The study proposes an interpretable machine-learning approach for predicting bus arrival delays based on the Automatic Vehicle Location (AVL) system. The data describe vehicle movements and schedule adherence at successive stops, enabling delay estimation both along the route and across different time periods. Delays are defined as deviations between observed arrival times and scheduled times at individual stops, allowing both spatial and temporal delay patterns to be examined. The approach integrates ensemble machine-learning models with nested, temporally aware cross-validation and hyperparameter optimization. This validation strategy preserves the temporal order of observations, ensuring that model evaluation reflects realistic forecasting conditions and avoids information leakage. In particular, training and test sets are separated along the time axis, preventing future observations from influencing model training. Three ensemble models—Random Forest, XGBoost, and CatBoost—achieved comparable accuracy (RMSE ≈ 5.4 min; MAE ≈ 3.6 min; R² ≈ 0.44). Model performance was evaluated at the level of individual stop arrivals, reflecting short-term delay prediction under operational conditions. Comparable performance across models indicates that the observed patterns are not specific to a single modelling technique. This consistency suggests that the identified delay patterns are robust with respect to model choice rather than driven by algorithm-specific assumptions. The results show that the proposed approach enables the identification of combined temporal and spatial feature interactions associated with bus delay magnitudes within the analysed case study. These interactions mainly involve time-of-day effects and stop sequence, illustrating how delays accumulate as vehicles progress along the route. This pattern reflects the cumulative nature of delays in mixed-traffic operations, where early disturbances propagate toward downstream stops. SHAP values were used to provide a quantitative interpretation of these interactions and to identify the most influential predictors in the analysed models. The analytical structure, designed around weekly data partitions, supports delay monitoring and operational analysis. The results indicate that the proposed approach can support the analysis and interpretation of bus delay patterns within similar operational contexts.
Dhaka’s first mass rapid transit (MRT) system has marked a critical shift in the city’s public transport landscape, aiming to provide high-capacity, and reliable mobility. However, the effectiveness of this transportation mode depends on seamless first and last mile (FLM) connectivity. This study examines whether paratransit users differ meaningfully from active mode travelers in FLM connectivity for MRT system where paratransit like rickshaw is predominant. Using 1,513 valid home-interview responses, the research applies Principal Component Analysis (PCA) to extract three latent constructs, i.e., Travel Utility Perception, Travel Time & Cost Impact, and Socio-Demographic Profile. Structural Equation Modeling (SEM) is used to assess distinct models for walking and rickshaw as FLM modes. The findings reveal that the behavioral factors influencing paratransit use are closely aligned with those of active transport users, implying that in compact urban settings, paratransit may function as a substitute for active travel, potentially due to the lack of adequate walking and cycling infrastructure. Two models are then combined, referring to active FLM modes to investigate how their trip making behavior interact with each other. Key findings indicate that trip distance, fare sensitivity, time savings, and socio-economic status significantly influence FLM mode choice. The combined model of active travel yielded the best fit (RMSEA = 0.102; CFI = 0.848; TLI = 0.875), suggesting rickshaws function as pragmatic extensions of active travel rather than discrete modes. These results emphasize a continuum-based strategy for FLM planning that integrates rickshaw-friendly and walkable infrastructure to maximize accessibility and inclusivity along MRT corridors in built-up areas.
Disbanding of freight trains on sorting humps is a key process in railway transport. At most stations, this process still involves human operators controlling retarders and car speed. The lack of models that account for human participation limits both the reliable evaluation of hump automation measures and the application of computer-based train release planning tools. The purpose of this study is to improve the methods of modelling the disbanding trains on sorting humps to take into account human influence. The method of simulation modelling was used as the main research method. Determining the speed and time of rolling of the hitches is carried out by solving the differential equation of motion where distance is used as a variable. Setting the indicators of the process of disbanding of trains is carried out on the basis of the statistical processing of a series of calculation experiments on the rolling of cuts. In order to take into account the human participation in the process of disbanding the trains in the simulation model, additional restrictions are set on the choice of braking modes of the cuts, which are associated with the need to switch the attention of the operator during the simultaneous control of several braking positions, as well as with the transition of the controller between different tracks. The scientific novelty of the work consists in the improvement of the model of the disbanding of trains on the sorting humps, which, unlike the existing ones, allows to take into account the influence of operators of brake positions and speed regulators of cars on the indicators of the sorting process. The practical significance of the work lies in improving the evaluation of automated train release control systems and supporting the development of decision-support tools for train release planning under uncertainty in cut rolling characteristics and braking execution.
The following paper presents research aimed at identifying the most critical risks and their mitigations in Urban Air Mobility (UAM) operations. This topic is one of aviation's most significant challenges in the coming decades. Having many flying vehicles in a single airspace requires an innovative approach, rule redefinition, and traffic management. Some solutions are scalable and can be adapted from general aviation. Therefore, stakeholders must address new risks and implement dedicated methods while maintaining the highest level of operational safety. Simulation research is needed to validate solutions before systems operate in real environments. The response to those challenges is the development of a simulation tool that can serve as a test benchmark. The study is divided into two sections: identifying potential risks associated with the rapidly growing UAV market and its applications in urban environments and developing a simulation tool that addresses various Urban Air Mobility challenges. A set of test cases is presented to demonstrate the tool’s functionality and capabilities for further analysis. The paper reviews the United States and European Union approaches to UAM integration, including NASA, FAA, SESAR, and EASA initiatives, and highlights differences in operational concepts and regulatory frameworks. The research identifies major categories of risks related to UAV operations, including technical failures, environmental hazards, human factors, and cybersecurity threats. Long-term challenges associated with increasing traffic density, autonomous operations, and airspace organization are also discussed. The research evaluates scalable safety solutions derived from commercial aviation and analyzes urban airspace concepts such as layers, zones, sky-lanes, and sky-corridors. The developed simulation environment, implemented for the Warsaw metropolitan area, enables modeling of large-scale UAV and VTOL operations, no-fly zones, vertiport hubs, and traffic distribution. The results demonstrate the importance of dedicated traffic structures, altitude separation, and decentralized traffic management systems in ensuring safe and efficient Urban Air Mobility operations.
The rapid electrification of road transport is reshaping the automotive market, particularly in Europe, where battery-electric vehicles (BEVs) are becoming increasingly prevalent. Evaluating vehicle-level technological performance is critical for understanding competitiveness in this evolving landscape. This study aims to systematically assess and rank 174 BEV models in the B and C segments, considering technical and performance criteria, to identify technologically competitive vehicles and to compare the technological performance of Chinese manufacturers with that of established global brands. Eight technical indicators were selected to capture key dimensions of competitiveness: driving range, battery capacity, energy efficiency, fast-charging speed, maximum speed, trunk capacity, acceleration, and full-charging time. An integrated multicriteria decision-making (MCDM) framework was applied, combining Shannon’s entropy, the CRITIC method for objective weighting, the TOPSIS method for ranking, and the Borda count for consensus aggregation. The proposed MCDM framework offers a replicable and robust approach for benchmarking technological performance across heterogeneous BEV models. The analysis reveals that European manufacturers, particularly German brands, continue to dominate the highest-ranking positions due to well-established engineering capabilities, long-term investment in innovation, and patent-based technological leadership. Chinese producers included in the sample, while representing a growing share of the market, display strong performance in battery technology, cost efficiency, and manufacturing scale. Yet, their presence in the European B and C segments remains limited. The results further highlight that BEV competitiveness is multidimensional, reflecting trade-offs between performance, efficiency, and usability. C-segment vehicles tend to prioritise extended driving range and larger battery capacity, whereas B-segment models focus on energy efficiency and compact design, catering to urban mobility requirements. The study demonstrates that a single attribute cannot determine technological competitiveness; rather, it depends on a balanced combination of multiple criteria. These findings provide policymakers, manufacturers, and investors with insights into the technological strengths and weaknesses of contemporary BEV models, supporting evidence-based discussions on sustainable mobility and industrial development.
Reliable and continuous operation of electric locomotives requires continuous and accurate assessment of the technical condition of traction motors. Traditional SCADA monitoring systems used in rail transport are typically limited to independent threshold monitoring of a small number of electrical and thermal parameters, which reduces their sensitivity to complex and early-stage faults. This paper proposes a multiparameter approach to real-time traction motor technical condition diagnostics based on a formalized condition assessment model. The proposed methodology combines seven diagnostic parameters of electrical, thermal, mechanical, acoustic, and magnetic nature into a single diagnostic framework. Each parameter is converted into a normalized local condition index based on engineering threshold values, and its diagnostic significance is determined using correlation weighting. This is the basis for the formation of an integrated technical condition index, which is used to automatically classify motor condition into three classes: normal, warning, and critical. This article presents the Pearson correlation coefficients between diagnostic parameters and the degradation indicator, as well as the corresponding normalized weighting factors used to calculate the integral index. To quantitatively assess the diagnostic effectiveness, formal statistical validation was performed using a confusion matrix, as well as precision, recall, F1-score, and overall accuracy metrics. The approach was validated on a mixed dataset, including limited real-world operational measurements and simulated degradation scenarios in the Siemens TIA Portal environment, which allowed for verification of the diagnostic logic under controlled conditions. The obtained results demonstrate that multiparameter correlation-weighted diagnostics provide a more sensitive and interpretable assessment of the technical condition of a traction motor compared to traditional single-parameter SCADA monitoring and create a basis for the development of predictive maintenance for traction rolling stock.
Modern rail transport requires precise traffic management to ensure safety and operational efficiency, which is only possible when the Control‑Command and Signalling (CCS) system relies on accurate, real‑time information about the location of rail vehicles within its area of influence. Precise positioning is essential for maintaining safety levels, optimizing traffic flow, and ensuring the smooth functioning of the railway network. Global Navigation Satellite Systems (GNSS) enable continuous monitoring of train positions, creating opportunities to improve infrastructure efficiency and traffic management by supporting more flexible control strategies, better use of existing capacity, and the introduction of advanced automation. However, implementing GNSS in CCS systems presents challenges related to the absence of defined evaluation criteria, system architecture, and verified levels of accuracy and availability required in rail operations. These issues highlight the need for developing guidelines and technical and formal assumptions that define and characterize the potential application area of GNSS in CCS, helping to determine operational boundaries, constraints, and compliance with railway standards. This publication outlines a process designed to answer the fundamental question of: is it possible to use satellite systems in railways, and if so, to what extent? The implementation of this process begins with an analysis of the current state of knowledge and technology, including requirements and guidelines for the use of satellite systems in railway applications and their role in automatic train operation within the European Rail Traffic Management System / Automatic Train Operation (ERTMS/ATO). It then incorporates research and simulations assessing the availability and accuracy of satellite positioning under various operational conditions, providing insight into the performance of GNSS in the railway environment. The process concludes with the identification of potential areas of implementation and directions for further research, creating a coherent basis for assessing the feasibility and scope of GNSS use in railway traffic control and management systems.
The Automatic Identification System (AIS) has become a fundamental component of modern maritime navigation, supporting real-time vessel tracking, collision avoidance, and traffic management. However, its protocol was developed without cybersecurity considerations, resulting in structural vulnerabilities that increasingly threaten operational safety and reliability. AIS broadcasts are neither encrypted nor authenticated, allowing adversaries to intercept, manipulate, or fabricate messages with minimal technical effort. This exposes maritime operators to a spectrum of cyber threats, including identity spoofing, false-data injection, replay attacks, and targeted radio-frequency jamming. As AIS data is routinely integrated into Electronic Chart Display and Information Systems (ECDIS), Vessel Traffic Services (VTS), and autonomous navigation modules, these weaknesses propagate across interconnected maritime infrastructures, amplifying the potential consequences of compromised data integrity. This article provides a systematic assessment of the core vulnerabilities inherent in AIS communication, emphasizing the absence of cryptographic protections, the ease of broadcasting falsified vessel information, and the susceptibility of the VHF communication channel to intentional interference. The operational impacts of these threats are analyzed with regard to navigational decision-making, port coordination, surveillance accuracy, and maritime domain awareness. Particular attention is given to the risks resulting from excessive dependency on AIS as a primary sensor input, especially in automated or minimally manned operational environments. In response to these challenges, the study outlines a set of technical and organizational countermeasures. Proposed solutions include the integration of authentication layers and lightweight encryption into future AIS/VDES protocols, the deployment of anomaly-detection algorithms for real-time identification of spoofed or inconsistent data, the cross-verification of AIS information with independent sensors, and the systematic maintenance of AIS-capable equipment. Additionally, the article highlights the need for harmonized regulatory reforms and enhanced cybersecurity training for crews and port operators. Collectively, these measures aim to strengthen the resilience of maritime communication systems and ensure that AIS continues to serve as a reliable component of navigational safety in an increasingly digitalized and threat-exposed maritime domain.
Fuel efficiency is a critical issue in road freight transportation because fuel costs represent a major component of truck operating expenses amid increasingly stringent emission standards. Although Euro 4 technology is designed to reduce emissions compared with Euro 3, its real-world fuel-efficiency performance under uphill, load-varying, and speed-varying conditions remains insufficiently understood. This study evaluates the comparative fuel efficiency of Euro 3 and Euro 4 five-axle semi-trailer heavy-duty trucks and develops a terrain-sensitive evaluation framework using manufacturer-based telematics data from freight operations on Indonesian uphill toll-road segments. Fuel efficiency was expressed in km/l, where higher values indicate better performance. A four-factor factorial structure was applied, consisting of emission standard, operating speed class, load factor class, and road gradient category, resulting in 54 operational combinations. After cleaning and outlier filtering, 828 valid records were analyzed. A four-way factorial ANOVA examined main and interaction effects, while direct comparisons between Euro 4 and Euro 3 trucks were conducted under identical speed, load, and gradient combinations, producing 27 matched scenarios. The results show that Euro 4 trucks generally achieve higher fuel efficiency on flat segments, particularly at low to medium speeds with moderate to high loads. However, this advantage becomes less consistent on hilly and mountainous terrain. Under hilly and mountainous conditions, particularly at higher speeds and heavier loads, differences between Euro 4 and Euro 3 trucks become less consistent and often statistically non-significant. A key finding is that the fuel-efficiency benefit of Euro 4 technology decreases as road gradient and operating demand increase. This indicates that newer emission-control technology does not necessarily provide uniform energy-efficiency advantages across operating conditions. The study concludes that fuel-efficiency performance is shaped by the interaction among emission standard, speed, load factor, and road gradient rather than by vehicle technology alone. The findings support a terrain-sensitive fleet deployment strategy, suggesting that Euro 4 trucks should be prioritized on relatively flat corridors and moderate operating regimes, while Euro 3 trucks may remain operationally comparable in selected high-gradient and high-load conditions, although Euro 4 remains preferable from an emission-control perspective.
Accurate and timely prediction of the urban traffic congestion index (TCI) is crucial for implementing proactive traffic management and alleviating urban congestion. To address the limitations of single models in capturing both complex temporal dependencies and high-dimensional feature interactions, this paper proposes a novel hybrid prediction framework that synergistically integrates a Long Short-Term Memory (LSTM) network and a Light Gradient Boosting Machine (LightGBM). The model is designed to perform dual-stream learning: the LSTM module extracts medium- and long-term temporal patterns from historical TCI sequences, while the LightGBM module concurrently learns discriminative feature representations from the structured traffic data. A genetic algorithm (GA) is employed to optimize the fusion weights of the two components, constructing an adaptive and cohesive LightGBM-LSTM prediction model. The proposed framework was validated using real-world TCI data collected from three representative segments with varying congestion levels (mild, moderate, and severe) on Chengdu’s Third Ring Road, covering a period from September to October 2024. The experimental results demonstrate that the hybrid model significantly outperforms both standalone LSTM and LightGBM baselines across all test scenarios. Specifically, it achieved accuracy improvements of 4.87% and 33.06% in mildly congested sections, 26.80% and 22.32% in moderately congested sections, and 47.87% and 10.47% in severely congested sections, respectively, measured by the Mean Absolute Percentage Error (MAPE). These findings confirm that the proposed GA-optimized LightGBM-LSTM hybrid model effectively enhances TCI prediction precision and robustness by leveraging complementary strengths of sequence learning and feature engineering. The study provides a reliable and efficient analytical tool for short-term traffic state forecasting, offering valuable support for the development of data-driven and refined urban traffic management strategies.
The paper examines the growing importance of cybersecurity in rail transport within the broader framework of critical infrastructure protection. The ongoing digital transformation across all sectors of the economy increasingly affects the functioning of the railway system, which constitutes a strategic component of the national transport network. The adoption of information and communication technologies, as well as industrial automation, has facilitated the deployment of advanced solutions, such as intelligent traffic management systems and predictive maintenance tools, for railway infrastructure. These technologies have significantly enhanced the efficiency, safety, and reliability of transport operations; however, they have also introduced new and complex cybersecurity challenges. The progressive integration of information technology and operational technology systems increases the vulnerability of transport infrastructure to cyber threats that may result in service disruptions, data integrity breaches, or the reduced availability of critical functions. The article underscores the necessity of developing and maintaining an effective cybersecurity management framework in the transport sector, incorporating incident response mechanisms, threat intelligence sharing, and the adoption of standardized procedures and best practices aimed at safeguarding digital assets. Particular attention is devoted to the role of AI as a key driver of digital transformation in the transport sector. Although AI-based technologies enhance operational efficiency and enable greater process automation, their deployment simultaneously introduces a new set of challenges. These challenges primarily relate to data quality and security, system interoperability, and ensuring the transparency and reliability of algorithms used within critical transport infrastructure. The authors emphasize that effective cybersecurity management requires a holistic and integrated approach that combines technical, organizational, legal, and human capital dimensions. Equally important is fostering close cooperation among infrastructure managers, transport authorities, and carriers to ensure a coordinated and resilient cybersecurity framework across the entire transport ecosystem.
This paper addresses the improvement of energy efficiency in intermodal freight trains by reducing aerodynamic drag through optimisation of container arrangement on railcars. In current terminal practice, loading plans are usually driven by local criteria such as minimising crane operating time or travel distance, which may result in non-compact cargo configurations and increased aerodynamic losses during train movement. A mathematical model of aerodynamic drag is formulated using position-dependent drag coefficients and a gap-penalty function that reflects the length and location of empty slots in the train consist. The loading problem is modelled as a constrained assignment task with the primary objective of minimising crane working time. Three approaches to generating the initial loading plan are analysed: a slot-priority heuristic, a greedy algorithm, and an ant colony optimisation algorithm. On this basis, a dedicated post-processing algorithm is applied, which iteratively relocates containers towards the front of the train, reduces gaps between units, and preserves all technical and operational constraints. The method is implemented by linking a FlexSim simulation model of an inland intermodal terminal with Python-based optimisation procedures. Nine scenarios with different shares of containers from the road zone and storage yard are evaluated using 16 replications each. The results show that the proposed procedure can reduce estimated aerodynamic drag by approximately 5–9%, at the cost of increased crane operating time. An energy balance comparison indicates that the additional terminal energy consumption is significantly lower than the traction energy savings, confirming that aerodynamic criteria should be explicitly incorporated into train loading strategies.
The article presents research problems related to the operation of classical switch heating solutions and systems. This is due, among other things, to the excessive use of electricity and the strive to optimize these solutions. An overview of research related to various electric switch heating (ESH) systems is presented extensively in Chapter 2. This article presents selected results of thermal imaging measurements of ESH electric switch heating components used to heat railroad switches under the BRIK2/0036/2022 project. The study was carried out for different types of ordinary railroad switches most commonly used on the PKP PLK network. The studies included various positions of the switches in the normal position (straight track) and the non-normal position (switch track). The tests were conducted on real objects and under conditions allowing for the snowing of critical elements of the switch. The use of thermal imaging cameras and classic thermoelectric sensors with a recorder allowed for the assessment of the effectiveness of existing switch heating systems as well as proposed modifications. The results of the study made it possible to observe the temperature distribution for different switch design solutions and switch heating systems, and to interpret the results. Based on the obtained results, it can be concluded that the new solution in the area of electric switch heating with dedicated radiation overlays is more efficient compared to the classic solution. The use of thermal imaging cameras in research has enabled the execution and recording of heating sequences of the switch and imaging in the visible spectrum of the electromagnetic spectrum for snow melting in critical areas, such as the switch zones. Further work will focus on preparing a diagnostic manual for services responsible for the maintenance of railway switches, including the use of thermal imaging diagnostics. Subsequently, the authors will prepare the assumptions and criteria for the development of an application to assist in the interpretation of thermograms, which can be a tool supporting the diagnostics of electric switch heating (ESH) devices. The creation of a simple diagnostic tool that allows for the assessment and facilitates the interpretation of the obtained thermogram may enable maintenance services to make appropriate decisions regarding the operation of ESH systems.
Operational performance in commercial airlines increasingly depends on the effective alignment of internal capabilities operating within highly regulated, reliability-critical environments. While existing aviation research frequently examines operational drivers in isolation, limited evidence integrates multiple theoretical perspectives or provides empirical insights from emerging Asian markets. This study develops and empirically validates a multi-theoretical structural equation model grounded in the Resource-Based View (RBV), Total Quality Management (TQM), and Compliance Management Theory to explain how four strategic enablers—ground operations, crew performance, maintenance and reliability, and regulatory compliance—collectively influence operational performance in Thailand’s commercial airline sector. A convergent mixed-methods design was employed, incorporating qualitative insights from 15 executives and regulatory specialists and quantitative data from 412 employees across six Thai airlines. Confirmatory factor analysis demonstrated strong reliability and validity, and covariance-based SEM indicated good model fit (RMSEA = 0.056, CFI = 0.938, GFI = 0.916, SRMR = 0.048). All four strategic enablers exerted significant positive effects on operational performance, with regulatory compliance (β = 0.46) and ground operations (β = 0.42) emerging as the strongest contributors. Qualitative findings reinforced these results, emphasizing compliance culture, turnaround precision, and crew coordination as dominant operational priorities. Subgroup analysis revealed clear distinctions between full-service carriers (FSCs) and low-cost carriers (LCCs), reflecting differences in organizational structure, safety culture, and maintenance strategies. The study offers a unified, empirically tested framework that advances theoretical understanding of airline capability systems and provides actionable guidance for managers seeking to strengthen safety governance, frontline execution, and operational reliability in emerging aviation markets.
This study is based on a quantitative survey of a sample of Polish local government units (LGUs) and existing data for these entities. Cluster analysis identified two groups of municipalities - those less and more engaged in implementing actions for sustainable transportation. Subsequently, an econometric logit model allowed the analysis of factors influencing Polish LGU's compliance with the Act on Electromobility and Alternative Fuels. This Act mandates a specific quota of zero-emission vehicles in municipal fleets. Our research identifies key determinants affecting LGUs' adherence to these environmental standards, focusing on financial capacity, infrastructural readiness, environmental and socio-economic factors relevant to adopting zero-emission public transportation. This research contributes to the global discourse on environmental compliance and urban transport policy, underlining the importance of understanding local nuances in implementing such initiatives. It provides crucial information for policymakers internationally, aiding in developing more effective and tailored green transportation strategies sensitive to local contexts.
Linking urban bus transit analysis with traffic engineering contributes to safe and efficient movement of people. A simulation model capable of modelling passenger service at bus stops and traffic conditions around bus stops is an excellent tool in transit efficiency assessment. In the paper, the analysis focused on street segments between intersections with bus stops used by regular bus service, various other transit providers (private operators), and other users (taxis, passenger cars). Large numbers of minibuses of non-urban bus operators stopping at bus stops cause significant disruptions in the traffic flow. The apparent differences in the efficiency and operation of the stops designed for use by local buses and those used by other users required additional analyses. In the probabilistic and simulation models of bus stop operation developed so far, did not use car-following models for the modelling of the movement of buses and other vehicles at and in proximity of bus stops. This theory is used in off-the-shelf traffic microsimulation software packages, but they do not allow a reliable representation of how bus stops operate under the deregulated service of passengers (oversaturated bus stops with a variable number of service channels). This study analyses the movement of buses and other vehicles at a bus stop area using a car-following model with fuzzy logic applied for parameter estimation. A microscopic simulation model was built and tested. The selection of the shape of fuzzy sets and membership functions was based on multiple simulation runs. The model simulates queuing and delays imparted on buses and traffic flow in lanes adjacent to bus stops used by city buses and other transit providers. The simulation results were compared with the real-world processes with the use of the author's two-stage method. Verification based on the PROC and SDDIST indicators showed that the simulated and observed distributions of travel time and delay were highly consistent, with maximum deviations below approximately 10%. The analysis also confirmed that the model accurately reproduces average delays caused by bus queues and vehicle interactions near shared-use stops. The analyses demonstrated that the fuzzy logic-based car-following model proposed in this study is suitable for planning, designing, and relocating urban bus stops used by various operators. The feasibility of the developed model and directions of its further development were evaluated.
This article presents an approach to decision-making for the implementation of electric passenger cars in enterprise fleets, considering both economic and environmental factors. The literature review is focused on fleet composition and the impacts of technological change. The paper examines practical aspects of introducing alternatively powered vehicles into corporate fleets and proposes a model for optimal fleet composition. The final section provides a case study on the adoption of electric vehicles (BEV, HEV) in selected fleets in Poland. The conclusion highlights key opportunities and constraints associated with integrating electric vehicles into fleet operations. The model proposed in the article incorporates Total Cost Ownership (TCO) of fleet and environmental criteria, as well as various forms of vehicle financing and the resulting limitations on vehicle mileage and duration of use, together with budget constraints and the discounting of cash flows over time. Importantly, vehicle depreciation and several other parameters are treated as nonlinear functions of multiple variables. As the research presented in the paper demonstrates, the introduction of electric vehicles into fleets is currently unprofitable in Poland, particularly for short-term use in company car fleets. The persistently higher purchase prices of such vehicles compared with internal combustion vehicles of a similar standard, combined with their substantially higher depreciation during the initial period of use, are not offset by lower operating costs. Electric vehicles gain an advantage only when environmental objectives are assigned a sufficiently high weighting. At the same time, over sufficiently long operating periods, the economic performance of electric vehicles may prove more favourable, although there remains considerable market uncertainty concerning price formation and the residual values of these vehicles.
With the rapid development of the global economy, international trade plays a crucial role in the economic growth and resource allocation of various countries. As an important logistics channel connecting China and Europe, the China-Europe Railway Express has significantly contributed to supporting the "Belt and Road" initiative and promoting regional economic cooperation. However, the efficiency of container transportation directly affects the quality of transport and operational costs, thus improving the loading efficiency of container transport has become an urgent issue in modern logistics management. Most existing container transport methods rely on traditional scheduling and loading strategies, which often fail to meet the real-time demands posed by dynamic market changes. This paper proposes a new real-time loading optimization framework based on Radio Frequency Identification (RFID) technology to address the container transport optimization problem for the China-Europe Railway Express. This framework manages real-time cargo requests through task queues and dynamically invokes the Iterative Heuristic Tree Search (IHTS) algorithm by the core decision-making component to generate loading plans and pass them to the execution component. By constructing a data generation model based on a normal distribution, this paper simulates the recognition probability of RFID tags to enhance decision-making accuracy. Experimental results show that the proposed method completed 45 tasks within 60 minutes, which is 50.00% higher than the improved Q-learning algorithm and 28.57% higher than the genetic algorithm based on the Metropolis criterion. In terms of path optimization, the length of the path in this method is 108 meters, significantly shorter than the 125 meters of the improved genetic algorithm and the 141 meters of the Q-learning algorithm. In addition, the total transportation cost of the proposed loading optimization method is 608.28 yuan. This cost integrates the vehicle transportation distance cost, the delay penalty caused by failure to load on time, and other operational losses. Experimental results demonstrate that this real-time loading optimization framework not only significantly enhances container loading efficiency but also effectively reduces operational costs, showing promising application prospects and practical value.
An important aspect of an efficient transport system in urban areas is to provide passengers with a high level of access to public transport stops. In areas with dense and diverse development, locating such facilities in the transport network is a significant challenge and a complex decision-making problem. Therefore, it is necessary to support it with appropriate analyses before making the final decision. Many approaches in this field require spatial determination of the range of impact in the form of a catchment area, which can be constructed in various ways. Therefore, the study aimed to compare two methods for designating catchment areas of public transport stops in urban locations, i.e., circular buffers and isodistances, to support more informed decisions. The novelty of the approach was to enable a comparison of both approaches at the level of individual stops and the entire network, and the introduction of a way to designate the optimal buffer radius that best approximates the isodistance-based area. A new measure based on the weighted average percentage of area coverage is introduced. Such analysis allows us to use both approaches interchangeably. The analysis was conducted for the public transport stops in a large metropolitan area in southern Poland, GZM Metropolis (Górnośląsko-Zagłębiowska Metropolia), which differed both in terms of their location with respect to the city center and in terms of the structure of the surrounding road and street network adapted to pedestrians. The results of the analysis suggest that the percentage of buffer and isodistance coverage with the same radius sizes and values may range from about 34% to 61%. Based on the performed analysis, the best results of the substitution of the isodistance model with the circular buffer model are obtained if the buffers are 100-200 m smaller than the isodistances. In future work, it is also worth examining the dependence of the values of the analyzed measures for a single public transport stop, as well as the average values for the entire set of stops, on the parameters of the road and street network within the buffer or isodistance.
The article discusses the method of reconstructing the ejection process. Measurement data were obtained during field tests on an actual object. These data served as the basis for validating the mathematical model of the seat-pilot system’s motion. This model describes the spatial motion of the K-36DM ejection seat. The article includes a description of the mathematical model and a comparison of the measured motion parameters with the calculation results. It also presents computed parameters that were not recorded during the tests. Particular attention was paid to the reconstruction of the flight trajectory and seat rotation and the determination of the G-forces acting on the pilot. The primary objective of the research was to develop a mathematical model of the pilot ejection process using the K-36DM ejection seat. In addition to classical equations of motion, such as linear motion of the seat along the guide rails and motion of the seat along the rails with rotation around the lower pair of rollers, the model also considered the free motion of the seat-autopilot system, taking into account the forces acting on the seat-autopilot system. In this work, four phases of the chair movement were modeled, i.e. phase 1: After activation of the first pyrotechnic charge, the seat moves in a straight line along the guide rails until the two upper pairs of rollers disengage from the rails; phase 2: The seat continues moving along the guide rails using the lower pair of rollers until it exits the cockpit. Simultaneously, the seat begins to rotate relative to this pair of rollers; phase 3: The seat moves through the air; initially, it is propelled by the second pyrotechnic charge, providing the necessary flight altitude; phase 4: The pilot separates from the seat and descends under a parachute.
