Development Of Transport System Research Articles

Razvitie sistemy gorodskogo passazhirskogo transporta i perekhod na brutto-kontrakty (chast' 1)

Urban passenger transport is one of the socially significant branches of the urban economy of any modern municipality, since it plays an important role in ensuring the quality of life of the urban population. The article discusses one of the current problems in the field of municipal economy the development of the urban passenger transport system.

Path Difference Optimization of 5G Millimeter Wave Communication Networks in Malaysia

The development of intelligent transport systems, mobile cellular networks, microwave links, and vehicle communications has accelerated with the use of wireless connections as a communication channel in 5G wireless technology. Weather, including rain, fog, snow, sand, and dust, impacts wireless communication channels in various ways. These effects are more pronounced at the high frequencies of millimeter-wave bands. Recently, the 5G network has made it possible to support a variety of applications with fast speeds and high-quality content. To facilitate the use of high-millimeter-wave frequencies, a recent study investigated how sand and dust affect the 5G communication channel. In this paper, we consider the impact of frequent and heavy rainfall on millimeter-wave propagation and cross-polarization of the wave at various points along the propagation path caused by rainfall in urban and highway scenarios in Malaysia. We estimate rainfall attenuation, path loss, and link margin at various millimeter-wave frequencies. From our simulation results, it is evident that rainfall attenuation, path loss, and link margin depend on the operating frequency, path difference, and rainfall rate. In this paper, we estimate and compare the optimal path difference values under urban and highway scenarios both with and without rainfall attenuation.

TCaS‐VN: A novel V2V communication scheme to improve traffic control and safety in vehicular cloud networks

SummaryRoad accidents and traffic congestion are the unavoidable consequences of the growing number of vehicles on the road. Conventional management systems are not effective in mitigating these issues. However, a range of technologies, including sensors, processors, vehicle‐based resources, and mobile cloud computing, can help solve traffic and safety problems. Vehicular cloud networks, a cutting‐edge technology for the development of intelligent transportation systems, face significant challenges, such as ensuring reliable service providers that maintain stable connections between vehicles. Roadside units (RSUs) play a crucial role in these networks, but they come with several drawbacks, including high costs, imbalanced load distribution, and unreliable connections. To address these issues, we propose the Traffic Control and Safety in Vehicular Networks (TCaS‐VN) algorithm, a cloud‐based sharing services framework for vehicles that eliminates the need for RSUs. This approach offers several advantages, including a twofold improvement in the lifetime of clusters, the elimination of noncluster head nodes, a 54% reduction in overhead, a 58% reduction in service search time, and a success rate of over 95% in service provisioning.

Investigating of transportation systems development for urban districts, costs and social equity: a case of Sanandaj, Kurdistan

Investigating of transportation systems development for urban districts, costs and social equity: a case of Sanandaj, Kurdistan

Potential field-based modeling and stability analysis of heterogeneous traffic flow

Car-following behavior is influenced by multiple factors. Utilizing the potential field theory, previously determined as a comprehensive representation of these influencing factors, this study introduces a unified potential field-based car-following model tailored for mixed traffic flow. Considering the diverse information receptivity of various vehicle types, the model is shaped to encapsulate both Human Driven Vehicles and Connected and Automated Vehicles. Using the characteristic equation-based method, the stability conditions of the model are delineated. Subsequently, a detailed examination of mixed traffic flow stability was conducted across varying Connected and Automated Vehicles penetration rates. Leveraging a dataset of 300 real-world trajectories, the results were benchmarked against classical models such as the Intelligent Driver Model. Notably, our proposed model exhibited an 18.67 % improvement in safety metrics and a 1.36 % improvement in fuel economy over the Intelligent Driver Model when representing Connected and Automated Vehicles. The stability of the mixed traffic flow was found to correlate strongly with vehicular time delay and acceleration parameter weights. Furthermore, augmenting the Connected and Automated Vehicles penetration rate surfaced as a promising strategy to bolster traffic flow stability. Collectively, these findings furnish substantial enhancements to the existing body of knowledge on mixed traffic flow stability, offering pragmatic insights for future research and real-world applications, including traffic management, urban planning, and the development of Intelligent Transport Systems.

До питання розвитку транспортних перевезень у регіональному аспекті

Today, globalization trends are becoming evident both in Ukraine and in the world. This approach is aimed at increasing urbanization and the development of agglomerations. Therefore, the issues of development of the transport industry of Ukraine in the regional aspect remain relevant. At the same time, it should be noted that modern multimodal transport networks have their own specific structure. A typical multimodal transport network includes modern components of such elements of the network of such types of transport as road, rail, air, river, sea. The authors identified multimodal transport hubs in the structure of the transport system of Ukraine, analyzed their efficiency in terms of freight and passenger transport. It has been established that the existing distribution of cargo and passenger transportation by modes of transport indicates a certain priority of the use of road transport for all types of communication. At the same time, it should be noted that the automobile mode of transport today in Ukraine and the world is not an example of efficient and economical use of energy resources, has a significant share of the environmental load among other modes of transport, and is not always the objectively optimal option for transporting goods and passengers according to the criteria of reliability, economy and delivery terms. Thus, given that the transport industry of any region, country and at the global level is an important component of the economy, attracts a large number of specialists and affects the pace of development of other sectors of the economy, it is necessary to carefully study the development of this industry at all levels of operation and constantly to direct its development along vectors that constantly change over time. The transport industry at the regional level should also be noted as the basis for building a developed and efficient transport industry in the country. The modern development of the international transport system shows that the gateway or hub can perform the functions of modal and intermodal convergence. Especially if it is a combination of two or more operating modes of different systems. At the same time, the currently operating structures of regional multimodal networks unite the main gateways (hubs), which ensures connection to the international transport system through the sea-land-air interface.

Emerging trends for urban freight transport-The potential for sustainable micromobility.

Active transportation referring to non-motorized modes of transport is promoted and popularized both in practice and in the scientific literature, while their use for urban freight transport has been largely neglected. Thus the main scope of the paper is to indicate the development potential of micromobility use in urban freight transport and to check its influence on urban sustainability. The authors have hypothesized that active means of transport, with a focus on micromobility, have great development potential in freight transportation in cities. The implemented methods for analyzing the relationship between users' characteristics, micromobility, and its impact on urban sustainable development, were logit and probit modelling. The authors' system includes an analysis of factors connected with the topics of sustainability and micromobilty, that have met an essential scientific gap that this paper addresses. Logistic (logit) regression is used mainly for binary, ordinal, and multi-level outcomes to find the probability of success (i.e. occurrence of some event). Probit regression, however, is primarily used in binary response models and assumes the normal distribution of data. The main finding of the article has led the authors to the statement that active means of transport, including micromobility have great development potential in freight transportation in cities. Knowledge of the acceptance of micromobility solutions is essential for municipal authorities in shaping the development of urban transport systems. Thus proper strategies and actions need to be prioritized to leverage the sustainability-related co-benefits of active transport.

REGULATORY AND LEGAL ENSURING THE SUSTAINABLE DEVELOPMENT OF TRANSPORT AND LOGISTICS SYSTEMS

Topicality. The scientific article is devoted to the research analysis of the peculiarities of legal regulation and the construction of the architecture of the intellectualization of transport systems. Transport communications unite companies around the world and are a necessary prerequisite for the integrity of the country and its integration into the world economy through foreign economic activity. In this regard, the transport system is directly related to the economy and the social sphere. The transport policy of any state is characterized by national priorities at the micro and macro levels. A competitive transport system must have the following characteristics: be economically, ecologically and socially sustainable; to be able to recover effectively after natural disasters; have a developed and reliable IT infrastructure.The use of information systems in traffic management is associated with the rapid development of modern cities, an increase in the number of rolling stocks on the streets, new routes and other features. As a result, the amount of information needed for analysis and making operational decisions has increased significantly. The information component will become an integral part of city life. And for more effective use of all the possibilities of intelligent transport systems (ITS), legal regulation is necessary, which includes data protection, standardization, rules for the application of ITS achievements and their further development.Aim and tasks. The purpose of the research is to study the legal foundations of the concept of transport and logistics systems sustainable development.Materials and Methods. The research methodology is based on the analysis of legislative and legal methods of ensuring transport and logistics flows, economic and legal analysis using the general legal framework and special legal acts that regulate the activity of transport and logistics systems. The problem of intellectualization of environmental protection activities is considered both through structural changes in the international economy, through modern processes of development of national transport and logistics systems, as well as through the study of the complex network structure of global logistics chains, where both approaches complement each other.Research results. The rapid development of modern cities, the increase of vehicles on the streets, the development of new routes increases the amount of information necessary for analysis and making operational decisions. This, in turn, stimulates the active use of intelligent information technologies. And the creation of legal incentives for the development of the intellectualization of the transport and logistics system is one of the factors of strengthening the economy and improving the welfare of the population.Conclusion. The conducted research made it possible to conduct an analysis of legal regulation and stimulation of the development of intelligent transport systems (ITS). In particular, at the international and regional levels. The main necessary elements of the comprehensive application of intelligent transport systems are standardization at the international level, in order to be able to create a global system for more efficient use of transport and achieving global goals in the safety of movement and reducing the impact of transport on the environment.

How Generative Adversarial Networks Promote the Development of Intelligent Transportation Systems: A Survey

How Generative Adversarial Networks Promote the Development of Intelligent Transportation Systems: A Survey

Performance evaluation of the cloud computing application for IoT-based public transport systems

The object of research is cloud computing as an element of the server infrastructure for intelligent public transport systems. Given the increasing complexity and requirements for modern transportation, the application of the Internet of Things concept has a high potential to improve efficiency and passenger comfort. Since the load generated in IoT systems is dynamic and difficult to predict, the use of traditional infrastructure with dedicated servers is suboptimal. This study considers the use of cloud computing as the main server infrastructure for the above systems. The paper investigates the main cloud platforms that can be used to develop such systems and evaluates their advantages and disadvantages. The authors developed the overall architecture of the system and evaluated the performance and scalability of individual components of the server infrastructure. To test the system, a software emulator was developed that simulates the controller module installed in vehicles. Using the developed emulator, stress tests were conducted to analyze and confirm the ability to scale and process input data by the proposed architecture. The test scenarios were developed and conducted on the basis of the existing public transportation system in Kyiv, Ukraine. The experimental results showed that the proposed IoT architecture is able to scale efficiently according to the load generated by the connected devices. It has been found that when the number of incoming messages increases from 40 to 6000, the average message processing time remains unchanged, and the error rate does not increase, which is an indicator of stable system operation. The obtained results can be used in the development of modern public transport systems, as well as for the modernization of existing ones

A comprehensive analysis of the electronic fare collection systems effectiveness implementation on public transit and prospective directions of its application in Ukraine

The object of research is the effectiveness of the implementation of electronic fare collection systems on public transit. Applying the electronic fare collection systems is a general trend in improving public transport services for users. In the pre-war period, the systems began to be implemented in many cities of Ukraine. At the same time, this activity was not of a systemic nature and at the current stage it is mainly considered as a means of ensuring more convenient conditions for the use of scheduled passenger transport services for passengers. The article focuses on a broader understanding of the effectiveness of the fare collection systems implementation, their role in ensuring the internal integration of the multi-modal public transport system, increasing the operational efficiency, providing the safety of transportation and increasing the attractiveness of public transit services for the population as a real alternative for the private cars to use. The implementation efficiency of the electronic fare collection systems in public transit should be expressed through various aspects. There are 9 aspects to be considered: system integration; comfort ensuring; transportation safety assistance; operational efficiency and passengers' travel time reduction; integration into the management and planning systems; implementation of the flexible fare system; conduction of the flexible fare policy; development of reasonable income distribution system; increase of the scheduled passenger transport services attractiveness. It is suggested to use systemic approach for integrated multimodal public transit system creation. It requires the development of an intelligent transport system that would integrate separate functions of the electronic fare collection system into controlling, managing and planning subsystems. The practical introduction of the solutions proposed regarding the prospects to develop electronic fare collection systems in the cities of Ukraine will make it possible to increase the efficiency of their use and contribute to the improvement of the quality of transport services for passengers.

Life cycle assessment of the solid oxide fuel cell vehicles using ammonia fuel

New energy vehicles are developing rapidly to address energy shortages and global warming. However, the on-board hydrogen storage poses certain limitations. To solve the limitations, this paper introduced a novel vehicle powered by the ammonia-fueled solid oxide fuel cell (NH3-SOFC), which had the advantages of directly generating electricity and minimizing energy loss. The environmental impacts and energy consumption of the NH3-SOFC vehicle (NH3-SOFCV) were evaluated using the life cycle assessment (LCA) method, and the prospects of NH3-SOFCV with the development of sustainable transportation systems were examined. The results indicated that the largest impact during the entire life cycle was attributed to the creation of photochemical ozone potential (POCP), which accounted for 62.65% of all calculated impacts. The manufacturing phase of NH3-SOFCV utilized the most energy and emitted the most CO2 emissions, followed by the use phase. The global warming potential (GWP) of NH3-SOFCV was 0.124 kg CO2-eq/km, while the ammonia fuel utilization in the use phase had an impact on the acidification potential (AP) and eutrophication potential (EP). The sensitivity analysis showed that the total environmental impacts of NH3-SOFCV could be reduced by 29% with the life cycle mileage of 200,000 km, 4 tire changes and 2 battery changes. When ammonia fuel was produced from renewable energy sources, the GWP of NH3-SOFCV was less than that of other types of vehicles. Overall, NH3-SOFCV had tremendous development potential with the increase of new energy vehicles in the future.

Integrated Urban Transportation Policy: A Study of Kochi Metro and Vyttila Mobility Hub in Kerala, India

Integrated infrastructure has been considered the key to the development of the urban transport system. Studies have rarely focused on the benefits and issues related to the urban integrated transport system, including mobility hubs. The present study attempts to analyze different aspects concerning the urban integrated infrastructure with a special focus on the Kochi Rail Metro and the Vyttila Mobility Hub in Kerala. The study reveals that 41 percent of commuters rely on Hub and Metro for occupation-induced traveling, followed by education (28 percent) and health (20 percent). It is revealed that the existing fare of Kochi Metro does not appear to be attractive to passengers. Almost 90 percent of passengers consider fares as not reasonable. Most of the commuters of Metro Train services and Mobility Hub share the view that these are ‘inclusive.’ The most noteworthy aspect of Metro and Hub is that they are eco-friendlier and sustainable. However, as an integrated infrastructure mode, it has to travel a long way yet. The study shows that the metro rail services must be accessible and affordable to the people. The study suggests that metro authorities need to make policy decisions to rationalize the fare rates in accordance with different parameters to attract more passengers to the Metro services. As the commuters from the city area are averse to using the metro services, steps need to be taken to encourage the urban passengers to use the metro services.

Hierarchical Model-Predictive-Control-Based Energy Management Strategy for Fuel Cell Hybrid Commercial Vehicles Incorporating Traffic Information

With the development of intelligent transportation systems, access to diverse transportation information has become possible. Integrating this information into an energy management strategy will make the energy allocation prospective and thus improve the overall performance of the energy management program. For this reason, this paper proposes a hierarchical model predictive control (MPC) energy management strategy that incorporates traffic information, where the upper layer plans the vehicle’s velocity based on the traffic information and the lower layer optimizes the energy distribution of the vehicle based on the planned velocity. In order to improve the accuracy of the planning speed of the upper strategy, a dung beetle optimization-radial basis function (DBO-RBF) prediction model is constructed, artfully optimizing the RBF neural network using the dung beetle optimization algorithm. The results show that the prediction accuracy is improved by 13.96% at a prediction length of 5 s. Further, when the vehicle passes through a traffic light intersection, the traffic light information is also considered in the upper strategy to plan a more economical speed and improve the traffic efficiency of the vehicle and traffic utilization. Finally, a dynamic programming (DP)-based solver is designed in the lower layer of the strategy, which optimizes the energy distribution of the vehicle according to the velocity planned by the upper layer to improve the economy of the vehicle. The results demonstrate achieving a noteworthy 3.97% improvement in fuel economy compared to the conventional rule-based energy management strategy and allowing drivers to proceed through red light intersections without stopping. This proves a substantial performance enhancement in energy management strategies resulting from the integration of transportation information.

Identifying critical urban intersections from a fine-grained spatio-temporal perspective

With the rising travel demand of crowds, urban road networks are under increasing pressure, which makes them progressively more fragile. Accurate identification of critical intersections is essential for urban management, resource allocation and traffic supervision. However, existing intersection evaluation methods fail to fully exploit transportation infrastructure, travel behavior and their synergistic relationships, resulting in coarse-grained and low-dynamic evaluation results. In this study, we develop an urban Intersection Evaluation framework from a Fine-grained Spatio-Temporal perspective (IE-FST). The framework is divided into three parts: fine-grained extraction of intersection information, multi-perspective assessment of node importance, and dynamic perception of spatio-temporal patterns in importance ranking. Specifically, IE-FST upgrades the intersection evaluation scale to turn-level, proposes the concept of Turning Sub-Node (TSN) and constructs a refined TSN topology network. Then, by finely extracting the network properties and mobility patterns of TSNs and mining the association patterns among TSNs, IE-FST proposes a Critical Fine-grained node Identification (CFI) algorithm to achieve a refined and dynamic evaluation of urban intersections. Finally, IE-FST analyzes the temporal characteristics and spatial distribution of TSN importance rankings, and provides traffic management measures for different types of TSNs to improve urban transport efficiency. Experiments are performed in Wuhan, China, and the results indicate that the proposed IE-FST framework achieves an accurate perception of urban intersections with high spatial and temporal resolution. This study promotes a better understanding of the synergistic relationships between crowd travel movements and urban transportation infrastructure, and provides theoretical support for infrastructure siting, refined traffic supervision, and rational resource allocation, thus contributing to the resilient and sustainable development of transportation systems and society.

Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

As the main link of ground engineering, crude oil gathering and transportation systems require huge energy consumption and complex structures. It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps. In this paper, the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established. Combined with the big data analysis method, the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems. The comprehensive energy consumption, gas consumption, power consumption, energy utilization rate, heat utilization rate, and power utilization rate of crude oil gathering and transportation systems are predicted. Considering the efficiency and unit consumption index of the crude oil gathering and transportation system, the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method, and the subjective weight is determined by the triangular fuzzy analytic hierarchy process. The entropy weight method determines the objective weight, and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems. Finally, the weak links in energy utilization are identified, and energy conservation and consumption reduction are improved. The above research provides technical support for the green, efficient and intelligent development of crude oil gathering and transportation systems.

Trend analysis of traffic management based on literature data mining and graph analysis tools

AbstractStudites on traffic management is crucial for the development of intelligent transportation systems and smart cities. However, identifying the development stages of traffic management field based on bibliometric analysis is still lacking. In this study, CiteSpace and VOSviewer software are used to explore “traffic management” field by summarizing development process and predicting future research trend. A total of 3,028 relevant documents over the past 40 years were collected from Web of Science. Results show that (1) studies on traffic management were mainly published by researchers from USA (30.55%), China (20.90%), and some European countries; (2) the key traffic management research contents can be classified into four categories, that is, background requirements, traffic problems, method models, and control strategies; (3) the evolution process can be divided into four stages, that is, budding stage (1990–1994), development stage (1995–2003), calm stage (2004–2010), and maturation stage (2011–); (4) machine learning, deep learning and other intelligent algorithms have played more important roles in recent years, and connected vehicle management is also a potential development trend. Results suggest that cooperative vehicle‐infrastructure systems or machine learning‐based studies might be the hotspots on traffic management studies.

A Deep Reinforcement Learning Scheme for Spectrum Sensing and Resource Allocation in ITS

In recent years, the Internet of Vehicles (IoV) has been found to be of huge potential value in the promotion of the development of intelligent transportation systems (ITSs) and smart cities. However, the traditional scheme in IoV has difficulty in dealing with an uncertain environment, while reinforcement learning has the advantage of being able to deal with an uncertain environment. Spectrum resource allocation in IoV faces the uncertain environment in most cases. Therefore, this paper investigates the spectrum resource allocation problem by deep reinforcement learning after using spectrum sensing technology in the ITS, including the vehicle-to-infrastructure (V2I) link and the vehicle-to-vehicle (V2V) link. The spectrum resource allocation is modeled as a reinforcement learning-based multi-agent problem which is solved by using the soft actor critic (SAC) algorithm. Considered an agent, each V2V link interacts with the vehicle environment and makes a joint action. After that, each agent receives different observations as well as the same reward, and updates networks through the experiences from the memory. Therefore, during a certain time, each V2V link can optimize its spectrum allocation scheme to maximize the V2I capacity as well as increase the V2V payload delivery transmission rate. However, the number of SAC networks increases linearly as the number of V2V links increases, which means that the networks may have a problem in terms of convergence when there are an excessive number of V2V links. Consequently, a new algorithm, namely parameter sharing soft actor critic (PSSAC), is proposed to reduce the complexity for which the model is easier to converge. The simulation results show that both SAC and PSSAC can improve the V2I capacity and increase the V2V payload transmission success probability within a certain time. Specifically, these novel schemes have a 10 percent performance improvement compared with the existing scheme in the vehicular environment. Additionally, PSSAC has a lower complexity.

Energy consumption structure model considering urban green and low-carbon transportation

<p class="Abstract">The importance of energy conservation and emission reduction has become the consensus of the international community, and Iraq is also actively improving the urban public transportation system to control carbon emissions. This paper collects panel data of Tikrit city in Iraq in the past 3 years, constructs a random effect variable coefficient model, and studies the impact of the development of urban low-carbon transportation system on the energy consumption structure. The study finds that the government can use public transportation pricing strategies to influence consumers. In order to realize the optimization of energy consumption structure, the impact of electric vehicles on energy consumption structure will decrease with the increase of urban development. The transportation sector can increase the purchase and travel costs of traditional cars by restricting travel, purchases, and charging parking fees, which affects the number of private cars and reduces the obstacles to optimizing the energy consumption structure. The government should increase financial subsidies, improve rail transit and reasonable bus (electric) vehicle operation systems, increase investment in new energy vehicle research and development, and encourage high energy density and low power consumption technologies. development, increase residents’ demand for new energy passenger vehicles, and optimize the energy consumption structure.</p>

Modeling freight truck-related traffic crash hazards with uncertainties: A framework of interpretable Bayesian neural network with stochastic variational inference

Due to the increasing demand for goods movement, externalities from freight mobility have attracted much concern among local citizens and policymakers. Freight truck-related crash is one of these externalities and impacts urban freight transportation most drastically. Previous studies have mainly focused on correlation analyses of influencing factors based on crash density/count data, but have paid little attention to the inherent uncertainties of freight truck-related crashes (FTCs) from a spatial perspective. While establishing an interpretable analysis model for freight truck-related accidents that considers uncertainties is of great significance for promoting the robust development of urban freight transportation systems. Hence, this study proposes the concept of FTC hazard (FTCH), and employs the Bayesian neural network (BNN) model based on stochastic variational inference to model uncertainty. Considering the difficulty in interpreting deep learning-based models, this study introduces the local interpretable modelagnostic explanation (LIME) model into the analysis framework to explain the results of the neural network model. This study then verifies the feasibility of the proposed analysis framework using data from California from 2011 to 2020. Results show that FTCHs can be effectively modeled by predicting confidence intervals for effects of built environment factors, in particular demographics, land use, and road network structure. Results based on LIME values indicate the spatial heterogeneity in influence mechanisms on FTCHs between areas within the metropolitan regions and alongside the freeways. These findings may help transport planners and logistic managers develop more effective measures to avoid potential negative effects brought by FTCHs in local communities.