Development Of Transport System Research Articles

Towards Next Generation of Pedestrian and Connected Vehicle In-the-Loop Research: A Digital Twin Co-Simulation Framework

Digital Twin is an emerging technology that replicates real-world entities into a digital space. It has attracted increasing attention in the transportation field and many researchers are exploring its future applications in the development of Intelligent Transportation System (ITS) technologies. Connected vehicles (CVs) and pedestrians are among the major traffic participants in ITS. However, the usage of Digital Twin in research involving both CV and pedestrian remains largely unexplored. In this study, a Digital Twin framework for CV and pedestrian in-the-loop simulation is proposed. The proposed framework consists of the physical world, the digital world, and data transmission in between. The features for the entities (CV and pedestrian) that need digital twining are divided into external state and internal state, and the attributes in each state are described. We also demonstrate a sample architecture under the proposed Digital Twin framework, which is based on Carla-Sumo Co-simulation and Cave automatic virtual environment (CAVE). A case study that investigates Vehicle-Pedestrian (V2P) warning system is conducted to validate the effectiveness of the presented architecture. The proposed framework is expected to provide guidance to the future Digital Twin research, and the architecture we build can serve as the testbed for further research and development of ITS applications on CV and pedestrians.

Artificial Intelligence-Empowered Logistic Traffic Management System Using Empirical Intelligent XGBoost Technique in Vehicular Edge Networks

Recent advancements in computation and communication technologies and the increasing adoption of the Internet of Things (IoT) and Artificial Intelligence (AI) technologies have paved the way to tremendous developments in modern transportation systems. Driven by the massive number of connected vehicles and the stringent requirements of the public traffic management system, the transportation of data to and from the centralized cloud servers poses a great challenge. As a result, to meet the computational requirements and handle the massive amount of sensory data efficiently, the potential solution is to process/analyze the data at the edge of the network. Motivated by the challenges mentioned above, in this paper, we design a new empirically intelligent XGboost (EIXGB)-enabled logistic transportation system at the edge network for analyzing the data efficiently. Besides that, the proposed EIXGB technique intends to obtain real-time results based on the monitoring parameters of the public traffic management system with higher accuracy and minimum error. Extensive simulation results demonstrate the efficiency of the proposed EIXGB technique over the standard machine learning techniques using a set of parameters. The proposed technique achieves 87-97% accuracy over the different sets of features of a real-time dataset as per the simulation results.

Impact of Climate Disasters on Railway Infrastructure: Case Study of Northeast India

Climate disasters have become increasingly frequent in India, severely affecting the railway infrastructure every year. Physical damages to railway tracks, bridges, and signaling systems, caused by floods, cyclones, and landslides, are well documented. However, the impact of these disasters on the railway infrastructure was beyond direct physical damages. This paper aimed to explore the impact of climate disasters on railway infrastructure in Northeast India using case study approach. Three cases were studied to analyse the impact of climate disasters on railway infrastructure, including geological disasters and extreme weather. Infrastructure development and operation of railway transport system in Assam, Mizoram, and Manipur proved to be challenging, especially when coping with natural disasters, such as floods, landslides, and earthquakes. This paper found that disruption of railway services was associated with geo-physical structure of the region, which triggered the disaster vulnerability. The results showed that climate disasters had a significant impact on railway infrastructure in Northeast India in many aspects. Formulation and implementation of strategic policies might reduce the disaster risks. Therefore, policymakers and Ministry of Railways, Government of India should consider this possible probability approach over environmental determinism.

Complex Operation of Types of Transport in the Conditions of Military International Transportation

The article identifies the priority approaches to the technical and technological modernization of the main modes of transport of Ukraine and allied countries to ensure high-quality transportation of various goods, especially military goods, in the conditions of hostilities and during the period of economic recovery. The author proposes an algorithm for the formation of logistics chains for the delivery of military cargoes according to two schemes (from the United States and Western Europe). The necessity of creating a unified transport system, addressing the issue of further proportional development of the unified transport system and each of its components (modes of transport, structural units) is described. The main aspects that will affect the prospects for the development of a unified transport system are identified. The main tasks of the transport system that need to be solved to meet the needs of the state economy and the population in transportation are described. It is determined that when establishing the priority and order of restoration of the economic and social sectors destroyed by the war, it is necessary, first of all, to direct all resources (financial and material) to the restoration and development of transport, primarily rail and air transport. This is the only approach to solving the complex problem of improving the quality of transport. The complexity of its solution lies in ensuring, taking into account the actual volumes of transportation, the proportional development of each element of the transport modes.

Alignment of port policy to the context of the Physical Internet

ABSTRACT The Physical Internet (PI) is a paradigm-changing and technology-driven vision, which is expected to significantly impact the development of the freight transport and logistics (FTL) system of today. However, the development of the FTL system towards the PI creates much uncertainty for its current stakeholders. Ports are one of those stakeholders that are expected to be profoundly affected by these developments. However, research that focuses on port policy, under the uncertain developments towards the PI, is still lacking. By providing port authorities with insights and recommendations on robust policy areas, we address this void in literature. We conduct a scenario analysis in combination with multi-criteria decision analysis (MCDA) to determine the importance of port performance indicators and policy areas in different scenarios. The most significant, uncertain, and orthogonal factors for the development of the PI are technological development and institutional development. We find that for a proper alignment with the PI vision, in three out of four scenarios, ports should prioritize the implementation of digital solutions and standards, as opposed to an infrastructure focused policy.

Industry 4.0 technologies as a driver for the development of intelligent transport systems

The technologies of Industry 4.0 are considered as a driver of digitalization of transport and logistics ecosystems and their practical significance for increasing the efficiency of the industry, integrating with intellectual priorities for the development of the digital economy at the national and interstate levels.We used materials from open sources on the Internet, as well as the Transport Strategy of the Russian Federation until 2030, the strategy for the digital transformation of key sectors of the economy. The methodology of theoretical research, comparative and system analysis were applied. The results of the study show that the level of implementation of digital services and the digital connectivity of participants in the transport and logistics market will become determining factors in the process of increasing the competitiveness and efficiency of the transport industry.The results of the current stage of the digital transformation of the industry are characterized, the concept of the development of the logistics sector is presented, taking into account the current geopolitical agenda.Further scaling the use of digital services in transport and logistics ecosystems will solve the problem of ensuring the transport and digital sovereignty of the Russian Federation in the face of modern geopolitical challenges and will form points of growth for the competitiveness of the Russian transport system for Industry 4.0.In the context of the fourth industrial revolution, in order to increase the competitiveness of both the Russian economy as a whole and the transport and logistics industry, Russia will have to participate more actively in shaping the landscape of high-tech production, creating and implementing the main digital technologies that ensure its functioning.

THUẬT TOÁN SAI PHÂN HIỆU CHỈNH LAX FRIEDRICHS CHO BÀI TOÁN DỰ BÁO MẬT ĐỘ GIAO THÔNG CÓ TRỄ

In recent years, traffic density forecasting has been playing an important role in developing and improving the performance of intelligent traffic systems. Traffic density forecasting to optimize traffic management, urban management of vehicular traffic have the ability to coordinate traffic, optimize traffic light signals and apply intelligent regulation based on forecasts, thereby improving the handling of the road segment and reducing travel time. Therefore, the construction of predictive algorithms along with integration into traffic management systems is essential to promote the sustainable development of intelligent transportation systems in the future. In this research, an algorithm has been developed to predict traffic density on the delayed Lighthill - Whitham - Richards (LWR) model, in which a regularization difference method has been proposed as the basis for the algorithm. This article mainly focus on building an algorithm and performing experimental calculations to verify the correctness of the algorithm on a mathematical model.

Applications of Big Data technology in Intelligent Transportation System

With the continuous development of Intelligent Transportation System (ITS), amount of data can be collected, generated and used by ITS, and the application of big data technology in Intelligent Transportation System is also applied in different kinds of fields. This paper first explains the definition of big data and intelligent transportation systems. Second, the framework of big data technology in the Intelligent Transport System (ITS) is introduced, which summarizes the data collection methods in specific scenarios of ITS, the application methods of data analysis in ITS, and the application of big data technology in ITS. Furthermore, this paper introduces several case studies on the application of big data technology in ITS, including public transport service planning, road traffic flow prediction, road traffic accident analysis, personal travel route planning, rail traffic management and control, and data analysis and prediction of bridge safety. Finally, this paper discusses some challenges of using big data technology in ITS.

Analysis of Key Factors Affecting Low-Carbon Travel Behaviors of Urban Residents in Developing Countries: A Case Study in Zhenjiang, China

The transport sector accounts for 23% of global carbon emissions, the second largest after electricity and heat generation. Low-carbon travel, such as walking, cycling, and use of public transit, has become an effective way to reduce transportation-related emissions, however, what factors and how they affect low-carbon travel behavior remain unclear. This paper analyzes the key factors that affect the low-carbon travel behavior of urban residents by exploring 26 potential correlation indicators. Low-carbon travel survey data of urban residents in Zhenjiang, China are used as an example. Five factors derived from 26 indicators were identified and named as key factors influencing urban low-carbon travel behavior: traveler personal attributes (contribution rate 31.646%), user satisfaction with travel processes (contribution rate 17.438%), attitude towards low-carbon travel and environmental awareness (16.090%), the level of public transportation system development (10.793%), and overall attributes of travelers’ family (10.561%). The results provide a theoretical basis for the development and implementation of novel urban low-carbon travel concepts in the future.

Опыт и перспективы автоматизации управления перевозочным процессом скоростного транспорта городских агломераций

The article analyses the technological process of transportation process organization and its control in different systems of urban agglomeration rapid-transit transport. It is presented the generalization of organization technological schemes of transportation process at the compilation of normative documents-schedules of: train traffic, rolling stock turnover, work of locomotive teams. Common features, allowing to share automation and digitization positive experience from one transport systems to another, are revealed. As a typical example for urban agglomeration rapid-transit transport, the work of Moscow Central Ring in data flow diagram notation is considered. It has been shown that the conditions of traffic planning on Moscow Central Ring are analogous to active ones on the subway ring lines. As generalizing notions, there are: mixing, non-parallelism, zoning, non-autonomy. Corresponding illustrative examples are given. Congested experience in the sphere of control automation for transportation process of rapid-transit transport of urban agglomeration is considered on the examples of railway section Nizhniy Novgorod – Uren’ as well as Kaluzhsko-Rizhskaya line of Moscow subway (electrodepots “Kaluzhskoye” and “Sviblovo”). The article describes initial data sets for to perform train traffic schedule, the purpose of its performance has been formulated, limitations, reflecting the links between objects, inside the set of given resources, and limitations, being defined by rules of passenger service, have been revealed. Analysis, pursued in the article, has shown the perspective directions of automated transport systems development on knowledge accumulated bases. As a result of the application of complex approach to the solution of automated control tasks at the use of artificial intelligence technologies and big databases usage, it’s planned to increase the efficiency usage for given resources set, train traffic schedule implementation percentage and others; to reduce information transfer error number as well as those, appeared as a result of negative human factor influence and so on.

Attention-Based Multiple Graph Convolutional Recurrent Network for Traffic Forecasting

Traffic forecasting is essential in the development of intelligent transportation systems, as it enables the formulation of effective traffic dispatching strategies and contributes to the reduction of traffic congestion. The abundance of research focused on modeling complex spatiotemporal correlations for accurate traffic prediction, however many of these prior works perform feature extraction based solely on prior graph structures, thereby overlooking the latent graph connectivity inherent in the data and degrading a decline in prediction accuracy. In this study, we present a novel Attention-based Multiple Graph Convolutional Recurrent Network (AMGCRN) to capture dynamic and latent spatiotemporal correlations in traffic data. The proposed model comprises two spatial feature extraction modules. Firstly, a dot product attention mechanism is utilized to construct an adaptive graph to extract the similarity of road structure. Secondly, the graph attention mechanism is leveraged to enhance the extraction of local traffic flow features. The outputs of these two spatial feature extraction modules are integrated through a gating mechanism and fed into a Gated Recurrent Unit (GRU) to make spatiotemporal interaction predictions. Experimental results on two real-world traffic datasets demonstrate the superiority of the proposed AMGCRN over state-of-the-art baselines. The results suggest that the proposed model is effective in capturing complex spatiotemporal correlations and achieving about 1% improvements in traffic forecasting.

Optimal design method of public transit network considering transfer efficiency

AbstractImproving the sharing rate of public transport and reducing operation costs are key factors affecting the sustainable development of the public transport system. After quantifying the impact of transfer, this study proposes a multi‐objective optimization model of bus networks considering the integral transfer efficiency and operation costs, whose optimal solution is hard to find. Since the heuristic algorithm has great global searchability, a non‐dominated sorted genetic algorithm (NSGA‐II) is adopted to solve this NP‐hard problem. Also, an adaptive mutation mode selection algorithm is proposed to enhance the solution efficiency. The optimization framework is applied to the benchmark case of the Swiss public transit network. The results show that the method significantly improves the transfer efficiency of the designed network when compared to existing studies. After calculation, it is found that enhancing the direct rate can improve transfer efficiency, but with high costs. Moreover, the proposed method can design an efficient and economic network to balance the benefits of passengers and operators in practice.

An advanced intermodal service network model for a practical transition to synchromodal transport in the US Freight System: A case study

• Provide managerial insights for synchromodal freight transportation network designs in long haul transportation. • Present routing models based on MIP for a free mode choice network with different intermodal terminal schedules and delays. • Develop a genetic algorithm to solve the model for an advanced intermodal service network design. Free mode choice, termed “synchromodality,” is an extension of intermodal service network design and is still in the early stages of modeling development. European countries have already started moving toward realizing this innovative transportation system. However, advancement in global transport with longer distances is rare and needs more infrastructural preparation and studies to clarify the steps for such a transition. In this paper, an advanced intermodal service network model (AI-SNM) is proposed to support the development of synchromodal transportation systems. This mixed-integer programming (MIP) model finds the optimal path between O/D pairs while considering horizontal integration of variant transport modes in a supply chain network along with resource constraints and time windows. It minimizes the total transportation cost, transshipment cost, and tardiness with a penalty for delays at intermodal terminals and overdue costs at the destination that accounts for the opening and closing times of the terminals. In order to solve the model for large problem instances, an efficient multiobjective genetic algorithm using a novel coding approach is developed. The algorithm is tested on two US-based case studies, showing the capability of the model to provide cost- and time-saving advantages in long-haul freight. The results of this study can be applied to long-distance global transportation with similar geography and scale.

Joint offloading decision and resource allocation in vehicular edge computing networks

With the rapid development of Intelligent Transportation Systems (ITS), many new applications for Intelligent Connected Vehicles (ICVs) have sprung up. In order to tackle the conflict between delay-sensitive applications and resource-constrained vehicles, computation offloading paradigm that transfers computation tasks from ICVs to edge computing nodes has received extensive attention. However, the dynamic network conditions caused by the mobility of vehicles and the unbalanced computing load of edge nodes make ITS face challenges. In this paper, we propose a heterogeneous Vehicular Edge Computing (VEC) architecture with Task Vehicles (TaVs), Service Vehicles (SeVs) and Roadside Units (RSUs), and propose a distributed algorithm, namely PG-MRL, which jointly optimizes offloading decision and resource allocation. In the first stage, the offloading decisions of TaVs are obtained through a potential game. In the second stage, a multi-agent Deep Deterministic Policy Gradient (DDPG), one of deep reinforcement learning algorithms, with centralized training and distributed execution is proposed to optimize the real-time transmission power and subchannel selection. The simulation results show that the proposed PG-MRL algorithm has significant improvements over baseline algorithms in terms of system delay.

Physical Structure Expression for Dense Point Clouds of Magnetic Levitation Image Data.

The research and development of an intelligent magnetic levitation transportation system has become an important research branch of the current intelligent transportation system (ITS), which can provide technical support for state-of-the-art fields such as intelligent magnetic levitation digital twin. First, we applied unmanned aerial vehicle oblique photography technology to acquire the magnetic levitation track image data and preprocessed them. Then, we extracted the image features and matched them based on the incremental structure from motion (SFM) algorithm, recovered the camera pose parameters of the image data and the 3D scene structure information of key points, and optimized the bundle adjustment to output 3D magnetic levitation sparse point clouds. Then, we applied multiview stereo (MVS) vision technology to estimate the depth map and normal map information. Finally, we extracted the output of the dense point clouds that can precisely express the physical structure of the magnetic levitation track, such as turnout, turning, linear structures, etc. By comparing the dense point clouds model with the traditional building information model, experiments verified that the magnetic levitation image 3D reconstruction system based on the incremental SFM and MVS algorithm has strong robustness and accuracy and can express a variety of physical structures of magnetic levitation track with high accuracy.

Bidirectional spatial–temporal traffic data imputation via graph attention recurrent neural network

Spatiotemporal traffic data is increasingly important in transportation services with the development of intelligent transportation system (ITS). However, due to various unpredictable disruptions in the data collection and storage process, traffic data is often incomplete which will seriously hinder downstream tasks if not handled properly. Most existing methods for traffic data imputation either impose too strong assumptions on the data distribution or almost ignore the interdependencies across time steps and the information expressed by missingness. In this article, we propose a graph attention recurrent neural network (GARNN) for traffic data imputation. In our model, we impute data from both temporal and spatial perspectives. First, we model the observations and missingness separately via two LSTMs to treat the missingness of data as another special information distinct from observations. Then, a decay mechanism and graph attention network (GAT) are applied to learn the interdependencies across time steps and capture the spatial correlations respectively to generate temporal estimation and spatial estimation. Finally, those two estimations are integrated into the ultimate imputation. The whole process is in a bidirection. The proposed method is evaluated on two public datasets under three different missing scenarios. Experimental results show the effectiveness of the proposed model compared with other baselines.

Potential for Transition to Low-Carbon Urban Road Transport in Developing Countries: Insights from Literature

It is projected that if no actions are taken to cut down the current rate of global warming, global temperatures will rise to irreversible levels between the years 2030 and 2050. Consequently, climate-induced risks to health, livelihoods, food and water supply/security, economic development, and human security will increase. However, although many efforts have been put in the developed nations to enhance sustainability in cities through the development of a Low-carbon transportation system, the situation is different in the developing nations. These countries are, for some reason, reluctant to adopt the concept of Low-carbon cities. For example, in Kenya, despite the evident climate threat associated with rising urban carbon emissions in the country, very little action to curb the emissions is observed both at individual and government levels. This is especially so in the transport sector. Therefore, it is critical that suitable low-carbon transition pathways are identified and adopted in the sector in order to cut down the emission levels. Although the transition to low-carbon transport is faced with a myriad of challenges, transitions bring about an enormousness of environmental and societal benefits through the development of environmentally sustainable systems. Policy instruments are governance tools that can be used to enhance the environmental sustainability of urban transport options. However, climate change targets cannot be realistically attained by a single policy instrument. Instead, research indicates the need for more integrative and strong policy packages that can stimulate low-carbon transitions. In view of this, there is a clear need for the envisioning of sustainable future cities and potential transition pathways towards the realisation of these futures, as it is considered advantageous to various disciplines. This study provides information that can be used to develop an integrated urban transportation system that is environmentally sustainable and which efficiently incorporates all modes of transport; based on the theory of transitional management

Evaluation of Differential Settlement of Subgrade for Highway-Widening Projects

Highway widening is an important way to improve the existing expressway capacity and promote the development of transportation systems. The differential settlement between the old and new subgrade is the key factor to evaluate whether it is necessary to carry out ground improvement for the new foundation, which will cause longitudinal pavement cracking and even subgrade instability. Therefore, the most critical task in the highway expansion project is controlling differential settlement, particularly post-construction differential settlement. In this study, a fluid-structure coupling settlement analysis model was developed based on FLAC3D, and a modified Cam-clay (MCC) model was used to describe the difference between new and old foundation parameters. The working conditions of different subgrade heights and groundwater levels were simulated to analyze their influence on the differential settlement during and after construction. With the increment value of the transverse slope (Δi) and maximum slope (k) of new and old subgrade settlement curves as evaluation indexes, the differential settlement size of widened subgrade under different working conditions is evaluated, which provides a basis for the design of widened foundation engineering and provides suggestions on whether reinforcement measures should be taken. The results show that the post-construction differential settlement increases with the increase in groundwater level and subgrade height. Under the requirement of Δi ≤ 0.5%, it is not necessary to take reinforcement measures under the condition of local water levels under a 2 m subgrade height and −9 m and −11 m groundwater levels under a 4 m subgrade height. However, when the water level rises further, or the height of the subgrade increases further, it is necessary to take foundation reinforcement measures. Meanwhile, for the requirement of k ≤ 0.5%, foundation reinforcement measures should be taken for all working conditions regardless of the water level or subgrade height. The research results can provide theoretical value and reference for foundation treatment in roadbed-widening engineering.

Development of intelligent transport systems of Ukraine

The article determines that the possibilities of the extensive direction of meeting the needs of society in increasing the volume of passenger transportation by increasing the number of vehicles are largely exhausted, especially in large cities. A generalization of the modern development of intelligent transport systems (ITS) in Ukraine was carried out in two directions. The direction of introduction of advanced driver assistance systems (ADAS) into vehicles is highlighted. Reasonable expediency of the MaaS (Mobility as a service) direction of regulating passenger flows. The importance of ITS implementation in Ukraine is highlighted. The goals of ITS development and the possibility of their application are considered. The main traffic management systems are outlined using the example of GPS tracking in the city of Lviv. Important aspects of the development of ITS in Ukraine are disclosed and the development of ITS of Ukraine is presented, in order to increase road safety. It has been proven that under the conditions of a well-developed intelligent transport system, non-stop synchronized traffic on streets and roads is ensured.

Operational risk assessment model for city buses

The development of public transport systems presently focuses on sustainability. In this situation, the issue of user (passenger) migration has become important to the transport company as a service provider. This paper presents an integrated model of the operational risk of vehicle incapacity, including the following: costs of incidental repairs, costs of unplanned downtime and costs resulting from potential user migration. The paper presents the results of operational research on buses of two makes over a period of 6 years, in the mileage range of 0–420 000 km. The authors have determined the risk as a regression function of operational mileage to estimate the optimum life cycle. The quality of the vehicle was assessed using the criterion of the maximum cost of the operational incapacity risk being equal to the current residual value of the vehicle. The research results confirmed the suitability of the integrated risk model for a comparison of vehicle makes and assessment of their reasonably foreseeable life cycle in a balanced carrier-vehicleuser system.