Crowd logistics (CL) represents an innovative model within the logistics sector, leveraging the participation of individuals to enhance service provision, optimize resource utilization, and reduce operational costs. Among the various applications of CL, crowd distribution has emerged as one of the most prevalent methods. This study introduces a Multi-Criteria Decision-Making (MCDM) framework for the selection of CL platforms, examining key factors that contribute to their success. A comprehensive review of relevant literature and an in-depth analysis of both domestic and global platforms were conducted, revealing critical performance indicators for successful platform implementation. The Step-wise Weight Assessment Ratio Analysis (SWARA) and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) methods were employed to evaluate essential criteria, including cost efficiency, delivery speed, reliability, environmental sustainability, flexibility, and customer support quality. The results of this analysis demonstrate that platforms such as Company 1, Company 2, and Company 3 have achieved market dominance in Serbia, attributed to their optimal balance across these performance criteria. This study’s proposed model serves as a practical tool for businesses and consumers seeking to select the most suitable CL platforms, while also providing actionable insights for further enhancement of logistics systems. The findings contribute to the growing body of knowledge on CL, highlighting the importance of comprehensive evaluation in the selection process.

Advanced ship detection technologies play a critical role in improving maritime safety by enabling the rapid identification of vessels and other maritime targets, thereby mitigating the risk of collisions and optimizing traffic efficiency. Traditional detection methods often demonstrate high sensitivity to minor variations in target appearance but face significant limitations in generalization, making them ill-suited to the complex and dynamic nature of maritime environments. To address these challenges, an enhanced ship detection method, referred to as YOLOv5-SE, has been proposed, which builds upon the YOLOv5 framework. This approach incorporates attention mechanisms within the backbone network to improve the model's focus on key features of small targets, dynamically adjusting the importance of each channel to boost representational capacity and detection accuracy. In addition, a refined version of the Complete Intersection over Union (CIoU) loss function has been introduced to optimize the loss associated with target bounding box prediction, thereby improving localization accuracy and ensuring more precise alignment between predicted and ground-truth boxes. Furthermore, the conventional coupled detection head in YOLOv5 is replaced by a Decoupled Head, facilitating better adaptability to various target shapes and accelerating model convergence. Experimental results demonstrate that these modifications significantly enhance ship detection performance, with mean Average Precision (mAP) at IoU 0.5 reaching 94.9% and 95.1%, representing improvements of 3.1% and 1.2% over the baseline YOLOv5 model, respectively. These advancements underscore the efficacy of the proposed methodology in improving detection accuracy and robustness in challenging maritime settings.

An innovative context-aware fuzzy logic transmission map adjustment method is proposed for road image defogging, aimed at improving visibility and clarity under varying fog conditions. Unlike conventional defogging techniques that rely on a uniform transmission map, the presented approach introduces a fuzzy logic framework that dynamically adjusts the transmission map based on local fog density and contextual factors. Fuzzy membership functions are employed to classify fog density into low, medium, and high categories, enabling an adaptive and context-sensitive adjustment process. Road images are segmented into distinct regions using edge detection and texture analysis, with each region treated independently to preserve critical details such as road markings, lane boundaries, and traffic signs. A key contribution is the integration of proximity-based adjustments for areas near high-intensity light sources, such as streetlights, to maintain brightness and enhance visibility in illuminated zones. The final transmission map is generated through the combination of fuzzy density-based adjustments and an iterative Gaussian filter, which smooths transitions and minimizes potential artifacts. This approach prevents over-darkening while enhancing contrast, even in dense fog conditions. Experimental results demonstrate that the proposed method significantly outperforms traditional defogging techniques in terms of brightness, contrast, and detail retention. The results underscore the utility of fuzzy logic in road image defogging, offering a robust solution for applications in autonomous driving, surveillance, and remote sensing. This method sets a new benchmark for visibility enhancement in challenging environments, providing a high-quality, adaptive solution for real-world applications.

Automated Guided Vehicles (AGVs) represent a transformative advancement in the automation of transport operations, facilitating unmanned mobility within a wide array of environments, including production lines, warehouses, freight hubs, and terminal operations. In container terminals, where AGVs are increasingly deployed, the routing of these vehicles is a critical task aimed at minimising operational inefficiencies such as travel time, fuel consumption, and overall transportation costs. Routing in this context refers to the determination of optimal paths for a fleet of AGVs, which must satisfy a variety of operational constraints while also adhering to predefined user requirements. Given the high complexity of these problems, characterised by a large solution space, finding exact solutions is computationally intractable for most scenarios. As a result, heuristic methods are commonly employed to approximate optimal solutions. Among the various heuristic techniques, the nearest neighbor algorithm and Tabu search have been identified as promising approaches for determining efficient AGV routes in container terminal environments. These methods are applied to identify paths that minimise travel distance and time, enhancing resource utilisation and improving the overall reliability of goods delivery. The application of these algorithms is expected to lead to a significant reduction in the number of kilometres travelled by AGVs, thereby lowering operational costs and improving service efficiency. This paper examines the efficacy of the "nearest neighbor" and Tabu search algorithms in the context of AGV routing at container terminals, highlighting their potential to optimise fleet operations in the face of complex logistical challenges. Emphasis is placed on the comparative analysis of both algorithms, with a focus on their ability to approximate optimal solutions in dynamic and highly constrained environments.

The degradation of road infrastructure presents significant challenges to public safety and maintenance budgets, with cracks serving as critical indicators of structural instability. Despite extensive advancements, existing detection methodologies frequently fail to address complex surface textures, variable illumination, and diverse crack geometries, resulting in inconsistent performance. An adaptive, multi-stage framework has been developed to mitigate these limitations, integrating advanced image processing techniques with fuzzy logic-based analysis. The proposed approach utilises dynamic contrast enhancement and multi-scale feature extraction to ensure accurate detection of both fine and extensive cracks across heterogeneous surfaces. A fuzzy graph-based methodology is employed to evaluate crack connectivity, while an adapted algorithm is applied to assess continuity and severity. The framework incorporates fuzzy wavelet transforms to enhance feature segmentation and employs morphological techniques for precise crack boundary delineation. Dijkstra’s algorithm is integrated to optimise connectivity analysis, facilitating the identification of critical structural deficiencies. The performance of the model has been rigorously validated through extensive experimental testing, achieving an accuracy rate of 94.2%, with high precision and recall metrics. Comparative analysis with conventional techniques reveals a significant reduction in false detection rates and an improved capacity for capturing intricate crack features. The results underscore the practical utility of the proposed model, demonstrating its scalability and reliability across diverse roadway conditions. By enabling early and accurate identification of structural anomalies, the framework enhances roadway safety, minimises maintenance costs, and supports proactive infrastructure management. The findings highlight its potential as a transformative solution for addressing modern challenges in road maintenance, with implications for improved public safety and resource optimisation.

This study investigates the spatial distribution and potential expansion of electric vehicle (EV) charging infrastructure in Ludhiana, India, with a focus on optimizing site selection to accommodate increasing demand. A multi-criteria framework was employed, incorporating traffic volume, demographic data, and usage patterns of existing charging stations to identify high-priority locations. Central commercial zones, including Ghumar Mandi, Feroze Gandhi Market, ISBT Ludhiana, and Ludhiana Railway Station, were found to exhibit significant traffic density and high EV ownership rates, making them prime candidates for the establishment of new charging stations. Spatial analysis, including heat maps, bar graphs, and pie charts, was used to visualize these key areas, revealing critical patterns in demand and facilitating the strategic targeting of infrastructure expansion. Community engagement was emphasized as an essential component in ensuring that infrastructure development aligns with user needs and preferences. The study further highlighted the importance of accessibility, economic viability, and sustainability as pivotal criteria for site selection. The findings offer valuable insights for urban planners and policymakers, supporting the development of a robust EV charging network that contributes to the advancement of sustainable urban mobility and the reduction of carbon emissions in Ludhiana. These results provide a basis for informed decision-making in the design of EV infrastructure, guiding the city's efforts towards an eco-friendly, future-ready transportation system.

The rapid growth of population and vehicular traffic has necessitated effective urban planning strategies to mitigate traffic congestion and enhance roadway efficiency. This study focuses on a critical signalized intersection in Konya, Turkiye’s largest metropolitan area, which is notable for its agricultural, industrial, and educational significance. Strategically positioned at the nexus of major transportation routes linking the Black Sea and Central Anatolia regions to the Mediterranean and Aegean areas, Konya exhibits considerable logistical potential. The Coşandere intersection, located in the Selçuklu district, was selected for analysis due to its four-legged configuration, featuring three lanes on both the south and north approaches and two lanes on the east and west approaches. Additionally, suitable turning islands and U-turn pockets are provided on the south and north approaches. Observational data indicate that the evening peak period poses significant operational challenges. A video surveillance system was employed to monitor vehicle movements, yielding a traffic volume of 1,874 vehicles per hour. The existing geometric design, traffic dynamics, and signalization were modelled using PTV Vissim software to assess the intersection's performance. The analysis revealed an average delay of 44.1 seconds per vehicle, an average of 0.9 stops per vehicle, and an average vehicle speed of 29.6 km/h, resulting in a Level of Service (LOS) classification of D. These findings indicate that the intersection currently accommodates traffic demand to a moderate degree. However, substantial improvements in operational efficiency could be achieved through enhancements to the signalization system, including the potential implementation of an adaptive traffic signal control system. This study provides valuable insights for traffic management authorities and urban planners aiming to optimise intersection performance in rapidly developing urban environments.

Magnetic levitation (maglev) transportation represents an advanced rail technology that utilizes magnetic forces to lift and propel trains, eliminating direct contact with tracks. This system offers numerous advantages over conventional railways, including higher operational speeds, reduced maintenance requirements, enhanced energy efficiency, and reduced environmental impact. However, the dynamic interaction between maglev trains and railway bridges, particularly curved bridges, presents challenges in terms of potential instability during operation. To better understand the dynamic behavior of maglev trains on curved bridges, an experimental study was conducted on the Fenghuang Maglev Sightseeing Express Line (FMSEL), the world’s first “Maglev + Culture + Tourism” route. The FMSEL employs a unique ‘U’-shaped girder design, marking its first application in such a setting. Field test data were collected to analyze the dynamic characteristics of the vehicle, suspension bogie, curved rail, and ‘U’-shaped bridge across a range of train speeds. The responses of both the train and bridge were examined in both time and frequency domains, revealing that response amplitudes increased with train speed. Notably, the ride quality of the vehicle remained excellent, as indicated by Sperling index values consistently below 2.5. Furthermore, lateral acceleration of the train was observed to be lower than vertical acceleration, while for the track, vertical acceleration was consistently lower than lateral acceleration. These findings offer insights into the dynamic performance of maglev trains on curved infrastructure, highlighting key factors that must be considered to ensure operational stability and passenger comfort.

The spatial configuration of the pantograph-catenary system (PCS) is significantly altered by the superelevation present in curved railway tracks, leading to deviations in the system’s dynamic behaviour and imposing constraints on operational speeds. In this study, a detailed model of the PCS in curved sections has been developed to evaluate the dynamic performance of a dual PCS under these conditions. It was observed that the contact loss rate of the trailing pantograph increases markedly as train speed rises, with this effect being more pronounced in curved sections compared to straight tracks. This degradation in performance necessitates optimisation strategies to ensure operational efficiency at higher speeds. To address the issue, it is proposed that the static uplift force of the trailing pantograph be increased when trains traverse curved sections. Additionally, optimisation of the catenary system is recommended, involving both a reduction in the span length and an increase in the tension of the contact wire. By implementing these strategies, the dual PCS can sustain the necessary contact and satisfy dynamic performance criteria at speeds of up to 300 km/h in curved sections. These findings provide valuable insights for improving the reliability and safety of high-speed railway operations on complex track geometries.

Maglev transportation, as an innovative mode of rail transit, is regarded as an ideal future transportation system due to its wide speed range, low noise, and strong climbing ability. However, the maglev control system faces challenges such as significant nonlinearity, open-loop instability, and multi-state coupling, leading to issues like insufficient tuning and susceptibility to environmental influences. This paper addresses these problems by investigating the self-tuning parameters of a maglev control system using Q-learning to achieve optimal parameter tuning and enhanced dynamic system performance. The study focuses on a basic levitation unit modeled after the simplified control system of an electromagnetic suspension (EMS) train. A Q-learning reinforcement learning environment and Q-learning agent were established for the levitation system, with a forward "anti-deadlock" reward function and discretization of the action space designed to facilitate reinforcement learning model training. Finally, a Q-learning-based method for self-tuning the parameters of the maglev control system is proposed. Simulation results in the Python environment demonstrate that this method outperforms the Linear Quadratic Regulator (LQR) control method, offering better control effects, improved robustness, and higher tracking accuracy under system parameter perturbations.

A comprehensive analysis of vehicle collision dynamics is presented using a two-mass model that simulates the impact of a vehicle against a rigid barrier. The model incorporates dual springs and dampers to examine the influence of spring stiffness and damping on a mass attached to the vehicle. The equations of motion are solved utilizing state variables, while energy principles are employed to establish correlations between vehicle deformation, impact force, and acceleration. Validation is conducted through comparison with crash test data from a 2023 Honda Accord LX 4-Door Sedan. Average deformation values are used to calculate acceleration, followed by a Monte Carlo simulation to analyze acceleration data recorded by the engine sensor, enabling the determination of vehicle speed through integration. Parametric regression is applied to optimize model parameters, resulting in a high degree of concordance between experimental and theoretical values. The model's accuracy is further verified through the analysis of velocity and deceleration profiles and the integration of the deceleration curve. The findings underscore the model's capability to replicate real-world crash dynamics, highlighting its potential to enhance vehicle safety system design. The innovation of this research lies in its simplified yet effective approach to modeling collision dynamics, offering significant insights into the relationship between vehicle deformation and occupant forces. This work advances the understanding of vehicle collision mechanics and provides a robust tool for the development of advanced safety features. The integration of theoretical and empirical data reinforces the model's reliability, contributing substantively to the field of automotive safety engineering.

The towing limits for self-propelled rail track maintenance equipment (SP-TME) are influenced by a multitude of factors, including the type and weight of the equipment, speed, braking capabilities, track and weather conditions, traction, engine power, driveline performance, coupler/towing link integrity, and safety regulations. This study investigates these variables to determine their impact on the towing limits of SP-TME. Unlike traditional rail vehicles, SP-TME possesses unique operational constraints and specifications, necessitating careful consideration of its independent mobility. An extensive analysis was conducted on the towing usage and overuse of SP-TME during travel mode, examining various scenarios that incorporate different combinations of trailing load, rail track grade, rail curvature, and weather conditions. These scenarios, ranging from normal to worst-case, aim to simulate demanding operational environments. The parameters evaluated include structural strength, traction, engine and driveline performance, wheel rolling and skidding, braking capabilities, trailing load, speed, and track and weather conditions. Results indicate that under normal and moderate conditions, the equipment can tow significantly higher loads than the defined base load. However, in special situations, such as negotiating tighter curves and steeper grades in adverse weather conditions, wheel skidding and locking emerge as limiting factors. Findings related to service and parking brake performance during steep grade descents, particularly when the trailer lacks independent braking capabilities, are also presented. Recommendations and cautions are provided to ensure safe and efficient operation of SP-TME under various conditions.

Through the deployment of bibliometric techniques and network visualizations, this analysis synthesizes the evolution and trajectories of autonomous driving research from 2002 to May 2024, as captured in the Scopus database encompassing 342 scholarly documents. This study was conducted to delineate the developmental contours, thematic emphases, and the expansive growth trajectory within this field, particularly noting a surge in scholarly outputs since 2017. Such growth has been primarily facilitated by breakthroughs in artificial intelligence and sensor technologies, along with burgeoning interdisciplinary collaborations and escalating academic and industrial investments. A meticulous examination of publication trends, document types, subject areas, and geographic distributions elucidates the multidisciplinary and international nature of this burgeoning field. Key thematic clusters identified—comprising foundational technologies, environmental sustainability, safety measures, regulatory frameworks, user experience, connectivity, and technological innovations—underscore the prevailing research directions and emerging focal areas poised to shape future autonomous mobility solutions. Notably, increased emphasis on environmental sustainability and regulatory frameworks has been observed, highlighting their critical roles in advancing and integrating autonomous driving systems. This study provides pivotal insights for researchers, policymakers, and industry stakeholders, fostering a nuanced understanding of the field’s dynamics and facilitating strategic alignments and innovations in autonomous mobility. The emergent research domains and collaborative networks revealed herein not only map the current landscape but also guide future scholarly endeavors in autonomous driving systems globally.

In the field of pedestrian re-identification (ReID), the challenge of matching occluded pedestrian images with holistic images across different camera views is significant. Traditional approaches have predominantly addressed non-pedestrian occlusions, neglecting other prevalent forms such as motion blur resulting from rapid pedestrian movement or camera focus discrepancies. This study introduces the MotionBlur module, a novel data augmentation strategy designed to enhance model performance under these specific conditions. Appropriate regions are selected on the original image for the application of convolutional blurring operations, which are characterized by predetermined lengths and frequencies of displacement. This method effectively simulates the common occurrence of motion blur observed in real-world scenarios. Moreover, the incorporation of multiple directional blurring accounts for a variety of potential situations within the dataset, thereby increasing the robustness of the data augmentation. Experimental evaluations conducted on datasets containing both occluded and holistic pedestrian images have demonstrated that models augmented with the MotionBlur module surpass existing methods in overall performance.

To address the lack of multi-perspective, real-time monitoring and management of operations and equipment in automated container terminals, a digital twin system targeted at monitoring automated container terminal equipment has been designed and developed. Based on the concept of a five-dimensional model of digital twins, a digital twin framework for monitoring automated container terminal equipment was constructed. The system's maintainability is enhanced through a layered design, which also reduces coupling between different functional modules. A multi-dimensional, multi-scale virtual scene was built and model consistency evaluations were conducted to verify the system. The system's operational efficiency was improved by optimizing model rendering with discrete level of detail (LOD) techniques. A multi-layered distributed solution for the digital twin system was proposed to achieve multi-perspective monitoring. Ultimately, using a specific automated container terminal as a case study, a system prototype was developed, realizing multi-perspective digital monitoring of terminal operations and equipment. This project offers a solution for the application of digital twin technology in the field of automated container terminals and promotes the development of intelligent digital terminals.