Brownfield redevelopment represents a key strategy for sustainable urban transformation, yet mobility management in capacity-constrained industrial campuses remains insufficiently explored. This paper proposes a digital twin framework for corporate mobility management applied to the Cesana brownfield in Mlada Boleslav. The model integrates travel demand, behavioural adaptation, infrastructure capacity, and policy scenarios within a dynamic simulation environment. Analyses indicate that coordinated multimo dal strategies and incentive mechanisms can significantly reduce car dependency without infrastructure expansion. The framework demonstrates potential as a transferable decision-support tool for mobility planning in industrial brownfield contexts.
The study evaluated chatbots designed as conversational assistants for drivers with the aim of reducing driver fatigue through appropriate and undemanding conversation. The introductory part included a questionnaire survey focused on identifying preferred topics of conversation while driving, which provided a basis for selecting the content focus of the experiment. This was followed by a subjective evaluation of selected chatbots using user experience metrics, focusing on their comprehensibility, naturalness, and ability to maintain attention without increasing mental load. The final part used the QFD method to assess the extent to which individual chatbot procedures and features met the set criteria. The output was a comparison of chatbots and a determination of their suitability for further extensive experiments.
Maintaining consistent product quality is a key challenge in automotive manufacturing, where high production volumes and product variability place significant demands on inspection processes. Industrial computer vision (ICV) offers an effective approach for automating visual quality control using modern image processing and deep learning techniques. This paper presents a case study of an components on a pre-assembly production line. The system integrates industrial cameras, edge processing devices, and neural network models trained on annotated production datasets. The paper describes the system architecture, dataset preparation, model training, and integration with production monitoring tools. The deployed system inspects several million components annually and demonstrates reliable defect detection performance under real manufacturing conditions. The study highlights the practical benefits of industrial computer vision for large-scale automotive quality control and outlines future development directions including digital twin integration and predictive analytics.
Road surface abrasions significantly contribute to vehicle collisions and mechanical failures worldwide. Traditional machine learning-based methods for road damage detection typically rely heavily on extensive manual annotations, making them costly, labour-intensive, and inefficient. To address this challenge, this paper proposes a label-efficient image processing framework based on self-supervised representation learning for road damage classification. Our approach integrates contrastive learning with a regularized redundancy reduction method, enabling the extraction of rich, discriminative features directly from unlabelled data. Contrastive learning separates positive and negative samples to learn robust feature representations, while a cross-correlation loss maximizes information content by minimizing redundancy. Regularization through variance and covariance loss terms ensures feature diversity and prevents informational collapse in the learned representations. Extensive evaluations in both in-domain and cross-domain scenarios demonstrate that our proposed method achieves superior performance compared to supervised techniques, even when trained with substantially fewer labelled samples. Thus, this work provides an effective, economical, and scalable solution to the critical challenges faced in automated road maintenance. The downstream task considered in this study is multi-class classification of road damage categories rather than binary damaged-versus-undamaged road detection.
Public transport systems face increasing pressure to improve reliability under constrained infrastructure and limited investment capacity. Small-scale bus priority interventions represent a cost-effective tool for improving operational performance, yet their implementation requires reliable identification and prioritisation of delay-generating locations. This paper presents a fully automated method for spatial identification and quantification of delay formation in bus transport systems based exclusively on high-resolution AVL data. The proposed approach reconstructs vehicle trajectories using a high spatial resolution vectorized road network and map-matching, enabling continuous delay estimation along the entire route rather than only at stops. Referential travel times are derived empirically from historical data using a percentile-based approach, allowing delay quantification independently of static timetables and accommodating heterogeneous operating conditions. The method supports aggregation across multiple trips, lines, and corridors, providing a system-wide view of delay accumulation and its infrastructurerelated causes. The methodology is demonstrated on regional bus services. An experimental evaluation of machine learning models as substitutes for referential journeys indicates that, given the available data structure, AI-based approaches fail to achieve meaningful predictive performance and cannot reliably replace the proposed statistical reference. The presented method offers a scalable and robust decision-support tool for prioritizing bus priority interventions and improving public transport reliability using operational data already available to most transport authorities.
This study explores the electrical activity of the thalamocortical system in the human brain during states of wakefulness, relaxation, and cognitive effort. Using non-invasive electroencephalography (EEG), we examined the dynamics of brain waves, particularly the alpha rhythm (8-13 Hz), and its variations during mental tasks. We introduced a novel metric, Quotus alpha (Qa), to quantify the asymmetry and slope of alpha waves. Our research included 26 participants across diverse groups: healthy adults, individuals with neurological abnormalities, and children diagnosed with ADHD. Results revealed distinct patterns of alpha wave asymmetry, with steeper left-sided slopes during cognitive tasks, correlating with increased mental effort and altered delta activity. These patterns differed notably between adults and children, suggesting developmental and functional distinctions in thalamocortical processing. The findings support the role of iterative thalamocortical interactions in cognitive processes, emphasizing their adaptability and complexity. In healthy adults, alpha dynamics reflected higher cognitive performance and recruitment of neural resources. Conversely, the atypical EEG patterns observed in children with ADHD and individuals with neurological conditions highlight potential diagnostic and therapeutic implications. This study enhances our understanding of the neural underpinnings of attention, cognition, and neurodevelopmental disorders while providing new methodological insights into EEG analysis.
The paper focuses on optimization approaches in the operational management of airlines, emphasizing the impacts of irregular operational situations (IRROP) on passengers. The introductory part describes the complexity of the business models of airlines and highlights the importance of operational management in minimizing the negative effects of IRROP. The historical analysis shows the evolution of approaches from reactive management concerning only costs aspect of IRROP to predictive management considering the impact on passengers as main factor, incorporating modern methods and collaborative approaches. Furthermore, the necessity of objective decision-support tools that minimize the impact on passengers and increase passenger satisfaction is discussed. The main goal of the paper is to identify key factors affecting the daily utilization of the aircraft fleet and to propose fuzzy linear programming as an effective method for optimizing operations.
This study analyzes the impact of COVID-19 restrictions on road accident trends in the Czech Republic from 2015 to 2024, utilizing a comprehensive dataset of over one million recorded accidents. The research highlights a significant decline in accident rates during the strict lockdown periods, correlating reduced mobility with fewer traffic incidents. As restrictions eased, accident rates rose again, revealing seasonal variations and regional disparities, particularly in urban areas like Prague. Findings suggest that the pandemic has reshaped commuting patterns and could influence future traffic management strategies. The analysis underscores the need for targeted policies to enhance road safety, especially during high-risk seasons.
This study aims to evaluate the viability of hydrogen fuel cell (FC) technology as a range extender in powered two-wheelers (PTWs), focusing on choosing efficient FC size and under a time-limited, constant power delivery FC control strategy. The analysis presented in this study sheds light on the feasibility of hydrogen FC technology as an alternative energy source for mobility applications. In this study, a 1D powertrain simulation model was created, which enables the efficient analysing of both fundamental and advanced behaviour of individual vehicle subsystems and control strategies, even at the early conceptual design phase. The simulation results show that hydrogen FCs are a promising technology for range extension in urban mobility.
Global Navigation Satellite Systems are a critical positioning, navigation, and timing source for various industries. However, their weak signal on Earth's surface makes them vulnerable to jamming. This paper explores the use of machine learning image recognition for categorizing GNSS jamming signals. The study uses data from a long-term monitoring campaign, with over 2,000 jamming events recorded. Seven commonly used jamming signal types were analyzed using the Residual Neural Networks (ResNet). Five different ResNet models with 18 to 152 layers were evaluated, with the best performing achieving a precision greater than 90% in determining the correct jamming signal category.
This study compares machine vision deep learning models based on convolutional neural networks to detect tree trunks in orchards from camera images, with a primary focus on apple trees. Two distinct datasets are used, one original with apple trees and another publicly available featuring vineyard trunks. Multiple deep learning models are tested and compared in order to evaluate their efficacy in tree trunk detection. Research not only provides insight into the performance of various models but also serves as a valuable benchmark for assessing achievable results in orchard-based machine vision applications. The findings contribute to the field’s understanding of tree trunk detection, facilitating advancements in agricultural automation.
This article addresses the challenges operators face in decision-making during the operational management of tunnels and other transport systems. Operators of complex systems must process vast amounts of information and suggestions from various devices, subsystems, and both internal and external sources. In addition, they receive requests from multiple entities. This overwhelming influx of data and demands places significant pressure on operators to evaluate and respond swiftly and accurately, which is often crucial to ensuring smooth operation of the entire transport system. To assist operators in making better decisions, new approaches are being introduced, such as expert systems and artificial intelligence. These tools aim to enhance decision-making not only during crises but also for routine operations and more complex tasks related to controlling and monitoring transport systems. The article outlines components of an expert system that uses fuzzy logic to address the complexities of acquiring certain data, particularly from predictive maintenance, which cannot be easily interpreted through simple operational interventions by the operator. Predictive maintenance also relies on decision-making supported by advanced algorithms, which are integrated with the systems technology and control framework.
This study explores the development and evaluation of external humanmachine interfaces (eHMI) for communication between autonomous vehicles (AVs) and pedestrians. By employing advanced virtual reality (VR) simulations and leveraging behavioral data from 600 survey respondents, the research examines the intelligibility and effectiveness of seven eHMI prototypes. The experiments utilized eye-tracking and spatial analysis to measure pedestrian response to AV signals in realistic virtual environments. The findings emphasize the potential of LED strip-based communication interfaces, demonstrating their superiority in terms of visibility, clarity, and implementation cost. This research contributes to the broader field of artificial intelligence applications in transportation, with a focus on ensuring safety and trust in autonomous systems interacting with vulnerable road users.
Understanding individual travel behavior is crucial for developing effective travel demand management strategies and informed transportation policies. This study investigates the factors influencing individuals’ mode choices by analyzing data from a comprehensive travel survey. We employ a deep neural network model to explore the relationships between survey variables and respondents’ transportation mode preferences, focusing on both observable and latent factors. The SHAP method is applied to interpret the model’s outputs, providing global and local explanations that offer detailed insights into the contribution of each variable to mode choice decisions. By identifying the key determinants of mode selection and uncovering the complex interactions between these factors, this research provides valuable insights for designing targeted policies that can better address transportation needs and influence sustainable travel behavior.
This paper presents a novel tool for optimising residential parking allocation in urban environments using linear programming techniques. The tool addresses the growing challenge of parking space management in cities by quantifying parking utilisation and accessibility. It employs a unique application of the transport problem from Graph Theory to allocate parking supply to household demand while considering real-world constraints such as walking distances and infrastructure limitations. The methodology involves the pre-processing of supply, demand, and distance matrix data, followed by an optimization process that minimises total walking distance and penalises unmet demand. The tool’s effectiveness is demonstrated through an experiment in the Czech town of Slany, showcasing its ability to evaluate current parking situations and assess the impact of potential changes in parking supply. Key outputs include the percentage of satisfied demand, utilization rates of parking supply, and detailed allocation maps. This approach provides urban planners and policymakers with valuable insights for developing efficient and sustainable parking solutions, while also highlighting areas for further research in data preparation and model refinement.
The primary objective of the presented research is to enhance an exmechanisms based on generalized additive models. This approach targets timeseries traffic data, where traditional methods may fall short in identifying complex, non-linear patterns of anomalies. In collaboration with Simplity s.r.o., we are extending their current data quality assessment tool to incorporate generalized additive models, providing a more robust and dynamic solution for monitoring and ensuring the reliability of traffic datasets. The integration of these models aims to improve the accuracy of anomaly detection, leading to more effective data management in transport systems and contributing to higher standards of data quality in the field of traffic informatics.
The goal of the paper is to introduce a universal approach for calculating integrated information assessment (IIA) in complex systems by utilizing the geometric product from geometric algebra (GA). Traditional models of consciousness try to explain how neural networks and cognitive processes give rise to a unified conscious experience. Quantum mechanics (QM) could provide a framework for understanding this integration by suggesting that conscious experience arises from entangled states across different system parts. Thanks to the high redundancy of neural networks, it is possible to realize different variants of cognitive processes in parallel and switch between them as needed. This opens up the possibility of hypothetically creating non-separable (not necessarily non-local) entangled models without requiring a quantum environment. The described IIA algorithm is derived from the assessment of entanglement in QM systems using GA. The results are shown on a set of illustrative examples.
This paper solves a modified version of the asymmetric traveling salesman problem with the possibility of omitting certain nodes and with a defined time limit for the total travel time, also referred to as the asymmetric orienteering problem (AOP). This problem belongs to the class of NP-hard problems. A proposed mathematical model maximizes the total score gained from visiting nodes within a predefined time limit. The possibility of exceeding the time limit, which results in a penalty to the total score, is also considered. The profitable penalty is examined, i.e., whether accepting the penalty can be advantageous for increasing the total score. The problem is demonstrated in a case study from the ski adventure race, organized in the Jizera Mountains in the Czech Republic.
This study presents a comprehensive multi-objective transportation model aimed at optimizing complex vehicle routing problems, which are nondeterministic polynomial time NP-hard due to spatial, temporal, and capacity constraints. In this study, the multi-objective transportation model integrates decisionmaker preferences with hybrid optimization techniques, including the approximatecombinatorial method, ant colony optimization and evolutionary algorithms. it seeks to minimize transportation costs, time, and emissions while accounting for real-world constraints such as fleet composition, customer demand, and servicelevel agreements. The techniques like multi-criteria decision-making methods are employed to refine the solution set, balancing objectives like cost, time, environmental impact, and service level. The novel optimization model is applied to a fuel distribution case study involving 18 customers and a heterogeneous fleet, where it optimizes vehicle routes to meet delivery requirements efficiently. The multiobjective transportation framework generates multiple feasible solutions, which are further narrowed down using decision-making frameworks to ensure alignment with organizational goals and decision-maker preferences. The integration of quantitative optimization techniques with qualitative decision-making processes makes this model robust and scalable, offering a practical tool for enhancing operational efficiency in transportation systems. This approach effectively addresses real-world logistics challenges, demonstrating significant improvements in route efficiency, cost savings, and environmental sustainability.
This paper deals with the analysis of the relationship between locations and types of crime observed in the Czech Republic. Cluster analysis of crime data based on the recursive Bayesian mixture estimation algorithm is used to identify crime hotspots and estimate local models of crime type. The experiments report that the 2D configuration of the algorithm allows the detection of crime hotspots online. The 3D configuration provides 29% more accurate crime type models than 2D clustering and alternative data mining algorithms. For the data set used, it was determined in which crime hotspots the most serious and most frequent types of crime can be expected to occur with the highest probability. The limitation of the study is the artificial support of the 3D clusters by the fully continuous data vector with the recoded values of the crime type. The potential use of the algorithm is expected in online web applications for sharing information on criminal offenses managed by the Police of the Czech Republic with the public and local government entities in the Czech Republic.
