Efficient Transport System Research Articles

Analysis of the truck haulage system in an underground mine using long-term log data

ABSTRACT This paper addresses the collection, analysis, and optimization of log data related to the truck haulage system in a mine. After selecting an underground mine in South Korea as the research area, the Mine Production Management System (MPMS) using Bluetooth beacons and tablet PCs was utilized to collect log data related to the haulage system over 18 months. Through the analysis of the collected log data, strategies to enhance the productivity and efficiency of the haulage system were derived. Weekly analysis of log data files was performed, identifying periods with abnormal truck cycle times and conducting an analysis of the underlying causes. It was revealed that the truck cycle time is influenced by the haulage system, equipment operation, and the log data collection system. To shorten truck cycle times and improve the productivity and efficiency of transportation systems, continuous collection and regular analysis of log data and dynamic system and equipment operation based on this are important. Analyzing log data collected over an extended period can help predict future trends in the haulage system and formulate measures and strategies in response. Therefore, it can serve as a valuable decision-making tool for establishing short-term, medium-term, and long-term production and operation plans.

Gauging Public Acceptance of Conditionally Automated Vehicles in the United States

Public acceptance of conditionally automated vehicles is a crucial step in the realization of smart cities. Prior research in Europe has shown that the factors of hedonic motivation, social influence, and performance expectancy, in decreasing order of importance, influence acceptance. Moreover, a generally positive acceptance of the technology was reported. However, there is a lack of information regarding the public acceptance of conditionally automated vehicles in the United States. In this study, we carried out a web-based experiment where participants were provided information regarding the technology and then completed a questionnaire on their perceptions. The collected data was analyzed using PLS-SEM to examine the factors that may lead to public acceptance of the technology in the United States. Our findings showed that social influence, performance expectancy, effort expectancy, hedonic motivation, and facilitating conditions determine conditionally automated vehicle acceptance. Additionally, certain factors were found to influence the perception of how useful the technology is, the effort required to use it, and the facilitating conditions for its use. By integrating the insights gained from this study, stakeholders can better facilitate the adoption of autonomous vehicle technology, contributing to safer, more efficient, and user-friendly transportation systems in the future that help realize the vision of the smart city.

Transportation System and the Improvement of Urban Vehicular Flow in the District of Huánuco-Perú 2022

The objective of this research is to propose a public transport reorganization system that allows the improvement of urban vehicle flow. The lack of adequate transportation infrastructure and the existing disorder in the services provided by collective car, Microbus, Rural Public Transportation Van (Combi), Coaster, and mototaxis generate congestion in public transportation, especially during peak hours, resulting in environmental and noise pollution. The research was structured into four stages: data collection on the public and private transportation network, importing and creating the transportation network in the urban area of the Huánuco district, zoning and connectivity of the study area, and finally, creating the origin/destination (O/D) matrix for public transportation, supported by digital tools (ArcGIS 10.5, AutoCAD 2018, Excel 2017). To meet the demand of 135,343 passengers from South to North and 118,958 from North to South, the proposal includes establishing one main route and seven feeder routes, requiring 422 buses and road infrastructure, as depicted in the proposal This system will have exclusive lanes to operate the Mass Transit System, allowing it to accommodate 59% of users who prefer using public transportation. This proposal aims to offer an efficient and high-quality transportation system.

Influence of Particle Sizes and Volume Fractions on Fatigue Crack Growth Rates of Aerospace Al-Alloys Composites

Recently, the need to develop fuel efficient transport systems has led to the development of a range of materials of low density, high stiffness and high strength each can be made at a reasonable cost. The aluminium based alloys are particularly important because of their improved mechanical, physical and technical properties. Fatigue failures have been recognised since the early days of the industrial revolution. Fatigue response of most of materials is related with the microstructural variations in the structure. Hence, in this study, influence of particle size and volume fractions on fatigue properties of Al-alloy composites was investigated. It was found that particle size and volume fraction of reinforcement particles play significant role on fatigue propagation rates, stress intensity threshold values, crack tip opening distance and crack tip plastic zone sizes.

Exploring the Application of Blockchain Technology in Logistics and Transportation System

This paper explores the application of blockchain technology in improving the management and efficiency of logistics and transportation systems. By leveraging blockchain technology, real-time recording, traceability, data sharing, security, and trustworthiness of logistics information can be achieved, thereby enhancing the efficiency and safety of logistics operations. The paper discusses the theoretical foundations of blockchain technology and logistics and transportation systems, highlighting key concepts such as decentralization, distributed ledger, consensus mechanisms, smart contracts, operations research, supply chain management, and transportation economics. Additionally, the literature review presents an overview of existing studies on logistics and transport systems and blockchain technology, illustrating various applications and benefits, such as sustainable transport frameworks, transportation and logistics management models, smart logistics systems, and supply chain traceability platforms. In conclusion, the exploration of blockchain technology's application in logistics and transportation systems offers comprehensive support for improving logistics efficiency, reliability, and safety, promising more efficient and reliable transportation services for the logistics industry in the future.

Functional characterization of plant specific Indeterminate Domain (IDD) transcription factors in tomato (Solanum lycopersicum L.)

Plant-specific transcription factors (TFs) are responsible for regulating the genes involved in the development of plant-specific organs and response systems for adaptation to terrestrial environments. This includes the development of efficient water transport systems, efficient reproductive organs, and the ability to withstand the effects of terrestrial factors, such as UV radiation, temperature fluctuations, and soil-related stress factors, and evolutionary advantages over land predators. In rice and Arabidopsis, INDETERMINATE DOMAIN (IDD) TFs are plant-specific TFs with crucial functions, such as development, reproduction, and stress response. However, in tomatoes, IDD TFs remain uncharacterized. Here, we examined the presence, distribution, structure, characteristics, and expression patterns of SlIDDs. Database searches, multiple alignments, and motif alignments suggested that 24 TFs were related to Arabidopsis IDDs. 18 IDDs had two characteristic C2H2 domains and two C2HC domains in their coding regions. Expression analyses suggest that some IDDs exhibit multi-stress responsive properties and can respond to specific stress conditions, while others can respond to multiple stress conditions in shoots and roots, either in a tissue-specific or universal manner. Moreover, co-expression database analyses suggested potential interaction partners within IDD family and other proteins. This study functionally characterized SlIDDs, which can be studied using molecular and bioinformatics methods for crop improvement.

Analysis of Transportation Systems for Colonies on Mars

The colonization of Mars poses unprecedented challenges in developing sustainable and efficient transportation systems to support inter-settlement connectivity and resource distribution. This study conducts a comprehensive evaluation of two proposed transportation systems for Martian colonies: a ground-based magnetically levitated (maglev) train and a low-orbital spaceplane. Through simulation models, we assess the energy consumption, operational and construction costs, and environmental impacts of each system. Monte Carlo simulations further provide insights into the cost variability and financial risk associated with each option over a decade. Our findings reveal that while the spaceplane system offers lower average costs and reduced financial risk, the maglev train boasts greater scalability and potential for integration with Martian infrastructural development. The maglev system, despite its higher initial cost, emerges as a strategic asset for long-term colony expansion and sustainability, highlighting the need for balanced investment in transportation technologies that align with the goals of Martian colonization. Further extending our exploration, this study introduces advanced analysis of alternative transportation technologies, including hyperloop systems, drones, and rovers, incorporating dynamic environmental modeling of Mars and reinforcement learning for autonomous navigation. In an effort to enhance the realism and complexity of our navigation simulation of Mars, we introduce several significant improvements. These enhancements focus on the inclusion of dynamic atmospheric conditions, the simulation of terrain-specific obstacles such as craters and rocks, and the introduction of a swarm intelligence approach for navigating multiple drones simultaneously. This analysis serves as a foundational framework for future research and strategic planning in Martian transportation infrastructure.

Development of Fuzzy Logic Controller in Automatic Vehicle Navigation using IoT

The integration of fuzzy logic controllers in automatic vehicle navigation systems represents a significant advancement in intelligent transportation systems, especially when paired with Internet of Things (IoT) functionalities and optimized through genetic algorithms. This innovative fusion harnesses the precision of fuzzy logic, the connectivity of IoT, and the optimization capabilities of genetic algorithms to transform automatic vehicle navigation. Fuzzy logic controllers excel in managing uncertainty and imprecision, providing decision-making capabilities akin to human reasoning. By simultaneously assessing multiple inputs and determining actions based on degrees of truth, fuzzy logic enables safe and efficient navigation in dynamic driving environments with fluctuating variables like obstacle proximity and traffic flow. IoT integration enhances navigation systems by enabling real-time data collection and sharing among vehicles and infrastructure, fostering adaptive route planning and improving the overall navigation experience. Genetic algorithms further optimize system performance by iteratively adjusting fuzzy logic controller parameters, ensuring efficient decision-making tailored to specific performance criteria such as travel time and fuel consumption. This collaborative integration of fuzzy logic controllers, IoT, and genetic algorithms offers a holistic solution to the challenges of automatic vehicle navigation, enhancing safety, efficiency, and adaptability in complex driving scenarios. Beyond enhancing individual vehicle performance, this approach contributes to overall transportation system efficiency and safety by mitigating traffic congestion, reducing emissions, and minimizing accidents. Consequently, these integrated systems address crucial societal challenges and pave the way for widespread adoption of autonomous vehicles in the future.

Enhancing Multimodal Transportation Through Logistics Education

This qualitative study examines the effectiveness of logistics education within Indonesian transportation institutes' multimodal transportation programmes. Fifty cadets were analysed, revealing a positive perception of logistics education's importance and its significant impact on student competency and operational efficiency. Themes emerged highlighting cadets' strong understanding and application of logistical principles, leading to tangible improvements in transportation costs, service quality, and work processes. The findings underscore the critical role of logistics education in preparing students for careers in the transportation sector and improving the efficiency and sustainability of transportation systems. Recommendations include curriculum enhancement, professional development for educators, industry engagement, and the integration of technology. By implementing these recommendations, educational institutions can better align their programmes with industry needs and ensure graduates are equipped to address the sector's challenges. This research contributes to the discourse on transportation education, emphasising the importance of logistics education in shaping the future of multimodal transportation in Indonesia and beyond.

IoT workload offloading efficient intelligent transport system in federated ACNN integrated cooperated edge-cloud networks

Intelligent transport systems (ITS) provide various cooperative edge cloud services for roadside vehicular applications. These applications offer additional diversity, including ticket validation across transport modes and vehicle and object detection to prevent road collisions. Offloading among cooperative edge and cloud networks plays a key role when these resources constrain devices (e.g., vehicles and mobile) to offload their workloads for execution. ITS used different machine learning and deep learning methods for decision automation. However, the self-autonomous decision-making processes of these techniques require significantly more time and higher accuracy for the aforementioned applications on the road-unit side. Thus, this paper presents the new offloading ITS for IoT vehicles in cooperative edge cloud networks. We present the augmented convolutional neural network (ACNN) that trains the workloads on different edge nodes. The ACNN allows users and machine learning methods to work together, making decisions for offloading and scheduling workload execution. This paper presents an augmented federated learning scheduling scheme (AFLSS). An algorithmic method called AFLSS comprises different sub-schemes that work together in the ITS paradigm for IoT applications in transportation. These sub-schemes include ACNN, offloading, scheduling, and security. Simulation results demonstrate that, in terms of accuracy and total time for the considered problem, the AFLSS outperforms all existing methods.

The impact of glucose conjugation on the activity and uptake of zinc phthalocyanine in HaCaT keratinocytes stimulated with proinflammatory cytokines

SignificanceGlucose metabolism has been proven as an essential process for proliferating keratinocytes, which highlights the importance of glucose transporter-1 (GLUT1) not only in the onset of psoriasis but also in the progression and severity of this inflammation-driven disease. Recent evidence showed that overexpressed GLUT1 transporter may be a potential target for the treatment of psoriasis and other hyperproliferative skin diseases. ApproachFor this reason, we attempted to synthesize and evaluate the photocytotoxic activity and intracellular uptake of glucose-conjugated zinc phthalocyanine (Glu-ZnPc), in order to prove its preferential accumulation in highly proliferative psoriatic keratinocytes. HaCaT cells stimulated with exogenous cytokines (TNF-α, IL-6, IL-17, IL-23, IL-36) were used as in vitro model. ResultsWe managed to synthesize Glu-ZnPc that is stable, water soluble, cytotoxic, and has a more efficient transport system than unconjugated ZnPc. ConclusionsWe believe that GLUT1 overexpression could potentially be utilized by using selective glucose- conjugated photosensitizers against psoriasis.

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.

Transportation Problems: Analysis and Solutions

Abstract: Transportation systems are fundamental to modern society, facilitating the movement of people and goods essential for economic development and social well-being. However, the efficiency and sustainability of these systems are often challenged by various factors, leading to transportation problems such as traffic congestion, inefficient route planning, and environmental degradation. This review paper comprehensively examines the analysis and solutions to transportation problems, encompassing a range of analytical techniques and innovative approaches. Beginning with an exploration of different types of transportation problems, including route optimization and capacity planning issues, the paper delves into the analytical techniques employed, such as linear programming and heuristic algorithms. Through case studies focusing on urban transportation networks and freight systems, key challenges such as data quality and technological constraints are identified. The paper then explores solutions, including the role of intelligent transportation systems, green initiatives, and emerging technologies like artificial intelligence and blockchain. Finally, it outlines future directions, including the potential of autonomous vehicles and the importance of sustainable urban mobility planning. This review provides valuable insights for policymakers, practitioners, and researchers seeking to address transportation challenges and build resilient and efficient transportation systems for the future.

INTEGRATION OF AN URBAN TRANSPORTATION SYSTEM AS AN ELEMENT OF MOBILITY PLANS

In today’s urban mobility scene, the increasing number of private cars trying to travel through the city centers is one of the most onerous problems facing urban transport. Knowing about the impact that different transport demand management models can have on the efficiency of the urban transport system may be of key importance for developing solutions in this area. This knowledge is the main goal of the considerations undertaken. The observations presented in this article took place during the formation of a Sustainable Urban Mobility Plan (SUMP) for the Bielsko-Biała agglomeration. The author of the article is a member of the working group that prepared the SUMP project for the city. The study examined the effectiveness of mobility planning activities. The scope of these activities included collecting the data necessary to create a mobility plan, analyzing this data, building scenarios in the area of transport demand management, and designing the assessment of the adopted solutions. The article shows the procedure for creating a model of a SUMP for urban areas, which may be a prerequisite for municipal governments for effective applications for funding projects related to transport. The most important part of the analysis is the evaluation using integration indicators of selected key elements of the urban transport system of the Bielsko-Biała agglomeration in the area of transfer accessibility. Such research allows us to answer the question of whether the locations of new railway stops proposed under the agglomeration SUMP contribute to increasing the integration of the urban transport system. The current research implements one of the dimensions of mobility management projects. Related goals are, among other things, to achieve more effective integration of the urban transport system. The analyses were carried out by measuring selected properties of the transport network and a survey questionnaire.

Strategi Kebijakan Pemerintah Provinsi DKI Jakarta dalam Menanggulangi Tantangan Transportasi Perkotaan

The Jakarta Provincial Government faces significant challenges in managing urban transportation amidst the rapid growth of the city. Traffic congestion, air pollution, and the increasing volume of vehicles pose major issues. In addressing these challenges, the Jakarta Provincial Government has implemented various strategic policies. The development of public transportation, restrictions on private vehicles, and the empowerment of buffer zones are the primary focuses. This research aims to determine the Policy Strategies of the Jakarta Provincial Government in addressing urban transportation challenges. The study employs a qualitative approach with a literature review as its data collection technique. The results indicate that the Jakarta Provincial Government has implemented a comprehensive policy strategy to tackle urban transportation challenges. This includes the development of public transportation, restrictions on private vehicles, infrastructure investment, technology utilization, and the empowerment of buffer zones. The government has also demonstrated a commitment to creating an efficient, sustainable, and responsive transportation system. Keywords: Government Policy Strategy, Urban Transportation,Government Policy Strategies, Urban Transportation, Transportation Challenges, Public Policy

Integration of UAVs with public transit for delivery: Quantifying system benefits and policy implications

The maturation and scalability of unmanned aerial vehicle (UAV) technology offer transformative opportunities to revolutionize prompt delivery. This study explores integrating UAVs with public transportation vehicles (PTVs) to establish a novel delivery paradigm that enhances revenue for public transit operators and improves transport system efficiency without compromising passenger convenience or operational efficiency. Employing hexagonal planning technology, this study identifies and quantifies the available spatio-temporal resources of PTVs for UAV integration. This involves aligning the spatio-temporal dynamics of prompt delivery orders with PTV ridership, based on field data from Beijing’s Haidian District. Utilizing these outputs, we quantitatively analyze the benefits of integrating UAVs with PTVs on increasing public transit revenue, and potentials of reducing carbon emissions and mitigating congestion. Furthermore, we quantify the long-term benefits of UAV-PTV integration by predicting future increases in delivery demand. Based on obtained quantitative results, this study discusses practical and policy implications to support the sustainable integration of UAVs with PTVs.

Post-pandemic transit commute: Lessons from focus group discussions on the experience of essential workers during COVID-19

Public transit services, which provide a critical lifeline for many essential workers, were severely interrupted during the COVID-19 pandemic. As institutions gradually return to normal in-person operations, it is critical to understand how the pandemic affected essential workers' commute and what it will take to ensure the effective recovery of transit ridership and enhance the long-term resiliency and equity of public transportation systems for those who need it the most. This study used focus group discussions with essential workers who were pre-pandemic transit riders to understand how the COVID-19 pandemic has impacted their commute perceptions, experiences, motives, and challenges and explore the potential changes in their travel behavior post-pandemic. We used NVivo 12 Pro to conduct a thematic analysis of the transcripted discussion data and examined patterns of commute mode change with respect to participants' attributes, including job type, home location, and gender. The results show that public transit had multiple reliability and frequency challenges during the pandemic, which resulted in most participants switching away from public transportation. With the increased availability of hybrid remote work and pandemic-related parking policies, driving emerged as a safer and more affordable commute mode for many pre-pandemic transit riders, rendering transit services less efficient for those who continued to rely on it. Planning for post-COVID resilient and reliable mobility requires a major rethinking of providing an efficient and effective transport system and a more fundamental approach to long-term public transport policy. To recover transit ridership, transit agencies need to ensure transit service availability and provide reliable transit information through smartphone apps. Similarly, transit agencies need to coordinate with other employers to provide free or heavily subsidized transit passes, to facilitate the recovery of transit demand effectively.

Exploring the changes in travel behavior between the first and second waves of the COVID-19 pandemic in Dhaka

The COVID-19 outbreak created immense disruptions in our daily lives. Travel is one of the major areas severely impacted by the pandemic. Bangladesh experienced two waves of COVID-19 between March 2020 and July 2021. Although several studies focused on significant changes in the travel behavior of urban road users before and during the pandemic, hardly any research examined the differences between the two waves. Hence, it was important to study the differences in travel behavior between the two waves of the COVID-19 pandemic to understand how the effects of travel restrictions, health concerns, and the severity of the pandemic on people's travel decisions change over time. This study intends to investigate the changes in travel behavior between the first and second waves of the COVID-19 pandemic in Dhaka and the factors that influence these changes. Data were collected through online questionnaire surveys using the Google form. Voluntary response and convenience sampling techniques were used to collect responses from 447 people in Dhaka. Descriptive statistics and non-parametric tests were conducted to analyze the data. In addition, two multinomial logistic models were developed to identify the factors behind the changes in travel behavior. This study reveals that work and shopping trips increased, whereas work-from-home and online shopping somewhat decreased during the second pandemic wave compared to the first pandemic wave. Most non-car owners who reduced their use of public transportation during the first pandemic increased their use during the second wave. There was no significant increase in the use of active transport modes for work and shopping trips in Dhaka. The perceived risk of COVID-19 infection was relatively lower during the second wave, influencing the respondents to travel more frequently for work and shopping purposes. Ensuring more adaptive public transportation, flexibility to work from home, and creating supportive infrastructures for active transport modes might help to provide a safe, affordable, and efficient transportation system for all during the future waves of the pandemic and other unprecedented events.

Navigating the Future of Secure and Efficient Intelligent Transportation Systems using AI and Blockchain

Introduction/Background This study explores the limitations of conventional encryption in real-world communications due to resource constraints. Additionally, it delves into the integration of Deep Reinforcement Learning (DRL) in autonomous cars for trajectory management within Connected And Autonomous Vehicles (CAVs). This study unveils the resource-constrained real-world communications, conventional encryption faces challenges that hinder its feasibility. This introduction sets the stage for exploring the integration of DRL in autonomous cars and the transformative potential of Blockchain technology in ensuring secure data transfer, especially within the dynamic landscape of the transportation industry. Materials and Methods The research methodology involves implementing DRL techniques for autonomous car trajectory management within the context of connected and autonomous CAVs. Additionally, a detailed exploration of Blockchain technology deployment, consensus procedures, and decentralized data storage mechanisms. Results Results showcase the impracticality of conventional encryption in resource-constrained real-world communications. Moreover, the implementation of DRL and Blockchain technology proves effective in optimizing autonomous car subsystems, reducing training costs, and establishing secure, globally accessible government-managed transportation for enhanced data integrity and accessibility. Discussion The discussion delves into the implications of the study's findings, emphasizing the transformative potential of DRL in optimizing autonomous car subsystems. Furthermore, it explores the broader implications of Blockchain technology in revolutionizing secure, decentralized data transfer within the transportation industry. Conclusion In conclusion, the study highlights the impracticality of conventional encryption in real-world communications and underscores the significant advancements facilitated by DRL in autonomous vehicle trajectory management. The integration of Blockchain technology not only ensures secure data transfer but also paves the way for a globally accessible transportation blockchain, reshaping the future landscape of the industry.

Sustainable goods transport – inland navigation

Inland navigation plays a crucial role in goods transport, facilitating the movement of goods via rivers and canals. Sustainability in Goods transport involves reducing environmental impact, promoting social responsibility, and enhancing efficiency. Sustainability in goods transport includes optimising transportation routes, investing in fuel-efficient vehicles, and promoting alternative fuels. Green technologies such as GPS tracking and route optimisation software can improve efficiency. Encouraging sustainable packaging materials and carbon offsetting can further reduce environmental impact. Inland navigation can be greener by adopting alternative fuels, improving vessel efficiency, and optimising routes. Maintenance and modernisation of infrastructure, environmental protection measures, and modal shifts from road transport also contribute to sustainability. Various factors influence the performance of inland navigation, including infrastructure, water levels, vessel characteristics, regulations, economic conditions, technological advancements, environmental factors, and social considerations. Integrating sustainable practices and addressing these factors can lead to a more environmentally friendly and efficient goods water transport system. This paper investigates the connections between inland goods transport and sustainability, identifying the main factors. Some basic statistics were collected and analysed to show the performance of inland navigation. The analysis showed that inland navigation is very diverse in Europe, Western Europe is more developed than Eastern European countries. In conclusion, it can be stated that many factors influence the development of Eastern European inland navigation.