrates LiDAR and camera data to distinguish moving AGVs, static obstacles, and task targets. Based on this perception, a Deep Q-Network (DQN) policy with a tailored reward function enables real-time dynamic obstacle avoidance in complex traffic. Simulation results demonstrate that, compared with the Artificial Potential Field (APF) baseline, the proposed GG-DRL approach reduces collisions by ~70%, lowers planning time by 25–30%, shortens paths by 10–15%, and improves smoothness by 20–25%. On the Maze Benchmark Map, GG-DRL surpasses classical planners (e.g., RRT) and deep RL baselines (e.g., DDPG) in path quality, computation, and avoidance behavior, achieving an average path length of 81.12, computation time of 11.94 s, 5.2 avoidance maneuvers, and smoothness of 0.86. Robustness is maintained as a dynamic obstacles scale up to 30. These findings confirm that combining multi-sensor fusion with deep reinforcement learning enhances AGV safety, efficiency, and reliability, with broad potential for intelligent railway logistics. Full article

The increasing adoption of electric delivery fleets introduces significant challenges related to uneven energy utilization and suboptimal scheduling efficiency. Vehicle-to-Vehicle (V2V) energy sharing presents a promising solution, but its effectiveness critically depends on precise matching and co-optimization within dynamic urban traffic environments. This paper proposes a hierarchical optimization framework to minimize total fleet operational costs, incorporating a comprehensive analysis that includes battery degradation. The core innovation of the framework lies in coupling high-level path planning with low-level real-time speed control. First, a high-fidelity energy consumption surrogate model is constructed through model predictive control simulations, incorporating vehicle dynamics and signal phase and timing information. Second, the spatiotemporal longest common subsequence algorithm is employed to match the spatio-temporal trajectories of energy-provider and energy-consumer vehicles. A battery aging model is integrated to quantify the long-term costs associated with different operational strategies. Finally, a multi-objective particle swarm optimization algorithm, integrated with MPC, co-optimizes the rendezvous paths and speed profiles. In a case study based on a logistics network, simulation results demonstrate that, compared to the conventional station-based charging mode, the proposed V2V framework reduces total fleet operational costs by a net 12.5% and total energy consumption by 17.4% while increasing the energy utilization efficiency of EV-Ps by 21.4%. This net saving is achieved even though the V2V strategy incurs a marginal increase in battery aging costs, which is overwhelmingly offset by substantial savings in logistical efficiency. This study provides an efficient and economical solution for the dynamic energy management of electric fleets under realistic traffic conditions, contributing to a more sustainable and resilient urban logistics ecosystem. Full article

Background: General aviation pilots are, anecdotally, referred to as “weekend warriors” due to their flying infrequency. Considering that flight skills erode with irregular practice/reinforcement, we determined whether private pilots (PPLs) fly/train sufficiently to operate safely in the context of slow flight, a skill critical for safe operations and which rapidly atrophies with <~51 h flight time/8 months per prior research. Method: Slow-flight-related aviation accidents (2008–2019) were per the NTSB Access^R database, and fatal mishap rates were calculated using general aviation fleet times. Eight-month flight histories of airplanes in single PPL ownership were captured retrospectively using FlightAware^R. PPL survey responses were collected between January and March 2025. Statistical tests employed proportion/Independent-Samples Median Tests and a Poisson Distribution. Results: The slow-flight-related fatal accident rate (2017–2019) trended downwards (p = 0.077). In-flight tracking of 90 airplanes revealed an 8-month median flight time of 6 h, which is well below the aforementioned 51 h requisite for safe operations. Of the aircraft flown < 51 h, only 9% engaged in slow-flight practice. In the online survey, only the upper quartile of 126 PPLs achieved the aforementioned time requisite for preserving slow-flight skills, but nevertheless, 89% of respondents attested to being flight-proficient. Conclusions: Persistence in slow-flight-related fatal accidents likely partly reflects PPLs’ deficiency in in-flight time/slow-flight practice. Full article

In fresh produce cold chain last-mile delivery, the highly dispersed customer base leads to exorbitant delivery costs, posing the greatest challenge for cold chain enterprises. Achieving a symmetrical balance between cost-efficiency, environmental sustainability, and service quality is a fundamental pursuit in logistics system optimization. This paper proposes integrating the crowd-shipping logistics model—characterized by internet platform sharing and flexibility—into the delivery service. It incorporates and extends features such as cold chain delivery, mixed fleets using gasoline and diesel vehicles (GDVs), electric vehicles (EVs), partial charging strategies for EVs, and time-of-use electricity pricing into the crowd-shipping model. A joint delivery mode combining traditional professional delivery (using GDVs and EVs) with crowd-shipping is proposed, creating a symmetrical collaboration between centralized fleet management and distributed social resources. The challenges associated with utilizing occasional drivers (ODs) are analyzed, along with the corresponding compensation decisions and allocation-related constraints. A route optimization model is constructed with the objective of minimizing total cost. To solve this model, an Improved Whale Optimization Algorithm (IWOA) is proposed. To further enhance the algorithm’s performance, an adaptive variable neighborhood search is embedded within the proposed algorithm, and four local search operators are applied. Using a case study of 100 customer nodes, the joint delivery mode with OD participation reduces total delivery costs by an average of 24.94% compared to the traditional professional vehicle delivery mode, demonstrating a more symmetrical allocation of logistical resources. The experiments fully demonstrate the effectiveness of the joint delivery model and the proposed algorithm. Full article

At the international level, new measures, policies, and technologies are being developed to reduce greenhouse gas emissions and, more broadly, air pollutants. Road transportation is one of the main contributors to such emissions, as vehicles are extensively used in logistics operations, and many fleet owners of fossil-fueled trucks are adopting new technologies such as electric, hybrid, and hydrogen-based vehicles. This paper addresses the Hybrid Fleet Capacitated Vehicle Routing Problem with Time Windows (HF-CVRPTW), with the objectives of minimizing costs and mitigating environmental impacts. A mixed-integer linear programming model is developed, incorporating split deliveries, scheduled arrival times at stores, and a carbon cap-and-trade mechanism. The model is tested on a real case study provided by Decathlon, evaluating the performance of internal combustion engine (ICE), electric (EV), and hydrogen fuel cell (HV) vehicles. Results show that when considering economic and emission trading costs, the optimal fleet deployment priority is to use ICE vehicles first, followed by EVs and then HVs, but considering only total emissions, the result is the reverse. Further analysis explores the conditions under which alternative fuel, electricity, or hydrogen prices can achieve competitiveness, and a further analysis investigates the impact of different electricity generation and hydrogen production pathways on overall indirect emissions. Full article

As of mid-2025, California maintains a target (and legal agreement with truck OEMs) to achieve 100% zero-emission medium- and heavy-duty (M/HD) truck sales by 2036. While the US federal government has relaxed its targets, fuel economy standards continue to incentivize electrification. To meet these ambitions, the adequate rollout of charging infrastructure at scale is needed. This paper reviews existing studies on M/HD charging and investment needs in California and the U.S. This paper introduces a novel matrix that delineates charging needs by charging power, truck type (Class 2b-8), charger-to-vehicle ratios, and charger investment costs. Results indicate that California may require 151,000 to 156,000 depot and public chargers on the road by 2030, growing to 434,000 to 460,000 chargers on the road by 2035. Corresponding investment—including new installation and replacement—could reach USD 7.1 to USD 7.4 billion by 2030 and USD 16.4 to USD 17.8 billion by 2035. Meeting this scale of infrastructure deployment represents not only a technical challenge but also a sustainability imperative, demanding unprecedented coordination among policymakers, utilities, and fleet operators to overcome barriers like financing and permitting and to ensure infrastructure growth aligns with climate commitments and equitable access. Full article

With the surge in global maritime trade of new energy vehicles (NEVs), the roll-on/roll-off (Ro-Ro) shipping market faces a severe supply–demand imbalance, pushing shipping rates to persistently high levels. To tackle this challenge, NEV manufacturers and other automakers have begun establishing their own Ro-Ro fleets, creating an urgent need for optimized operational scheduling of these proprietary fleets. Against this context, this study focuses on optimizing the operational scheduling of automakers’ self-owned Ro-Ro fleets. Under the premise of deterministic automobile export transportation demands, a mixed-integer programming model is developed to minimize total fleet operational costs, with decision variables covering vessel port call sequence/selection, port loading and unloading quantities, and voyage speeds. A genetic algorithm is designed to solve the model, and the effectiveness of the proposed approach is validated through a real-world case study. The results demonstrate that the optimization method generates clear, actionable scheduling schemes for self-owned Ro-Ro fleets, effectively helping automakers refine their maritime logistics strategies for proprietary fleets. This study contributes to the field by focusing on automaker-owned Ro-Ro fleets and filling the research gap in cargo-owner-centric scheduling, providing a practical tool for automakers’ overseas logistics operations. Full article

With the growing deployment of electric buses (e-buses), accurate energy use modelling has become essential for fleet optimisation and operational planning. Using the SORT methodology, this study analyses instantaneous energy consumption and recuperation (IECR). Three vehicle configurations were tested (one battery with pantograph, four batteries, and eight batteries), each with ten repeatable runs. Four approaches were compared: a baseline linear regression, an extended linear model (ELM) due to the state, a feed-forward neural network, and a recurrent neural network (RNN). The extended linear model achieved a determination coefficient of R² = 0.9124 (residual standard deviation 4.26) compared with R² = 0.7859 for the baseline, while the determination coefficient for the RNN is 0.9343, and the RNN provided the highest accuracy on the test set (the correlation coefficient between real and predicted values is 0.9666). The results confirm the dominant influence of speed and acceleration on IECR and show that battery configuration mainly affects consumption during acceleration. Literature-consistent findings indicate that regenerative systems can recover 25–51% of braking energy, with advanced control methods further improving recovery. Despite non-normality and temporal dependence of residuals, the state-aware linear model remains interpretable and competitive, whereas recurrent networks offer superior fidelity. These results support real-time energy management, charging optimisation, and reliable range prediction for electric buses in urban public transport. Full article

This study presents an optimisation algorithm to solve a collaborative vehicle routing problem with time windows. The algorithm was developed and tested on a real-world case study to investigate the potential for a shared-fleet operation involving public organisations, specifically, the Isle of Wight Council (IWC) and the National Health Service (NHS). The aim was to evaluate whether collaborative use of public-sector vehicles could reduce total fleet size, operational costs, and vehicle-kilometres travelled, while maintaining existing service levels. The study develops a two-stage optimisation algorithm that incorporates real-world constraints such as vehicle capacity, time windows, and pre-assigned mandatory stops. The first stage maximises the number of assignable collaborative tasks across fleets, while the second stage minimises the total travel cost conditional on this maximum assignment. Using historical data and a novel optimisation algorithm, vehicle movements were modelled to evaluate benefits in terms of cost savings, reduced CO₂ emissions and vehicle usage. The case study results generated by the algorithm suggested that considerable improvements could be made by integrating patient diagnostic collection rounds into the existing IWC minibus routes: (a 10.6% reduction in CO₂ emissions (644 kg/month) and vehicle kilometres (2300 km/month), a 20.2% reduction in working hours (219 h/month), and a 17.8% saving in cost (GBP (£) 3596/month) leading to IWC gaining a potential additional revenue of GBP (£) 54,829 annually while reducing costs by 22.4% for the NHS. The findings highlighted the potential benefits of shared fleet collaborations between public sector organisations, offering a model for similar collaborations in other public sector contexts. Full article

Internal combustion engines remain a predominant source of global energy consumption, contributing substantially to both operational costs and greenhouse gas emissions. This work evaluates a nanomaterial-based engine oil additive that reduces friction and wear and increases torque, horsepower, and fuel efficiency. This novel nano oil additive contains functionalized carbon nanotubes and hexagonal boron nitride nanosheets that are dispersed in base oil using a proprietary ultrasonication process. Block-on-ring tests performed by multiple testing facilities demonstrated up to a 17% decrease in coefficient of friction and up to a 78% decrease in wear compared to the base oil after treating with the nano oil additive. Thermal properties enhancement by the nano oil additive was evaluated and increases up to 17 °C in thermal stability were obtained. Additionally, the nano oil additive increased torque and horsepower by an average of 7% in motorcycles and 2.4% in pickup trucks. Most importantly, the nano oil additive demonstrated improvements in fuel economy in both gasoline and diesel engines, with laboratory tests reporting 3–5% increases and practical field tests on a commercial truck fleet reporting an average of a 6% increase. The improved engine efficiency leads to reduced turbo temperature in heavy diesel engines and prolonged engine life expectancy and will significantly improve global environmental sustainability. Full article

Aviation’s sustainability discourse often centres on flight emissions, but production and end-of-life phases also carry material, energy, and pollution impacts that are large enough to merit systematic intervention. With ~13,000 aircraft projected to retire over the next two decades—roughly 44% of the global fleet—the sector must scale responsible dismantling and material recovery to avoid lost opportunities for meeting future sustainability goals and to harness economic value from secondary parts and recycled feedstocks. Embedding major sustainability and circular economy principles into aircraft design, operations, and retirement can reduce waste, conserve critical materials, and lower lifecycle emissions while contributing directly to multiple SDGs. Furthermore, when considering particular aircraft types, thousands of narrow-body aircraft such as the Airbus A320 and Boeing 737 are due to reach their end of life over the next two decades. This research evaluates the economic and environmental feasibility of aluminium recycling from these aircraft, integrating material flow analysis, cost–benefit modelling, and a lifecycle emissions assessment. An economic assessment framework is developed and applied, with the results showing that approximately 24.7 tonnes of aluminium can be recovered per aircraft, leading to emissions savings of over 338,000 kg of CO₂e, a 95% reduction compared to primary aluminium production. However, scrap value alone cannot offset dismantling costs; the break-even scrap price is over USD 4200 per tonne. When additional revenue streams such as component resale and carbon credit incentives are incorporated, the model predicts a net profit of over USD 59,000 per aircraft. The scenario analysis confirms that aluminium recycling only becomes financially viable through multi-stream revenue models, supported by Extended Producer Responsibility (EPR) and carbon pricing. While barriers remain, aluminium recovery is a strategic opportunity to align aviation with circular economy and decarbonisation goals. Full article