Transport Network Nodes Research Articles

Sustainable Transportation: Exploring the Node Importance Evolution of Rail Transit Networks during Peak Hours

The scientific and rational assessment of the evolution of node importance in rail transit line networks is important for the sustainability of transportation systems. Based on the complex network theory, this study develops a weighted network model using the Space L method. It first considers the network topology, the mutual influence of neighboring nodes of the transportation system, and the land use intensity in the station influence domain to construct a comprehensive index evaluation system of node importance. It then uses the covariance-weighted principal component analysis algorithm to more comprehensively evaluate the node importance evolution mechanism and analyzes the similarity and difference of the sorting set by adopting three different methods. The interaction mechanism between the distribution of important nodes and the evolution of land use intensity is explored in detail based on the fractal dimension theory. The Xi’an rail transit network is considered an example of qualitative and quantitative analysis. The obtained results show that the importance of nodes varies at different times of the day and the complexity of the morning peak is more prominent. Over time, articulated fragments with significance values greater than 0.5 are formed around the station, which are aligned with the direction of urban development, creating a sustainable mechanism of interaction. As the network’s crucial nodes in the center of gravity increase and the southern network expands, along with the increased intensity of the city’s land utilization, the degree of alignment in evolution becomes increasingly substantial. Different strategies for attaching the network, organized based on the size of Si can lead to the rapid damage of the network (reducing it to 0.2). The identification of crucial nodes highlighted in this paper serves as an effective representation of the functional characteristics of the nodes in transportation networks. The results obtained can provide a reference for the operation and management of metro systems and further promote the sustainable development of transportation networks.

Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks

Novel architectures incorporating transport networks and artificial intelligence (AI) are currently being developed for beyond 5G and 6G technologies. Given that the interfacing mobile and transport network nodes deliver high transactional packet volume in downlink and uplink streams, 6G networks envision adopting diverse transport networks, including non-terrestrial types of transport networks such as the satellite network, High-Altitude Platform Systems (HAPS), and DOCSIS cable TV. Hence, there is a need to match the traffic to the transport network. This paper focuses on such a matching problem and defines a method that leverages machine learning and mixed-integer linear programming. Consequently, the proposed scheme in this paper is to develop a traffic steering capability based on types of transport networks, namely, optical, satellite, and DOCSIS cable. Novel findings demonstrate a more than 90% accuracy of steered traffic to respective types of transport networks for dedicated transport network resources.

Multimodal network path optimization based on a two‐stage algorithm in the perspective of sustainable transport development

AbstractThe environmental issues brought on by carbon emissions from transport have risen to prominence in recent years. More and more academics are using the multi‐objective path optimization method to solve the multimodal optimization problem from the standpoint of sustainable development in order to address the environmental issues brought on by the transport process. The research proposes a two‐stage method to handle multi‐objective optimization convergence and simplify multimodal transport path optimization. In the first stage, a fuzzy C clustering model is established, and based on the clustering results, the multimodal transport network nodes are identified. In the second stage, a multimodal transport multi‐objective path optimization model is established, and the optimal path is solved using a genetic algorithm. The research method was applied in the Bohai Rim region. Results indicated that the fuzzy C‐clustering method and the genetic method were able to select the optimal node city, thus solving the actual site selection problem of multimodal transportation networks. Using the FCM model, the 86 city nodes were categorized into four types, leading to the establishment of the most proficient multimodal transportation network in the Bohai Rim region. Using a genetic algorithm for optimization, a stable state is reached after 25 iterations. In the validation experiment on path optimization, the cost was reduced by 47.12% compared to the minimum single objective time, and transportation carbon emissions saw a reduction of 28.23%. Similarly, compared to the lowest target for transportation carbon emissions, the cost was reduced by 39.48% and the time was reduced by 38.12%. Compared to the lowest target for transportation carbon emissions, the time was reduced by 32.02% and the carbon emissions were reduced by 19.23%. Notably, the transportation multi‐objective path optimization model showed significant improvement compared to the single‐target model. The research method has been proven to be superior, and can offer the most optimal transportation route guidance for participants in multimodal transportation. Furthermore, it can effectively tackle the issue of node selection convergence and multi‐objective optimization, while also serving as a valuable source of data to support the theoretical advancement of multimodal transportation network path optimization.

Gray Correlation Entropy-Based Influential Nodes Identification and Destruction Resistance of Rail-Water Intermodal Coal Transportation Network

Evaluating the importance of nodes in coal transportation networks and identifying influential nodes is a crucial study in the field of network science, vital for ensuring the stable operation of such complex networks. However, most existing studies focus on the performance analysis of single-medium networks, lacking research on combined transportation, which is not applicable to China’s coal transportation model. To address this issue, this paper first establishes a static topological structure of China’s coal-iron-water combined transportation network based on complex network theory, constructing a node importance evaluation index system through four centrality indicators. Subsequently, an enhanced TOPSIS method (GRE-TOPSIS) is proposed based on the Grey Relational Entropy Weight (GRE) to identify key nodes in the complex network from local and positional information dimensions. Compared to previous studies, this research emphasizes composite networks, breaking through the limitations of single-medium network research, and combines gray relational analysis with entropy weighting, enhancing the objectivity of the TOPSIS method. In the simulation section of this paper, we establish the model of China’s coal-iron-water combined transportation network and use the algorithm to comprehensively rank and identify key nodes in 84 nodes, verifying its performance. Network efficiency and three other parameters are used as measures of network performance. Through simulated deliberate and random attacks on the network, the changing trends in network performance are analyzed. The results show that in random attacks, the performance drops to around 50% after damaging nearly 40 ordinary nodes. In contrast, targeting close to 16 key nodes in deliberate attacks achieves a similar effect. Once key nodes are well protected, the network exhibits a certain resistance to damage.

Wind Field Numerical Simulation of a Cable-stayed Bridge in a Mountainous Area Using Improved Inlet Boundary by CIRFG Method

Bridges in mountainous areas are indispensable nodes in transportation networks, and wind resistance capabilities have become a controlling factor of long-span bridges built in mountain areas. Therefore, it is necessary to study the characteristics of wind fields under complex terrain. An improved inlet boundary by fitting the boundary curve was proposed in this study. The inlet fluctuating wind field was generated by the Correlation Improved Random Flow Generation method (CIRFG). The results of the numerical simulations show that the fluctuating wind input generated by CIRFG tallies with the target wind field, which proves the reliability of the proposed method. The method of fitting boundary curves to give inlet wind speed profiles can achieve non-uniform wind profile inputs. The results show the wind direction of the gorge varies significantly by height. The wind speed at the summit will accelerate affected by the terrain. Also influenced by the terrain, the turbulence intensity profiles in the simulated area show an S-shape. The transverse wind and angle of attack are uneven along the main girder, especially near slopes. The conclusions obtained in the study can provide references for the wind resistance of bridges built in mountainous areas.

Transportation Centrality: Quantifying the Relative Importance of Nodes in Transportation Networks Based on Traffic Modeling

Transportation Centrality: Quantifying the Relative Importance of Nodes in Transportation Networks Based on Traffic Modeling

Gromov centrality: A multiscale measure of network centrality using triangle inequality excess.

Centrality measures quantify the importance of a node in a network based on different geometric or diffusive properties, and focus on different scales. Here, we adopt a geometrical viewpoint to define a multiscale centrality in networks. Given a metric distance between the nodes, we measure the centrality of a node by its tendency to be close to geodesics between nodes in its neighborhood, via the concept of triangle inequality excess. Depending on the size of the neighborhood, the resulting Gromov centrality defines the importance of a node at different scales in the graph, and it recovers as limits well-known concepts such as the clustering coefficient and closeness centrality. We argue that Gromov centrality is affected by the geometric and boundary constraints of the network, and illustrate how it can help distinguish different types of nodes in random geometric graphs and empirical transportation networks.

On the Analytical Probabilistic Modeling of Flow Transmission Across Nodes in Transportation Networks

This paper focuses on the analytical probabilistic modeling of vehicular traffic. It formulates a stochastic node model. It then formulates a network model by coupling the node model with the link model of Lu and Osorio (2018), which is a stochastic formulation of the traffic-theoretic link transmission model. The proposed network model is scalable and computationally efficient, making it suitable for urban network optimization. For a network with [Formula: see text] links, each with a space capacity of one, the model has a complexity of [Formula: see text]. The network model yields the marginal distribution of link states. The model is validated versus a simulation-based network implementation of the stochastic link transmission model. The validation experiments consider a set of small networks with intricate traffic dynamics. For all scenarios, the proposed model accurately captures the traffic dynamics. The network model is used to address a signal control problem. Compared with the probabilistic link model of Lu and Osorio (2018) with an exogenous node model and a benchmark deterministic network loading model, the proposed network model derives signal plans with better performance. The case study highlights the added value of using between-link (i.e., across-node) interaction information for traffic management and accounting for stochasticity in the network.

Applications of machine learning methods in port operations – A systematic literature review

Ports are pivotal nodes in supply chain and transportation networks, in which most of the existing data remain underutilized. Machine learning methods are versatile tools to utilize and harness the hidden power of the data. Considering ever-growing adoption of machine learning as a data-driven decision-making tool, the port industry is far behind other modes of transportation in this transition. To fill the gap, we aimed to provide a comprehensive systematic literature review on this topic to analyze the previous research from different perspectives such as area of the application, type of application, machine learning method, data, and location of the study. Results showed that the number of articles in the field has been increasing annually, and the most prevalent use case of machine learning methods is to predict different port characteristics. However, there are emerging prescriptive and autonomous use cases of machine learning methods in the literature. Furthermore, research gaps and challenges are identified, and future research directions have been discussed from method-centric and application-centric points of view.

A game-theoretic approach for reliability evaluation of public transportation transfers with stochastic features

A game-theoretic approach based on the framework of transferable-utility cooperative games is developed to assess the reliability of transfer nodes in public transportation networks in the case of stochastic transfer times. A cooperative game is defined, whose model takes into account the public transportation system, the travel times, the transfers and the associated stochastic transfer times, and the users’ demand. The transfer stops are modeled as the players of such a game, and the Shapley value – a solution concept in cooperative game theory – is used to identify their centrality and relative importance. Theoretical properties of the model are analyzed. A two-level Monte Carlo approximation of the vector of Shapley values associated with the nodes is introduced, which is efficient and able to take into account the stochastic features of the transportation network. The performance of the algorithm is investigated, together with that of its distributed computing variation. The usefulness of the proposed approach for planners and policy makers is shown with a simple example and on a case study from the public transportation network of Auckland, New Zealand. • A stochastic transferable utility game variation is introduced to assess transfers. • Uncertainty is realized by additional waiting time whenever a transfer is missed. • The approach assess transfer’ reliability effect on the network performance. • An efficient bi-level Monte Carlo approximation is developed. • A real-world public transportation network is analyzed.

Research on Hub-and-Spoke Transportation Network of China Railway Express

China Railway Express is developing rapidly, but the point-to-point direct organization mode has brought many problems to it. Therefore, this paper proposes to construct a hub-and-spoke network and adopt the “collecting and transportation” organization mode. In this paper, based on the single distribution p-hub median problem, a dual-objective planning model was constructed by considering the characteristics of CR Express in terms of cost and time. In addition, considering the port as a key node of international rail transport network, it plays a vital role for CR Express. Therefore, a hub-port allocation model was constructed to determine the hub-port allocation relationship. Furthermore, a Lagrangian relaxation heuristic algorithm was designed to solve the model built for CR Express transportation network. Finally, based on the constructed model, the actual operation data of CR Express were used in the designed example to verify the effectiveness and applicability of the models and methods.

Basic centrality measures on urban transport network nodes

Based on the degree of nodes of urban transport in Dispur town, thirty five bus stations are selected as nodes. From the speculation of complex network, the network model of Dispur urban transport is built. By analyzing the degree centrality index, betweenness centrality index and closeness centrality index of nodes within the network, the extent of centrality of every node within the network is studied. From a special purpose of read to work out the hub node of Dispur urban transport network, appreciate the city's key sites and major transfer sites. The dependableness of the network is decided by the soundness of some key nodes. The analysis of network node centralization will offer a theoretical basis for the rational allocation of urban transport network sites and conveyance system designing.

МОДЕЛИРОВАНИЕ ПРОПУСКНОЙ СПОСОБНОСТИ УЗЛОВ ТРАНСПОРТНОЙ ГОРОДСКОЙ СЕТИ НА ОСНОВЕ МЕТОДОВ НЕЧЕТКОЙ ЛОГИКИ

Traffic state prediction is a key component of intelligent transport systems (ITS) which has attracted a lot of attention over the past few decades. The improvement in the accuracy of mod-eling and predicting the traffic capacity of intersections, depending on such uncertain factors as the intensity of pedestrian flow and its discontinuity, is possible only with the development and use of new methods. In order to form a number of typical control algorithms for each regulated node of a city transport network, the need to cluster them arises. The traffic flow parameters of each separate regulated node of transport network have been measured using convolutional neu-ral networks (YOLOv3). As a result of the analysis of the differences between the clusters in terms of mean values of independent factors, statistically significant differences have been revealed and linear regression models have been detected. On the basis of these models, typical manage-ment decisions on increasing the traffic capacity of regulated nodes of the transport network will be formed. When constructing the model, the fuzzy logic methods, as they more fully reflect the influence of random factors of pedestrian flow on the traffic capacity of the intersection as a whole, have been used.

Basic centrality measures on urban transport network nodes

Based on the degree of nodes of urban transport in Dispur town, thirty five bus stations are selected as nodes. From the speculation of complex network, the network model of Dispur urban transport is built. By analyzing the degree centrality index, betweenness centrality index and closeness centrality index of nodes within the network, the extent of centrality of every node within the network is studied. From a special purpose of read to work out the hub node of Dispur urban transport network, appreciate the city's key sites and major transfer sites. The dependableness of the network is decided by the soundness of some key nodes. The analysis of network node centralization will offer a theoretical basis for the rational allocation of urban transport network sites and conveyance system designing.

Simulation Study on Cascading Failure of Multimodal Transport Network

Cascading failure in multimodal transport network may cause huge economic loss and social impact, which has gradually attracted public attention. In view of the coupling effect of nodes in multimodal transport network and the higher complexity of cascading failure process, the concepts of node correlation degree and node cooperation degree are proposed to characterize the characteristics of the network, and a logit model is introduced to calculate the initial load of nodes. In the case of ignoring network interruption, we propose two load redistribution methods: local allocation and global-local allocation. Taking the multimodal transport network in Sichuan–Tibet region of China as an example, the cascading failure effect of multimodal transport network in Sichuan–Tibet region is quantified by sensitivity analysis. The results show that when the load of the multimodal transport network in Sichuan–Tibet region exceeds the maximum capacity but does not exceed 150%∼170% of the network capacity, the network can still operate normally. In addition, the nodes in the multimodal transport network should have 0.3∼0.5 scalable space. In the cascading failure control method, load redistribution based on global-local allocation can minimize the impact of node overload.

Impacts of Anthropogenic Pollutants on Benthic Prokaryotic Communities in Mediterranean Touristic Ports.

Ports and marinas are central nodes in transport network and play a strategic role in coastal development. They receive pollution from land-based sources, marine traffic and port infrastructures on one side and constitute a potential pollution source for the adjacent coastal areas on the other. The aim of the present study was to evaluate the effects of organic and inorganic co-contamination on the prokaryotic communities in sediments from three Mediterranean ports. The structure and composition of the bacterial and archaeal communities were assessed by targeted metagenomic analysis of the 16S rRNA gene, and the links of prokaryotic communities with environmental and pollution variables were investigated. The harbors presented pronounced site-specificity in the environmental properties and pollution status. Consistently, the structure of archaeal and bacterial communities in surface sediments exhibited a strong spatial variation among the three investigated ports. On the contrary, a wide overlap in composition of prokaryotic assemblages among sites was found, but local variation in the community composition and loss of prokaryotic diversity was highlighted in a heavily impacted port sector near a shipyard. We provided evidences that organic matter, metals and PAHs as well as temperature and salinity play a strong role in structuring benthic bacterial communities significantly contributing to the understanding of their responses to anthropogenic perturbations in marine coastal areas. Among metals, copper was recognized as strongly associated with the observed changes in bacterial assemblages. Overall, this study provides the first assessment of the effects exerted by multiple organic and inorganic contaminations on benthic prokaryotes in ports over a large spatial scale and designates bacterial community as a candidate tool for the monitoring of the sediment quality status in harbors.

Risk in port logistics: risk classification and mitigation framework

Ports are important nodes in maritime transportation networks. However, port managers face different types of risks in port operations which may disrupt cargo flows in the logistics chain. These risk factors are expected to increase in view of such global issues as climate change and geopolitical changes. An overview of risk classifications and mitigation strategies in port logistics can help academics and practitioners prepare for managing port logistics risks. Consequently, this paper presents a conceptual discussion of port logistics risks and illustrates an exemplary define-measure-analyse-improve-control (DMAIC) implementation framework to highlight potential risk management and mitigation strategies in port operations. Our discussion advances knowledge on the different risk factors with a practical step-by-step approach for addressing the risk in port logistics.

Risk in port logistics: risk classification and mitigation framework

Ports are important nodes in maritime transportation networks. However, port managers face different types of risks in port operations which may disrupt cargo flows in the logistics chain. These risk factors are expected to increase in view of such global issues as climate change and geopolitical changes. An overview of risk classifications and mitigation strategies in port logistics can help academics and practitioners prepare for managing port logistics risks. Consequently, this paper presents a conceptual discussion of port logistics risks and illustrates an exemplary define-measure-analyse-improve-control (DMAIC) implementation framework to highlight potential risk management and mitigation strategies in port operations. Our discussion advances knowledge on the different risk factors with a practical step-by-step approach for addressing the risk in port logistics.

SmartTransfer

In urban transportation systems, transfer stations refer to hubs connecting a variety of bus and subway lines and, thus, are the most important nodes in transportation networks. The pervasive availability of large-scale travel traces of passengers, collected from automated fare collection (AFC) systems, has provided unprecedented opportunities for understanding citywide transfer patterns, which can benefit smart transportation, such as smart route recommendation to avoid crowded lines, and dynamic bus scheduling to enhance transportation efficiency. To this end, in this article, we provide a systematic study of the measurement, patterns, and modeling of spatiotemporal dynamics of passenger transfers. Along this line, we develop a data-driven analytical system for modeling the transfer volumes of each transfer station. More specifically, we first identify and quantify the discriminative patterns of spatiotemporal dynamics of passenger transfers by utilizing heterogeneous sources of transfer related data for each station. Also, we develop a multi-task spatiotemporal learning model for predicting the transfer volumes of a specific station at a specific time period. Moreover, we further leverage the predictive model of passenger transfers to provide crowdedness-aware route recommendations. Finally, we conduct the extensive evaluations with a variety of real-world data. Experimental results demonstrate the effectiveness of our proposed modeling method and its applications for smart transportation.

Gantry crane scheduling in intermodal rail-road container terminals

Modern rail-road container terminals serve as important consolidation nodes in intermodal transportation networks where containers are transferred between freight trains and trucks. Among the most important decision problems in these yards is the crane scheduling problem, which decides on the sequences of container moves executed by each of the parallel gantry cranes. This paper treats a rich crane scheduling problem, which considers plenty relevant constraints and is directly applicable in Chinese intermodal terminals. For solving this problem, we propose a fix-and-optimise procedure, which is easy to implement and shown to successfully solve even large-sized instances with 100 container moves in reasonable time.