Search Results (76)

To address the efficiency and safety challenges arising from the mixed operation of autonomous and human-driven container trucks during the automation transformation of traditional container terminals, this study designed a simulation-based optimization framework for mixed vehicle scheduling. A spatio-temporal graph dynamic scheduling model was constructed, incorporating node capacity, arc capacity, and path constraints, to establish a multi-objective optimization model aimed at minimizing the maximum completion time of internal trucks and the average waiting time of external trucks. An improved NSGA-II algorithm was employed to generate task assignment solutions, which were evaluated using discrete-event simulation, integrating a dynamic programming-based yard block selection strategy for external trucks and a congestion-aware path planning algorithm. Experimental results demonstrate that the dynamic priority strategy effectively adapts to different traffic flow scenarios: under low external truck flow, the autonomous internal truck priority strategy reduces task completion time by 18% to 25%, while under high flow, the external truck priority strategy significantly decreases the average waiting time. The optimal configuration ratio between internal and external trucks was identified as approximately 1:2. This research provides a theoretical basis and decision support for enhancing terminal operational efficiency and automation transformation. Full article

This study investigated how consumers’ food consumption needs and perceptions influence their attitudes and behavioral intentions toward food truck dining. Drawing on the stimulus–organism–response (SOR) framework, perceived risks and benefits were conceptualized as external stimuli; food consumption needs (necessity vs. enjoyment) and attitudes represented the organism; and behavioral intentions denoted the response. Data were collected via Amazon Mechanical Turk and analyzed using structural equation modeling and multigroup comparisons based on prior food truck experience. Perceived benefits and food enjoyment positively influenced attitudes, whereas perceived risks negatively influenced attitudes. Attitude significantly predicted future behavioral intentions, whereas food as a necessity did not. The multigroup analysis revealed that prior experience moderated these relationships; perceived benefits and risks primarily guided inexperienced consumers, whereas experienced consumers formed attitudes mainly through hedonic needs. These findings contribute to the literature by integrating the SOR and value–attitude–behavior hierarchies to explain cognitive and motivational mechanisms underlying food truck patronage. They also highlight the moderating role of prior experience, which reshapes the strength of the model’s key paths. The study offers practical implications for food truck operators seeking to balance risk mitigation with perceived benefits to encourage repeat patronage. Full article

Heavy vehicles present high aerodynamic drag. This results in significant fuel consumption and, consequently, high emissions of harmful substances. This study examines the variation in aerodynamic drag in a European-type truck with different trailer configurations. Passive flow control by geometry modifications of the rear part of the trailer and active flow control using the Coanda effect were tested, with the aim of improving the aerodynamic efficiency of the vehicle. To achieve this, a modular structure of a 1:30 scaled truck was designed to enable different trailer configurations. Drag measurements were performed with a two-component external balance, and PIV tests were conducted to correlate the drag reduction with the aerodynamic changes behind the trailer. Passive control reduced drag by up to 5.7%, and active flow control reduced it by up to 12.6% compared to the unmodified base trailer. PIV flow visualization confirms that blowing effectively reduces the recirculation zone behind the trailer. Full article

Automated driving systems (ADSs) have the potential to make mobility services both safer and more accessible. The New Assessment/Test Method (NATM) from the UNECE establishes a multi-pillar framework for ADS safety assessment, centred on comprehensive scenario-based testing of the operational design domain (ODD). While NATM sets out the vision, it leaves unresolved how such assessments can be scaled and applied in practice. The SUNRISE safety assurance framework (SAF) addresses this challenge by offering a concrete and scalable pathway for operationalising NATM principles. The core contribution of this paper is the successful execution of the SAF process. Rather than validating the performance of a specific automated driving function, the work demonstrates how the SAF can be applied end-to-end: starting from external requirements for the system under test (SUT), through scenario generation based on ODD, dynamic driving task (DDT), and test objectives to the allocation of scenarios across heterogeneous test environments and the consolidation of outcomes into a structured safety argument. The approach is exemplified through the use case of automated truck docking in confined logistics environments. Simulation (CARLA), a scaled model truck, and a full-size truck are employed not to validate the ADS function itself, but to show that the SAF enables consistent, traceable, and defensible execution of NATM-aligned safety assessment. This walk-through highlights the scalability, practicality, and applicability of the SAF to real-world ADS features. Full article

Airport operations significantly contribute to air pollution in their vicinity through various sources, including aircraft activities—particularly taxiing and take-off—as well as ground support equipment, service vehicles, and maintenance work. Since emissions from aircraft engines represent the primary pollution source at airports, it is essential to reduce emissions at every phase of the LTO (landing and take-off) cycle to improve local air quality and promote environmental sustainability. Given the research gap in emission analysis, a comprehensive LCA framework for airport pushback and taxi operations is proposed, integrating tow truck propulsion, a taxiing strategy, and fleet management. Given the complexity of the issue, the authors first decided to investigate emissions from taxiing operations using tow trucks with different powertrains. The analyses performed were considered preliminary and a starting point for exploring emissions during taxiing operations at airports. Typically, aircraft are pushed back from the apron and then taxi under their own power using both engines at approximately 7% of maximum thrust. To substantially reduce exhaust emissions, external towing vehicles can be employed to move aircrafts from the apron to the runway. This study evaluates the potential for emission reductions in CO₂ and other harmful compounds such as CO, HC, NO_x, and PM by using electric towing vehicles (ETVs). It also compares emissions from different taxiing methods: full-engine taxiing, single-engine taxiing, ETV-assisted taxiing, and taxiing using diesel and petrol-powered tow vehicles. The analysis was conducted for Warsaw and Poznań airports. Three aircraft types—the most commonly operating at these airports—were selected to assess emissions under various taxiing scenarios. The results show that using electric towing vehicles can reduce CO and NO_x emissions to nearly zero compared to other methods. Interestingly, CO emissions from full-engine taxiing were lower than those from petrol-powered towing, although the Embraer 195 showed the highest CO emissions among the selected aircrafts. HC emissions were lowest for the A321neo and also relatively low for the diesel towing vehicle. The use of electric tow trucks significantly reduces CO₂ emissions: only 2.8–4.4 kg compared to 380–450 kg when taxiing with engines. This research highlights the critical role of sustainable ground operations in reducing harmful emissions and underscores the importance of integrating sustainability into airport taxiing practices. Full article

To address the issues of capacity resource waste and increased carbon emissions caused by the asymmetry between import and export container transportation tasks in port collection and dispatching, a green and cooperative task-and-route optimization method for container trucks with heterogeneous carriers based on task sharing is proposed from the perspective of system optimization. Based on the concept of a sharing economy, a sharing and cooperation mechanism with dual elasticity in capacity and information is designed, which integrates the container trucks’ resources and dissymmetric transportation tasks of heterogeneous carriers to expand the revenue potential for all participants. Based on task sharing and matching, a green and cooperative task-and-route optimization model for container trucks with heterogeneous carriers based on task sharing is formulated in order to optimize container trucks’ resources and transportation tasks comprehensively and reduce the system’s carbon emissions. A column generation algorithm embedded with a ring-increasing strategy is designed to solve the problem to improve computational efficiency. Through algorithm testing and a case analysis, the effectiveness of the model and algorithm is validated. The optimization results show that the overall carbon emissions are reduced by more than 28%, the number of used trucks decreases by 28%, and the profits of participants are increased by 24–65% compared with independent operations. Finally, several management insights are obtained regarding the number of shared trucks, the external market demand, task demand variability, the mixed fleet composition, subsidies, and bonus adjustments. Full article

Truck drivers play a pivotal role in global freight transport systems, yet their occupational and behavioral risk exposures make them a priority population in road safety research. This systematic review examines the factors influencing risky driving behaviors among truck drivers and their impacts on road safety outcomes. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, the review aimed to identify hazardous driving behaviors, the internal and external factors contributing to these behaviors, and their consequences for traffic safety. Inclusion criteria targeted original research published in English between 2009 and 2024 specifically focused on truck driver behavior and road safety outcomes. Systematic searches across PubMed, Scopus, Web of Science, and IEEE Xplore yielded 104 studies meeting these criteria. The synthesis revealed prevalent risky behaviors—such as speeding, fatigue-related impairments, distracted driving, and substance use—driven by internal factors (e.g., health conditions, psychological stress) and external pressures (e.g., occupational demands, regulatory constraints). These behaviors were consistently associated with increased crash risk. Nonetheless, limitations including the exclusion of non-English studies, reliance on self-reported data, and lack of standardized metrics constrained cross-study comparability and generalizability. Effective interventions identified include fatigue management programs, driver monitoring technologies, and positive safety climates. Findings underscore the urgent need for evidence-based, multifaceted strategies to enhance truck driver safety and inform policy, industry practices, and future research. Full article

Electric rope shovels play a critical role in open-pit mining, where their automation and operational efficiency directly affect productivity. This paper presents a LiDAR-based relative positioning method to determine the spatial relationship between the ERS and mining trucks. The method utilizes dynamic DBSCAN for noise removal and RANSAC for truck edge detection, enabling robust and accurate localization. Leveraging this positioning data, a time-optimal trajectory planning strategy is proposed specifically for autonomous swing motion during the unloading process. The planner incorporates velocity and acceleration constraints to ensure smooth and efficient movement, while obstacle avoidance mechanisms are introduced to enhance safety in constrained excavation environments. To execute the planned trajectory with high precision, a neural network-based sliding-mode controller is designed. An adaptive RBF network is integrated to improve adaptability to model uncertainties and external disturbances. Experimental results on a scaled-down prototype validate the effectiveness of the proposed positioning, planning, and control strategies in enabling accurate and autonomous swing operation for efficient unloading. Full article

Driven by the rapid expansion of e-commerce in China, there is a growing demand for high-efficiency, sustainability-oriented logistics solutions in rural regions, particularly for the time-sensitive distribution of perishable agricultural commodities. Traditional logistics systems face considerable challenges in these geographically complex regions due to limited infrastructure and extended travel distances. To address these issues, this study proposes an intelligent cooperative delivery system that integrates automated drones with conventional trucks, aiming to enhance both operational efficiency and environmental sustainability. A mixed-integer linear programming (MILP) model is developed to account for the diverse terrain characteristics of rural China, including forest, lake, and mountain regions. To optimize distribution strategies, the model incorporates an improved Fuzzy C-Means (FCM) algorithm combined with a hybrid genetic simulated annealing algorithm. The performance of three transportation modes, namely truck-only, drone-only, and truck–drone integrated delivery, was evaluated and compared. Sustainability-related externalities, such as carbon emission costs and delivery delay penalties, are quantitatively integrated into the total transportation cost objective function. Simulation results indicate that the cooperative delivery model is especially effective in lake regions, significantly reducing overall costs while improving environmental performance and service quality. This research offers practical insights into the development of sustainable intelligent transportation systems tailored to the unique challenges of rural logistics. 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

A simulation study was conducted on the hydrogen leakage diffusion process and influencing factors of fuel cell vehicles in enclosed spaces. The results indicate that when hydrogen leakage flows towards the rear of the vehicle, it mainly flows along the rear wall of the space and diffuses to the surrounding areas. Setting ventilation openings of different areas on the top of the carriage did not significantly improve the spatial diffusion speed of the leaked hydrogen, and the impact on the concentration of leaked hydrogen was limited to the vicinity of the ventilation openings. The ventilation opening at the rear can accelerate the diffusion of hydrogen gas to the external environment, significantly reducing the concentration of hydrogen and rate of gas rise. When the leaked hydrogen gas flows towards the front of the vehicle and above the space, the concentration of hydrogen mainly increases along the height direction of the space. The research results have significant safety implications for the use of fuel cell semi-trailer trucks. Full article

Recent catastrophic bridge fire incidents have highlighted the critical need for effective post-fire assessment of bridges, thereby challenging the dominant practice of complete replacement following these destructive events. This study investigates the post-fire performance of bare, isolated, and Carbon Fiber Reinforced Polymer (CFRP)-repaired Reinforced Concrete (RC) bridge columns under single-unit truck impact followed by air blast. This extreme loading scenario was deliberately selected given the increased vulnerability of bridge columns to this loading scenario in the recent few years. Three-dimensional Finite Element (FE) models of the structural system and surrounding environment were developed and validated in LS-DYNA. The effectiveness of two in-situ retrofitting schemes in mitigating damage and enhancing structural integrity of three column diameters under the selected multi-hazards was assessed. Results demonstrated that wrapping the bottom half of the column height prevents shear failure and significantly reduces the damage under the coupled impact and blast. In contrast, employing a combination of CFRP bars and externally bonded sheets showed limited enhancement on post-fire impact and blast performance. This study provides critical insights into the feasibility and efficacy of retrofitting bridge columns that have experienced fire, thus laying the groundwork for the reconsideration of current design and rehabilitation protocols. Full article

The spatial separation of port yards and railway hubs, which relies on external truck drayage as a necessary link, hampers the seamless transshipment of sea–rail intermodal containers between ports and railway hubs. This creates challenges in synchronizing yard cranes (YCs) at the port terminal, external trucks (ETs) on the road, and rail-mounted gantry cranes (RMGs) at the railway hub. However, most existing studies focus on equipment scheduling or container transshipment organization under the port–railway integration mode, often overlooking critical time window constraints, such as train schedules and export container delivery deadlines. Therefore, this study investigates the collaborative scheduling of YCs, ETs, and RMGs for synchronized loading and unloading under the port–railway separation mode. A mixed-integer programming (MIP) model is developed to minimize the maximum makespan of all tasks and the empty-load time of ETs, considering practical time window constraints. Given the NP-hard complexity of this problem, an improved genetic algorithm (GA) integrated with a “First Accessible Machinery” rule is designed. Extensive numerical experiments are conducted to validate the correctness of the proposed model and the performance of the solution algorithm. The improved GA demonstrates a 6.08% better solution quality and a 97.94% reduction in computation time compared to Gurobi for small-scale instances. For medium to large-scale instances, it outperforms the adaptive large neighborhood search (ALNS) algorithm by 1.51% in solution quality and reduces computation time by 45.71%. Furthermore, the impacts of objective weights, equipment configuration schemes, port–railway distance, and time window width are analyzed to provide valuable managerial insights for decision-making to improve the overall efficiency of sea–rail intermodal systems. Full article

An important process in the mining industry is material handling, where trucks are responsible for transporting materials extracted by shovels to different locations within the mine. The decision about the destination of a truck is very important to ensure an efficient material handling operation. Currently, this decision-making process is managed by centralized systems that apply dispatching criteria. However, this approach has the disadvantage of not providing accurate dispatching solutions due to the lack of awareness of potentially changing external conditions and the reliance on a central node. To address this issue, we previously developed a multi-agent system for truck dispatching (MAS-TD), where intelligent agents representing real-world equipment collaborate to generate schedules. Recently, we extended the MAS-TD (now MAS-TDRL) by incorporating learning capabilities and compared its performance with the original MAS-TD, which lacks learning capabilities. This comparison was made using simulated scenarios based on actual data from a Chilean open-pit mine. The results show that the MAS-TDRL generates more efficient schedules. Full article

As the world’s largest open-pit coal producer, China faces severe dust pollution in mining operations. Cabins of mining equipment (electric shovels, haul trucks, drills) exhibit unique micro-environmental contamination due to dual-source pollution (external infiltration and internal secondary dust generation), posing severe health risks to miners. This study focused on electric shovel cabins at the Heidaigou open-pit coal mine to address cabin dust pollution. Through analysis of dust physicochemical properties, a pollution characteristic database was established. Field measurements and statistical methods revealed temporal–spatial variation patterns of dust concentrations, quantifying occupational exposure risks and providing theoretical foundations for dust control. A novel gradient-pressurized air purification system was developed for harsh mining conditions. Key findings include the following. (1) Both coal-shovel and rock-shovel operators were exposed to Level I (mild hazard level), with rock-shovel operators approaching Level II (moderate hazard level). (2) The system reduced respirable dust concentrations from 0.313 mg/m³ to 0.208 mg/m³ (≥33.34% improvement) in coal-shovel cabins and from 0.625 mg/m³ to 0.421 mg/m³ (≥32.64% improvement) in rock-shovel cabins. These findings offer vital guidance for optimizing cabin design, improving dust control, and developing scientific management strategies, thereby effectively protecting miners’ health and ensuring operational safety. Full article