Search Results (172)

Battery-swapping stations (BSSs) are pivotal for supplying energy to electric heavy-duty trucks. However, their operations face challenges in accurate demand forecasting for battery-swapping and fair revenue allocation. This study proposes an optimization strategy for the economic operation of BSSs that optimizes revenue allocation and load balancing to enhance financial viability and grid stability. First, factors including geographical environment, traffic conditions, and truck characteristics are incorporated to simulate swapping behaviors, supporting the construction of an accurate demand-forecasting model. Second, an optimization problem is formulated to maximize the weighted difference between BSS revenue and squared load deviations. An economic operations strategy is proposed based on an adaptive Shapley value. It enables precise evaluation of differentiated member contributions through dynamic adjustment of bias weights in revenue allocation for a strategy that aligns with the interests of multiple stakeholders and market dynamics. Simulation results validate the superior performance of the proposed algorithm in revenue maximization, peak shaving, and valley filling. Full article

Inefficient inland repositioning of empty containers between depots remains a persistent challenge in container logistics, contributing significantly to unnecessary truck movements, elevated operational costs, and increased CO₂ emissions. Acknowledging the importance of this problem, a large amount of relevant literature has appeared. The objective of this paper is to track the empty container flow between ports, empty depots, inland terminals, and customer premises. Additionally, it aims to simulate and assess CO₂ emissions, capturing the dynamic interactions between different agents. In this study, agent-based modeling (ABM) was proposed to simulate the empty container movements with an emphasis on inland transportation. ABM is an emerging approach that is increasingly used to simulate complex economic systems and artificial market behaviours. NetLogo was used to incorporate real-world geographic data and quantify CO₂ emissions based on truckload status and to evaluate the other operational aspects. Behavior Space was also utilized to systematically conduct multiple simulation experiments, varying parameters to analyze different scenarios. The results of the study show that customer demand frequency plays a crucial role in system efficiency, affecting container availability and logistical tension. Full article

Road transportation is a major contributor to greenhouse gas (GHG) emissions in Thailand. This study assesses the potential for GHG mitigation in the road transport sector from 2018 to 2030. Emission factors for various vehicle types and technologies were derived using the International Vehicle Emissions (IVE) model. Emissions were then estimated based on country-specific vehicle data. In the baseline year 2018, total emissions were estimated at 23,914.02 GgCO₂eq, primarily from pickups (24.38%), trucks (20.96%), passenger cars (19.48%), and buses (16.95%). Multiple mitigation scenarios were evaluated, including the adoption of electric vehicles (EVs), improvements in fuel efficiency, and a shift to renewable energy. Results indicate that transitioning all newly registered passenger cars (PCs) to EVs while phasing out older models could lead to a 16.42% reduction in total GHG emissions by 2030. The most effective integrated scenario, combining the expansion of electric vehicles with improvements in internal combustion engine efficiency, could achieve a 41.96% reduction, equivalent to 18,378.04 GgCO₂eq. These findings highlight the importance of clean technology deployment and fuel transition policies in meeting Thailand’s climate goals, while providing a valuable database to support strategic planning and implementation. Full article

Dust emissions from unpaved haul roads in open-pit coal mining pose a significant risk to air quality, health, and operational efficiency of mining operations. This study integrated real-time field monitoring with numerical simulations using ANSYS Fluent 2023 R1 to investigate the generation, dispersion, and migration of particulate matter (PM) at the Ha’erwusu open-pit coal mine under varying meteorological conditions. Real-time measurements of PM2.5, PM10, and TSP, along with meteorological variables (wind speed, wind direction, humidity, temperature, and air pressure), were collected and analyzed using Pearson’s correlation and multivariate linear regression analyses. Wind speed and air pressure emerged as dominant factors in winter, whereas wind and temperature were more influential in summer (R² = 0.391 for temperature vs. PM2.5). External airflow simulations revealed that truck-induced turbulence and high wind speeds generated wake vortices with turbulent kinetic energy (TKE) peaking at 5.02 m²/s², thereby accelerating particle dispersion. The dust migration rates reached 3.33 m/s within 6 s after emission and gradually decreased with distance. The particle settling velocities ranged from 0.218 m/s for coarse dust to 0.035 m/s for PM2.5, with dispersion extending up to 37 m downwind. The highest simulated dust concentration reached 4.34 × 10⁻² g/m³ near a single truck and increased to 2.51 × 10⁻¹ g/m³ under multiple-truck operations. Based on spatial attenuation trends, a minimum safety buffer of 55 m downwind and 45 m crosswind is recommended to minimize occupational exposure. These findings contribute to data-driven, weather-responsive dust suppression planning in open-pit mining operations and establish a validated modeling framework for future mitigation strategies in this field. Full article

Diesel particulate matter—primarily ultrafine particles (UFPs), defined as particles smaller than 0.1 µm—are released by diesel-powered vehicles, especially those used in heavy-duty hauling. While much of the existing research on traffic-related air pollution focuses on urban environments, limited attention has been paid to how complex topography influences the concentration of UFPs, particularly in areas with significant truck traffic. With a focus on Morgantown, West Virginia, an area distinguished by a steep topography, this study investigates how travel over two different terrain conditions affects UFP concentrations close to roadways. Specifically, we sought to determine if the truck count taken from simultaneous video evidence could be used as a surrogate for varying topography in determining the concentration of UFPs. This study shows that “TRUCK COUNT” and “TRUCK SPEED” have a linear relationship and yield a possible surrogate measure of the lung dose of UFP number concentration. Our results demonstrate a statistically significant (p < 0.1) linear relationship between truck count and UFP number concentration (R = 0.77 and 0.40), validating truck count along with truck speed as a medium effect surrogate for estimating near-road UFP exposure. Dose estimation using the Multiple-Path Particle Dosimetry (MPPD) model further revealed that approximately 30% of inhaled UFPs are deposited in the alveolar region, underscoring the public health relevance of this exposure pathway in topographically complex areas. This method ultimately awaits comparison with health effects to determine its true potential as a useful exposure metric. Full article

Wildfires often spread rapidly and cause significant casualties and economic losses. Firefighting drones carrying water capsules provide an efficient way for wildfire extinguishing, but their operational capabilities are limited by their payloads. This weakness can be compensated by using ground vehicles to provide mobile water supply. To this end, this paper presents an optimization problem of scheduling multiple drones and water supply trucks for wildfire fighting, which allocates burning subareas to drones, routes drones to perform fire-extinguishing operations in burning subareas and reload water between every two consecutive operations, and routes trucks to provide timely water supply for drones. To solve the problem within the limited emergency response time, we propose a deep reinforcement learning method, which consists of an encoder for embedding the input instance features and a decoder for generating a solution by iteratively predicting the subarea selection decision through attention. Computational results on test instances constructed upon real-world wilderness areas demonstrate the performance advantages of the proposed method over a collection of heuristic and metaheuristic optimization methods. Full article

All current transportation systems (vehicles, trucks, planes, etc.) rely on the Global Positioning System (GPS) as their main navigation technology. GPS receivers collect signals from multiple satellites and are able to provide more or less accurate positioning. For civilian applications, GPS signals are sent without any encryption system. For this reason, they are vulnerable to various attacks, and the most prevalent one is known as GPS spoofing. The main consequence is the loss of position monitoring, which may increase damage risks in terms of crashes or hijacking. In this study, we focus on UAV (unmanned aerial vehicle) positioning attacks. We first review numerous techniques for detecting and mitigating GPS spoofing attacks, finding that various types of attacks may occur. In the literature, many studies have focused on only one type of attack. We believe that targeting the study of many attacks is crucial for developing efficient mitigation mechanisms. Thus, we have explored a well-known datasetcontaining authentic UAV signals along with spoofed signals (with three types of attacked signals). As a main contribution, we propose a more interpretable approach to exploit the dataset by extracting individual mission sequences, handling non-stationary features, and converting the GPS raw data into a simplified structured format. Then, we design tree-based machine learning algorithms, namely decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost), for the purpose of classifying signal types and to recognize spoofing attacks. Our main findings are as follows: (a) random forest has significant capability in detecting and classifying GPS spoofing attacks, outperforming the other models. (b) We have been able to detect most types of attacks and distinguish them. Full article

Multimodal transport refers to the integrated transportation in a logistics system in the form of multiple transportation modes, such as highway, railway, waterway, etc. In recent years, the deep integration of electric trucks and route optimization has significantly improved the cost-effectiveness and operational efficiency of multimodal transportation. It has provided strong support for the sustainable development of the logistics system. Based on whether to consider low-carbon requirements, uncertainty, and special cargo transportation, the literature is divided into five areas: traditional multimodal transport path optimization, multimodal transport path optimization considering low-carbon requirements, multimodal transport path optimization considering uncertainty, multimodal transport path optimization considering low-carbon requirements and uncertainty, and multimodal transport path optimization considering special transport needs. In this paper, we searched the literature on multimodal path optimization after 2016 in WOS (Web of Science) and CNKI (China National Knowledge Infrastructure), and found that the number of publications in 2024 is three times that in 2016. We collected 130 relevant studies to summarize the current state of research. Finally, with the development of multimodal transport to collaborative transport and the improvement of the application of in-depth learning in different fields, the research mainly focuses on two future research directions: collaborative transport and the use of in-depth learning to solve uncertain problems, and combining it with the problem of multimodal transport route optimization to explore more efficient and perfect transport solutions. Full article

Recently, the electrification and automation of heavy-duty trucks has gained significant attention from both industry and academia, driven by new legislation introduced by the European Union. During a typical drive cycle, the mass of an urban service truck can vary substantially as waste is collected, yet most existing studies rely on a single controller with fixed gains. This limits the ability to adapt to mass changes and results in suboptimal energy usage. Within the framework of the EU-funded OBELICS and ESCALATE projects, this study proposes a novel control strategy for a semi-autonomous refuse truck. The approach combines a particle swarm optimization algorithm to determine optimal controller gains and a multiple model controller to adapt these gains dynamically based on real-time vehicle mass. The main objectives of the proposed method are to (i) optimize controller parameters, (ii) reduce overall energy consumption, and (iii) minimize speed tracking error. A cost function addressing these objectives is formulated for both autonomous and manual driving modes. The strategy is evaluated using a real-world drive cycle from Eskişehir City, Turkiye. Simulation results show that the proposed MMC-based method improves vehicle performance by $5.19\%$ in autonomous mode and $0.534\%$ in manual mode compared to traditional fixed-gain approaches. Full article

Container drayage, as a pivotal element of door-to-door intermodal transportation, has garnered increasing attention due to its significant influence on container logistics costs. Although various types of transport requests have been defined in the literature, no comprehensive study has addressed all of them together yet, due to the lack of an efficient model and corresponding algorithms. Furthermore, existing research on container drayage often neglects the simultaneous incorporation of two trucking operation modes, two empty container repositioning strategies, and the availability of empty containers across multiple depots. To address these issues, this study proposes a comprehensive container drayage problem (CDP) and mathematically formulates it as an innovative mixed integer linear programming (MILP) model, capturing the uncertainty and unpredictability inherent in empty container allocation, truck dispatching, and route planning. Given the problem’s complexity, obtaining an exact solution for large instances is not feasible. Therefore, an improved large neighborhood search (LNS) algorithm is tailored by incorporating the “Sequential insertion” and the “Solution re-optimization” operations. Extensive numerical experiments using randomly generated instances of varying scales validate the correctness of the proposed model and demonstrate the performance of the proposed algorithm. Additionally, sensitivity analysis on the number and distribution of depots and empty containers offers valuable managerial insights for the development of an effective container drayage system. Full article

Waste heat utilization in fuel cell vehicles represents a critical technology for enhancing overall energy utilization efficiency and environmental adaptability, which reduces auxiliary heating consumption, extends driving range, and minimizes thermal management parasitic losses, holding significance for promoting application of fuel cell commercial vehicles. This study investigates a 49-ton fuel cell heavy-duty truck equipped with waste heat recovery capability, conducting vehicle energy flow experiments under multiple ambient temperatures (including 7 °C, $-7$ °C and $-25$ °C extreme cold conditions), varying load conditions, and waste heat recovery mode switching, with focused analysis on the energy consumption and temperature response of the waste heat recover critical components, to evaluate the energy utilization of fuel cell waste heat. Experimental results demonstrate the substantial impact of waste heat recovery function on the proportion of the warm air positive temperature coefficient (PTC) energy consumption on total energy consumption, showing that deactivating waste heat recovery increased the PTC energy consumption obviously. Besides, activating the waste heat recovery function contributes to elevated the stack radiator outlet temperature under low-temperature operating conditions. Full article

With the rapid proliferation of electric vehicles (EVs), urban logistics faces increasing challenges in optimizing vehicle routing. This paper presents a new modeling framework for the Electric Vehicle Routing Problem (EVRP), where multiple electric trucks serve a set of customers within their capacity limits. The model incorporates critical EV-specific constraints, including limited battery range, charging demand, and dynamic urban traffic conditions, with the objective of minimizing total delivery cost. To efficiently solve this problem, a Dual Population Cooperative Genetic Algorithm (DPCGA) is proposed. The algorithm employs a dual-population mechanism for global exploration, effectively expanding the search space and accelerating convergence. It then introduces local refinement operators to improve solution quality and enhance population diversity. A large number of experimental results demonstrate that DPCGA significantly outperforms traditional algorithms in terms of performance, achieving an average 3% improvement in customer satisfaction and a 15% reduction in computation time. Furthermore, this algorithm shows superior solution quality and robustness compared to the AVNS and ESA-VRPO algorithms, particularly in complex scenarios such as adjustments in charging station layouts and fluctuations in vehicle range. Sensitivity analysis further verifies the stability and practicality of DPCGA in real-world urban delivery environments. Full article

The sea–rail intermodal container terminal serves as a key transportation hub for green logistics, where efficient resource coordination directly enhances multimodal connectivity and operational synergy. To address limited storage capacity and trans-shipment inefficiencies, this study innovatively proposes a resource-sharing strategy between the seaport and the railway container terminal, focusing on the joint allocation of yard space and internal trucks. For indirect trans-shipment operations between ships, the port, the railway container terminal, and trains, a mixed-integer programming model is formulated with the objective of minimizing the container trans-shipment cost and the weighted turnaround time of ships and trains. This model simultaneously determines yard allocation, container transfers, and truck allocation. A two-layer hybrid heuristic algorithm incorporating adaptive Particle Swarm Optimization and Greedy Rules is designed. Numerical experiments verify the model and algorithm performance, revealing that the proposed method achieves an optimality gap of only 1.82% compared to CPLEX in small-scale instances while outperforming benchmark algorithms in solution quality. And the shared yard strategy enhances ship and train turnaround efficiency by an average of 33.45% over traditional storage form. Sensitivity analysis considering multiple realistic factors further confirms the robustness and generalizability. This study provides a theoretical foundation for sustainable port–railway collaboration development. Full article

With the rapid development of e-commerce, the logistics industry faces multiple challenges, including high delivery costs, long delivery times, and a shortage of delivery personnel. Truck–drone collaborative delivery combines the high load capacity of trucks with the flexibility and speed of drones, offering an innovative and practical solution. This paper proposes the Truck–Drone Collaborative Delivery Routing Problem (TDCRPTW) and develops a multi-objective optimization model that minimizes delivery costs and maximizes time reliability under capacity and time window constraints in multi-truck, multi-drone scenarios. To solve the model, an innovative two-stage solution strategy that combines the adaptive k-means++ clustering algorithm with temperature-controlled memory simulated annealing (TCMSA) is proposed. The experimental results demonstrate that the proposed model reduces delivery costs by 10% to 50% and reduces delivery time by 15% to 40%, showcasing the superiority of the truck–drone collaborative delivery model. Moreover, the proposed algorithm demonstrates outstanding performance and reliability across multiple dimensions. Therefore, the proposed approach provides an efficient solution to the truck–drone collaborative delivery problem and offers valuable insights for enhancing the efficiency and reliability of e-commerce logistics systems. Full article

Thailand’s transport sector faces critical challenges in energy management amid rapid economic growth, with transport accounting for approximately 30% of total energy consumption. This study addresses research gaps in transport energy forecasting by comparing Long Short-Term Memory (LSTM) neural networks and XGBoost models for predicting transport energy consumption in Thailand. Utilizing a comprehensive dataset spanning 1993–2022 that includes vehicle registration data by size category, vehicle kilometers traveled, and macroeconomic indicators, this research evaluates both modeling approaches through multiple performance metrics. The results demonstrate that XGBoost consistently outperforms LSTM, achieving an R-squared value of 0.9508 for test data compared to LSTM’s 0.2005. Feature importance analysis reveals that medium vehicles contribute 36.6% to energy consumption predictions, followed by truck VKT (20.5%), with economic and demographic factors accounting for a combined 15.2%. This research contributes to both methodological understanding and practical application by establishing XGBoost’s superior performance for transport energy forecasting, quantifying the differential impact of various vehicle categories on energy consumption, and demonstrating the value of integrating vehicle registration and usage data in predictive models. The findings provide evidence-based guidance for prioritizing policy interventions in Thailand’s transport sector to enhance energy efficiency and sustainability. Full article