Reliability Of Transportation System Research Articles

Traffic data imputation via knowledge graph-enhanced generative adversarial network

Traffic data imputation is crucial for the reliability and efficiency of intelligent transportation systems (ITSs), forming the foundation for downstream tasks like traffic prediction and management. However, existing deep learning-based imputation methods struggle with two significant challenges: poor performance under high missing data rates and the limited incorporation of external traffic-related factors. To address these challenges, we propose a novel knowledge graph-enhanced generative adversarial network (KG-GAN) for traffic data imputation. Our approach uniquely integrates external knowledge with traffic spatiotemporal dependencies to improve data imputation quality. Specifically, we construct a fine-grained knowledge graph (KG) that differentiates attributes and relationships of external factors such as points of interest (POI) and weather conditions, facilitating more robust knowledge representation learning. We then introduce a knowledge-aware embedding cell (EM-cell) that merges traffic data with these learned external representations, providing richer inputs for the spatiotemporal GAN. Extensive experiments on a large-scale real-world traffic dataset demonstrate that KG-GAN significantly outperforms state-of-the-art methods under various missing data scenarios. Additionally, ablation studies confirm the superior performance gained from incorporating external knowledge, underscoring the importance of this approach in addressing complex missing data patterns.

AI-enhanced fault-tolerant control and security in transportation and logistics systems: addressing physical and cyber threats

Transportation and logistics systems are becoming increasingly complex and critical to modern infrastructure. This paper proposes a novel AI-enhanced fault-tolerant control framework to address the dual challenges of physical malfunctions and cyber threats. By leveraging advanced machine learning algorithms and real-time data analytics, the proposed methodology aims to enhance the reliability, safety, and security of transportation and logistics systems. This research explores the foundations and practical implementations of AI-driven anomaly detection, predictive maintenance, and autonomous response systems. The findings demonstrate significant improvements in system resilience and robustness, making a substantial contribution to the field of intelligent transportation management.

Cybersecurity in Electric and Flying Vehicles: Threats, Challenges, AI Solutions & Future Directions

Electric and Flying Vehicles (EnFVs) represent a transformative shift in transportation, promising enhanced efficiency and reduced environmental impact. However, their integration into interconnected digital ecosystems poses significant cybersecurity challenges, including cyber-physical threats, privacy vulnerabilities, and supply chain risks. This paper comprehensively explores these challenges and investigates artificial intelligence (AI)-driven solutions to bolster EnFV cybersecurity. The study begins with an overview of EnFV cybersecurity issues, emphasizing the increasing complexity of threats in digital transportation systems. Methodologically, the paper reviews existing literature to identify gaps and assesses recent advancements in AI for cybersecurity applications. Key methodologies include AI-powered intrusion detection, threat analysis leveraging machine learning algorithms, predictive maintenance strategies, and enhanced authentication protocols. Results underscore the effectiveness of AI technologies in mitigating EnFV cybersecurity risks, demonstrating improved threat detection and response capabilities. The study concludes by outlining future research directions, highlighting the need for continued innovation in AI, quantum computing resilience, blockchain applications, and ethical considerations. These findings contribute to a clearer understanding of EnFV cybersecurity dynamics and provide a roadmap for enhancing the security and reliability of future transportation systems.

Travel Time Variability in Urban Mobility: Exploring Transportation System Reliability Performance Using Ridesharing Data

Travel time variability (TTV) is a crucial indicator of transportation network performance, assessing travel time reliability and delays. This study investigates TTV metrics within the context of shared mobility using probe data from transportation network companies (TNCs) in Chicago, Los Angeles, and Dallas–Fort Worth. Eight reliability metrics are analyzed and compared for each origin–destination (OD) pair in the network, including standard deviation (SD), the Planning Time Index (PTI), the Travel Time Index (TTI), the Buffer Index (BI), On-time Measures PR (alpha), and the Misery Index (MI), to evaluate their effectiveness in clustering OD pairs using K-means clustering. The findings confirm that SD, PTI, and MI are particularly effective in measuring travel time reliability and clustering within urban systems. This study identifies the most unbalanced supply–demand OD pairs/regions in each city, noting that low/medium-SD clusters around metropolitan airports indicate stable travel times even in high-demand zones, while high-SD clusters in downtown areas reveal significant traffic demands and unreliability. These patterns become more pronounced in study areas with multiple city centers. This study highlights the need for targeted strategies to enhance travel time reliability, particularly in regions like Dallas–Fort Worth, where public transportation alternatives are limited.

ResNet diagnosis of rotor faults in oil transfer pumps

To address rotor imbalance and misalignment in oil transfer pumps, an innovative diagnostic framework using Residual Network (ResNet) is proposed. The model incorporates advanced signal processing algorithms and strategic sensor placement to enhance diagnostic efficacy. A fault simulation test rig captured vibration signals from eight key measurement points on the pump. One-dimensional and multi-dimensional signal processing techniques generated comprehensive datasets for training and validating the model. Sensor placement optimization, focusing on the bearing seat's axial direction, inlet flange's vertical direction, and outlet flange's axial direction, increased rotor fault sensitivity. Time-frequency data processed via Short-Time Fourier Transform (STFT) achieved the highest diagnostic accuracy, surpassing 98 %. This study highlights the importance of optimal signal processing and precise sensor placement in improving the accuracy of diagnosing rotor faults in oil transfer pumps, thus enhancing the operational reliability and efficiency of energy transportation systems.

Resilience-Based Approach to the Measurement of Headway Inconsistency

Maintaining a consistent headway is a primary goal in bus operation, but it poses a significant challenge because of fluctuating traffic conditions and uneven passenger demand. This leads to inconsistent headways and even bus bunching, resulting in negative consequences such as increased travel time and reduced vehicle capacity. Various control strategies have been developed to alleviate headway inconsistencies, but most of them have focused on whether an inconsistency occurs while overlooking its duration. This study proposes a resilience-based approach to measure headway inconsistencies considering both the depth and duration of the impacts with deviations from the optimal headway. The approach is devised to analyze the cumulative impacts of inconsistent headways comprehensively. It goes beyond addressing only extreme cases, such as bus bunching, and considers persistently uneven headways. We first identify vehicle trajectories from smart card data, then compute deviations from the center of the preceding and following vehicles as an index of impact. Using the concept of resilience, cumulative impacts are measured and their distribution is utilized to set the failure criterion. The approach is applied to 320 bus lines in Seoul to measure the deviations of headways and is used to analyze the characteristics of the bus lines and links where inconsistencies frequently occur. The proposed method enables transportation planners to identify factors that can lead to headway inconsistency, facilitating tailored and data-informed planning processes. This study provides a comprehensive framework that recognizes line-specific and link-specific factors, thereby contributing to enhancing the reliability and efficiency of public transportation systems.

Методологічний підхід до розв’язання проблеми надійності функціонування автомобільних транспортних систем

An analysis of methods and techniques for solving the problem of increasing and ensuring the appropriate level of reliability of technical systems was carried out, which made it possible, based on the use of analogies, to find out the directions for ensuring the reliability of automobile transport systems. The essence of the concept of the reliability of the functioning of automobile transport systems for the transportation of goods and passengers from a methodological point of view is clarified. The insufficiency of regulatory and legal documentation for the development of methods and techniques for assessing the reliability of transport systems is substantiated. The synthesis of the concepts of "management" and "reliability" of the functioning of transport systems in comparison with similar concepts of technical systems is considered. The expert method and methodology of expert assessment of the reasons for failures of the production subsystem of the motor vehicle enterprise and its services are considered. It was found that factor analysis in combination with expert assessment makes it possible to assess the significance of the reasons for failure of transport systems and their frequency. In accordance with the classification of failures of technical systems, an analysis of their belonging to automobile transport systems was carried out. The classification of failures of technical systems according to different sets of features is analyzed. It was established that the classification of failures of automobile transport systems is based on the use of analogy with the classification of failures of technical systems, as well as on the results of the analysis of the factors of the causes of failures that occur in the production system of the carrier's enterprise. It was revealed that the selection of significant factors affecting the failure of automobile transport systems is based on the method of expert evaluations, their ranking by importance was carried out, and their relative frequencies were calculated. The results of the factor analysis of the reasons for the failure of the production subsystem of the motor vehicle enterprise are given. It is assumed that by analogy with technical automobile transport systems, they are also subject to decomposition by stages of the life cycle. Therefore, a sudden failure of automobile transport systems is characterized by a sudden change in the values of one or more initial parameters agreed with the customer, and a gradual one occurs as a result of a slow, gradual deterioration of the functioning characteristics of the subsystem or the system as a whole or a participant in the transportation of goods and passengers.

Edge computing and AI-driven intelligent traffic monitoring and optimization

Intelligent transportation system is a comprehensive system engineering, involving real-time data processing, security and privacy protection and other challenges. This paper discusses the key role of edge computing in intelligent transportation, especially its combination with SLAM technology. Edge computing enables faster data processing and response times by placing computing and data storage resources near the data source and end users, improving the efficiency and reliability of intelligent transportation systems. At the same time, edge computing can also enhance information security and privacy protection, and bring more possibilities for the future development of intelligent transportation systems. In the future, with the progress and development of technology, we can look forward to the deeper application of edge computing and SLAM technology in the field of intelligent transportation, bringing more innovation and development to smart city transportation.

Problems of the human factor in transport systems

Transport accident statistics and practical safety results indicate that technological solutions cannot ensure the viability (safety, sustainability, reliability and efficiency) of complex systems and structures without addressing human factors. This problem is especially acute for transport systems as highly complex technological and social structures aimed at ensuring the efficiency and safety of an entire sphere of human life. In transportation systems and technologies, the human factor plays a critical role. The successful operation of transport systems requires consideration of the human factor at all levels – starting from the development, design and planning of systems and technologies, training and education to the involvement of society in decision-making processes. Therefore, it is essential to develop the concept of human factor management and analyze the main problems in transport systems associated with the human factor to improve the safety, reliability and efficiency of their functioning. The article is devoted to the analysis of the human factor problem in transport systems and the search for solutions to manage it. The purpose of the article is to analyze the main problems in transport systems associated with the human factor and to develop the concept of human factor management for further search for solutions to improve the safety and reliability of transport systems. A set of instruments was proposed in the study to achieve the goal and solve the set tasks. Problems connected to the human factor in transport systems were analyzed. The main directions for solving problems with the influence of the human factor on the safety of transport systems were systematized. The main principles, models and ways of developing the concept of managing the human factor problem in complex systems, including transport, were outlined. A systematization of the risk management strategy was proposed. A matrix for the relationship between risk management strategies and approaches to managing the safety of complex systems was proposed. A new look at the principle of adaptive ergonomics as an implementation of the tolerance strategy was proposed, and applying the principle of adaptive ergonomics to transport systems was considered. The main motivation of the work is to attract the attention of the scientific and educational community to the problem of human factor and the need to use the concept of human factor management in the educational process to improve the general safety culture in society.

A Review of the Advances in Artificial Intelligence in Transportation System Development

In modern times, the rapid expansion of urban populations has intensified the urgency to optimize transportation systems, which has become an alarming issue in the face of urbanization and traffic congestion. This paper reviews the latest applications of Artificial Intelligence (AI) in the transport sector. It explores various AI methodologies, including Artificial Neural Networks (ANN), Genetic Algorithms (GA), Simulated Annealing (SA), Ant Colony Optimizer (ACO), Bee Colony Optimization (BCO), disruptive urban mobility, Fuzzy Logic Models (FLM), automated incident detection systems, and drones, which improve dynamic traffic management and route optimization. The study reveals that integrating these AI techniques with real-time data analytics improves traffic flow, automated incident management, and overall transportation efficiency. The results demonstrate that AI-driven systems, such as drones equipped with advanced sensors and AI algorithms, are increasingly capable of autonomous navigation, real-time monitoring, and predictive traffic management. These advancements in technologies, such as electric Vertical Take-off and Landing (eVTOL) aircraft, Hyperloop Transportation Technologies (HTT), Mobility-as-a-Service (MaaS) and autonomous delivery robots, contribute to smarter urban mobility solutions. However, it is important to focus on refining AI models for better performance, addressing challenges such as computational complexity and privacy concerns, and continuing to innovate in AI to improve the economic efficiency and reliability of transportation systems. Furthermore, to promote sustainability development in this sector, ethical considerations such as the protection of user information and the integration of the concepts of informed consent and human autonomy with community engagement programs should also be considered.

Public transport reliability in growing cities: the case of Asokore Mampong, Ghana

ABSTRACT The reliability of public transport systems is often reported in transportation literature as an essential factor in assessing the quality of urban transport services. Studies have largely examined the effects of transport reliability from the perspective of both customers and operators. Policies and interventions have been largely based on the experiences in developed countries, with a dearth of studies within the global south, particularly Africa, with rapidly urbanizing cities and where the transport sector is largely informal with several reliability challenges. This paper thus examines the reliability of public transport services in Ghanaian cities, using Asokore Mampong Municipality as a case study. Using a sample of 250 passengers interviewed at various bus terminals in the Municipality, the paper establishes five factors – the number of stops to pick up and drop off passengers, overaged and rickety vehicles, congestion on the roads, frequent breakdown of vehicles, and the number of vehicles available as affecting the reliability of public transportation in the study area. The paper establishes that public transportation in the study area is unreliable regarding waiting and boarding times of operating vehicles and recommends an effective regulation of public transport operators by the Asokore Mampong Municipal Assembly to improve reliability.

BUS KARO!: Live Bus Tracking System

Abstract: This research paper introduces 'BusKaro!', a transformative solution aimed at enhancing public transportation accessibility and promoting environmental sustainability. In the context of pressing global concerns regarding climate change and pollution, the imperative to transition from private vehicles to public transport is underscored. However, the efficiency and reliability of public transportation systems, particularly in densely populated urban areas, remain significant challenges. 'BusKaro!' addresses these challenges by leveraging advanced GPS technology to provide real-time tracking of buses, enabling users to access accurate information about bus locations and schedules. Beyond mere convenience, 'BusKaro!' offers a comprehensive platform that empowers commuters with features such as route planning, nearby bus stop location, and service disruption alerts. Moreover, 'BusKaro!' serves as a catalyst for broader societal change. By promoting the use of public transport, it contributes to reducing traffic congestion, lowering carbon emissions, and fostering sustainable urban environments. Through a detailed examination of its design, implementation, and potential impacts, this paper elucidates the transformative potential of 'BusKaro!' in shaping the future of transportation systems. Case studies and analysis further highlight the tangible benefits of adopting 'BusKaro!' for cities and commuters alike, illustrating its role as a pivotal tool in the journey towards a more sustainable future.

Cluster Analysis as a Basis for the Development of an Application Assessing the Reliability of Transport Infrastructure

AbstractIncreased demand for transport services and increased mobility of citizens can lead to a reduction in the level of reliability of transport systems. This in turn increases the demand for ways to assess the reliability of road infrastructure by both transport companies and individual users. The article presents the substantive basis of an application used to determine the reliability of transport infrastructure. Our approach was based on grouping information into clusters – based on the author’s proprietary clustering method. Its basis is the detailed analysis of the road infrastructure in terms of errors occurring on it, divided into conceptual, design and operational errors. The methodology consists of three stages of clustering (1) creating a database of sections with assigned errors, (2) determining the initial clusters, (3) creating a final database of clusters, and then assessing the reliability of the road infrastructure of the transport system on their basis. The assumption is that the application will remain open-ended – i.e. the database will be developed by users. The proposed methodology was verified on the example of the selected route in Poland (between Kalisz and Szczecin). Based on the results obtained during the experiment on the selected route, errors in the road infrastructure were determined. This, in turn, allowed us to find that there are a number of errors in the road infrastructure, including errors with a high frequency of occurrence i.e. the so-called permanent errors, which further confirms the need to create an application to assess the reliability of the road infrastructure.

PRINCIPLES OF MATHEMATICAL SIMULATION OF MINING VEHICLES USING APPLIED MATHEMATICAL SOFTWARE

The transportation of rock mass and coal in mining operations relies heavily on the efficiency and reliability of mining transport systems. This study delves into the intricate dynamics of chassis functions during transportation, emphasizing the critical role of transmitting dynamic stresses and mass to the road or rail structures. With a focus on increasing productivity, modern mining locomotives and heavy dump trucks now carry substantial adhesion adhesive weights, allowing for the hauling of heavier loads even on steep slopes. The integration of scientific simulations and research-based design methods aids in identifying and managing dynamic loading effects within mining vehicle chassis. This approach minimizes the transfer of dynamic loads onto the bolster structure, enhancing system reliability and operational efficiency. Computer-aided software plays a vital role in stream-lining the development process, reducing the need for costly physical prototypes and extensive testing in challenging mining environments. The study outlines simplified calculation schemes for mining transport utilities, balancing the need for accuracy with computational efficiency. By utilizing mathematical models and simulation techniques, the dynamics of mine locomotives and dump trucks can be accurately evaluated, guiding design decisions and operational strategies. The application of Lagrange equations and software tools like “Wolfram Mathematica” facilitates the generation of differential equations for dynamic analysis, providing insights into vehicle dynamics and road interactions. Overall, this research underscores the importance of advanced modeling and simulation techniques in optimizing mining transport systems, enhancing safety, productivity, and reliability in demanding mining environments. Purpose. To highlight the application of mathematical software as an instrument for mechanical system dynamics study, that allows scientifically substantiate the usage of new technical solutions during their development. Method. Development a system of differential equations using Lagrange equations of the 2nd order, which are solved in Wolfram Mathematica. Preparation of initial conditions and mass-inertia data can be done by any known software. The generalized mathematical model can be used for any vehicle suspending unnecessary coordinates. Results. Implementation of new technical solutions without scientific ground could be resulted in difficulties while exploitation. For its removal necessary to receive dynamical characteristics. Existing engineering calculation methods are not suitable, especially because of tight time and limited financing. The customized mathematical models can be developed and solved on demand using available software. A scientific novelty. Enhancement of theoretical and experimental research become possible owing to the usage of mod- ern approaches of dynamic systems simulation using applied mathematical software Wolfram Mathematica. Obtained model can be modified in few operation from one mass task into multibody system with maximum 69 free displacements (coordinates). Practical value. The possibility to receive customized simulation model fast, verified with increased quality for scientific purposes.

Research Progress and Prospects of Transit Priority Signal Intersection Control Considering Carbon Emissions in a Connected Vehicle Environment

Transit priority control is not only an important means for improving the operating speed and reliability of public transport systems, but it is also a key measure for promoting green and sustainable urban transportation development. A review of signal intersection transit priority control strategy in a connected vehicle environment is conducive to discovering important research results on transit priority control at home and abroad and will promote further developments in urban public transport. This study analyzed and reviewed signal intersection transit priority control at four levels: traffic control sub-area divisions, transit signal priority (TSP) strategy, speed guidance strategy, and the impacts of intersection signal control on carbon emissions. In summary, the findings were the following: (1) In traffic control sub-area divisions, the existing methods were mainly based on the similarity of traffic characteristics and used clustering or search methods to divide the intersections with high similarity into the same control sub-areas. (2) The existing studies on the TSP control strategy have mainly focused on transit priority control based on fixed phase sequences or phase combinations under the condition of exclusive bus lanes. (3) Studies on speed guidance strategy were mainly based on using constant bus speeds to predict bus arrival times at intersection stop lines, and it was common to guide only based on bus speed. (4) The carbon emissions model for vehicles within the intersection mainly considered two types of vehicles, namely, fuel vehicles and pure electric vehicles. Finally, by analyzing deficiencies in the existing studies, future development directions for transit priority control are proposed.

Забезпечення та підвищення надійності й ефективності процесу автомобільних вантажних перевезень побудовою дерева відмов структурних елементів транспортної системи

It was found that to ensure and improve the reliability of transport systems, it is necessary to have an effective toolkit for identification and classification of their failures, the number for a given period of operation and the determination of the required number of structural elements in the form of participants.
 It is shown that the construction of a tree of their failures is an effective tool for identification and classification of transport system failures. The procedure for its construction and its implementation is presented on the example of a transport system for the transportation of rock mass for the production of crushed stone. A tree of failures of this system was built and the main directions of its activity were developed to ensure the technical serviceability of the rolling stock and prevent its failures. Attention is focused on the coefficient of technical readiness of the vehicle fleet, an analysis of various types of formulas for evaluating this complex indicator of reliability and ways to increase its level are provided. Factors affecting the reliability of the transport system are determined and their list is given.
 It was found that regardless of the mutual location of the elements of the transport system, it is difficult to assign any of them to the main category. At the same time, the main argument is that the failure of any structural element of the transport system can lead to its complete failure, with the exception of elements connected in parallel.
 A number of assumptions have been made regarding the formation of the organizational and production structure of a trucking company based on functional characteristics, when the grouping and selection of elements of the company's production system is carried out in accordance with the commonality of performed functions. Areas of activity of trucking companies to prevent failures in the transport system caused by technical malfunctions of rolling stock are given.
 It was established that based on the results of the analysis of possible failures in the transport system, the optimization of the number of elements of the structural and functional scheme of the production process of transportation is carried out, when solving the problem, the minimum necessary number of its participants is determined. At the same time, the optimization criterion is selected taking into account the cost of the transportation process, which includes conditionally constant and variable costs directly for the movement of the object of transportation and its terminal processing on the way of passage.

Cyber Security Protection in Roadside Unit Based on Cross-Layer Adaptive Graph Neural Networks (Gnns) in Vanet

The proposed systems can improve cyber security in VANET applications by enabling efficient detection of complex attacks on the RSU component. The subsequent sections discuss the systems that are applied and support the suggestions for improving the VANET trustworthiness. VANETs and show that the utilization of Cross-Layer Adaptive GNNs can improve cyber security and LEARNING in VANET-based RSUs. As a result, the suggested system can provide robust ways for detecting cyber-attacks by: modeling the network architecture using graphs while combining information regarding several protocol layers to detect complicated interactions between the network entities and find the abnormal activities. the nature of the GNN enables it to update in real-time by adapting to the evolving attack patterns and the shifting network conditions, making them sturdy and flexible defense ways for cyber security. The proposed network e systems can efficiently detect multiple cyber threats and focus on reducing the number of false positives while maintaining low computation costs. Therefore, chances are that incorporating the Cross-layer adaptive GNNs into the RSUs can improve cyber security in VANETs, enhancing the robustness and reliability of prospective smart transportation systems.

Повышение надежности и эффективности функционирования транспортных систем в результате понижения их аварийности

The subject of the study is the state of accidents in the transport sector of the economy of the Russian Federation. The objectives of the work are to put forward proposals to improve the methodology for assessing accidents and damage at enterprises in the transport sector and to identify new types of accidents that occur in transport as a result of changes in technologies, processes and social conditions, in order to establish optimal ways to reduce the damage caused. The difference in the interpretation of the concepts of "accident rate" and "damage" in transport has been revealed. The practical significance of the study lies in the compilation of a typology of accidents, which makes it possible to plan a number of planned preventive measures and thereby prevent the onset of economic damage. The methodology of accident assessment at transport enterprises is characterized. The causes of the accident of transport systems have been established. Measures aimed at reducing accidents have been identified, contributing to improving the reliability of transport systems and increasing the efficiency of their operation. When assessing the nature of accidents, local and regional aspects of the use of transport systems are taken into account, determining their current state and readiness for use. The author's classification of accidents is given. Conclusions are drawn about the need to introduce a single comprehensive program of anti-accident measures for each individual transport sector.

Emerging Data-Driven Calibration Research on an Improved Link Performance Function in an Urban Area

The reliability of urban transportation systems is crucial for ensuring smooth traffic flow and minimizing disruptions caused by external factors. This study focuses on improving the stability and efficiency of transportation systems through the calibration of a refined link performance function while building upon the U.S. Bureau of Public Roads (BPR) model. To achieve this, we propose three customized algorithms—Newton’s method, Bayesian optimization, and the differential evolutionary algorithm—to calibrate the key parameters. Additionally, we conducted a sensitivity analysis to assess the influences of the model parameters on link performance. Numerical experiments conducted in Yuyao City demonstrate the applicability and efficacy of the proposed model and solution algorithms. Our results reveal that the Newton approach is notably more efficient than the Bayesian optimization algorithm and the differential evolutionary algorithm.

Geophysical Processes in the Arctic and the System Analysis of their Impact on Operation and Development of the Transport Infrastructure

The scientific research that has become the subject of consideration in this article is related to assessment of the influence of geophysical factors on sustainable functioning of transport systems and the system analysis of their impact on the transport infrastructure at the Arctic latitudes. The research is a new direction in the field of study of operational reliability of transport systems and scientific support for development of transport infrastructure in the Russian Arctic.The paper touches upon the issues of reliability and possible failures of technical equipment under the influence of space weather, and also discusses multifaceted problems of safety and efficiency of development of transport systems considering new data on the structure and properties of the lithosphere referring to thawing of permafrost and mineral deposits. A separate section is devoted to new information on seismic activity and seismic hazard assessment in areas of operation and promising development of the transport infrastructure of the Arctic zone of the Russian Federation (AZRF).Intellectual accounting and generalisation of the obtained interdisciplinary results together with their visualisation are provided by geoinformatics methods. The paper presents also the results of adoption of modern geodatabase management systems, of the application of modern technologies of geoportals and interactive spherical visualisations for qualitative presentation of new geophysical knowledge obtained in the course of research.