Assessing transfer property and reliability of urban bus network based on complex network theory

Exploring the robustness of urban bus network: A case from Southern China

Urban bus networks play an important role, when the capacity of urban public services is evaluated. With recent advancements in Internet and Communication Technologies, there is an emerging interest in building an urban bus network model through open big data. This has rarely been investigated and exposes several challenges in the provision of transportation services in urban planning. On the one hand, it is necessary to combine bus stations based on spatial distance constraints due to their ambiguous definition in open big data; on the other hand, it is difficult and time-consuming to relocate and build new stations, but the optimization of bus lines is relatively easy to implement. This study aimed to develop an explicit methodological framework for building and analyzing two different types of urban bus network model using open big data. Thereafter, the framework was applied in two case studies in China, within a county-level administration and in a region including three county-level administrations. The key result shows that there was a shortage of urban bus services across these different administrations. This paper contributes to the body of research methodologies into public transport networks and to understanding the sharing of urban public services across administrations, improving the management of urban bus networks, and highlighting the importance of examining the characteristics of urban bus network in county-level administrations rather than just in large cities in China.

Structural characteristics and spatiotemporal changes of a reticular river network based on complex network theory

Traffic flow prediction provides support for travel management, vehicle scheduling, and intelligent transportation system construction. In this work, a graph space–time network (GSTNCNI), incorporating complex network feature information, is proposed to predict future highway traffic flow time series. Firstly, a traffic complex network model using traffic big data is established, the topological features of traffic road networks are then analyzed using complex network theory, and finally, the topological features are combined with graph neural networks to explore the roles played by the topological features of 97 traffic network nodes. Consequently, six complex network properties are discussed, namely, degree centrality, clustering coefficient, closeness centrality, betweenness centrality, point intensity, and shortest average path length. This study improves the graph convolutional neural network based on the above six complex network properties and proposes a graph spatial–temporal network consisting of a combination of several complex network properties. By comparison with existing baselines containing graph convolutional neural networks, it is verified that GSTNCNI possesses high traffic flow prediction accuracy and robustness. In addition, ablation experiments are conducted for six different complex network features to verify the effect of different complex network features on the model’s prediction accuracy. Experimental analysis indicates that the model with combined multiple complex network features has a higher prediction accuracy, and its performance is improved by 31.46% on average, compared with the model containing only one complex network feature.

Aiming to deficiency of the existing evaluation & selection methods for urban bus network, the functional theory is introduced to plan evaluation for urban bus network. Firstly, the comprehensive criteria's weight coefficient of every index is obtained by computation of subjective weight coefficient and objective weight coefficient. Secondly, in order to rank the preparative plan of the evaluating value is computed by using vector norm. Thirdly, the elastic factors for each index are computed by partial derivative computation to reflect the influence degree of every index to the overall plan. Finally, applying this model, the four preparatory network schemes of the Chengdu urban bus network are evaluated and ranked. Compared with the real outcome, the rationality of this model is proved.

Using complex network theory, we quantify the topological characteristics and anti-destruction resilience of the 12 playoff teams that contested the 2023 Chinese Basketball Association (CBA) season. By constructing similarity networks based on players’ performance indicators, this study examines various topological features, including node degree, betweenness centrality, clustering coefficient, and average path length, and assesses their robustness under random and deliberate attacks.The results indicate that the structural balance of the network exerts a significant influence on a team’s resilience. For example, the Liaoning Bensteel team exhibited high network connectivity and resilience, with an average node degree of 11, a clustering coefficient of 0.83, and an average path length of 0.72. In contrast, the Jiangsu Kendia demonstrated greater structural vulnerability when key nodes were compromised, possessing a maximum node degree of 10, but a clustering coefficient of only 0.74, leading to an increased average path length of 1.17. Furthermore, teams with high clustering coefficients, such as Shandong High-Speed, which achieved a coefficient of 0.82, exhibit close internal cooperation and efficient ball movement.Consequently, it is recommended that CBA teams enhance their resilience by improving the balance and redundancy of key nodes within their network structures, thereby providing a scientific basis for basketball tactical planning and team management. These findings offer a novel perspective on understanding and enhancing the organizational structure and competitive performance of basketball teams.

Transit Centres for High-Capacity Bus Systems: Functions and Location

Prefabricated buildings are gradually gaining popularity in China because of their lower costs, shorter construction periods, and environmental friendliness. However, compared with traditional buildings, prefabricated buildings require coordination and management of processes such as component production, transportation and storage, and on-site component assembly. Therefore, managing the quality of prefabricated buildings is complex. In order to manage the quality of prefabricated buildings better, it is necessary to study the quality systems of prefabricated buildings. Firstly, by analyzing the logical relationship of each stage of the prefabricated buildings, we identified the key stages. Then, the main quality control factors that affect the qualities and relationships of the main quality control factors were derived by combining relevant building codes, literature studies, and project experiences; a network model of quality control factors of prefabricated buildings (NQPB) was constructed. Secondly, the complex network theory was applied to analyze the node degree, betweenness centrality, clustering coefficient, and other indicators of the NQPB to derive 17 key quality control factor nodes. The importance of the 17 key nodes in the quality management of prefabricated buildings was verified through a simulation. Next, the NQPB of Project A was constructed and analyzed, and five key nodes were identified. This demonstrates how the NQPB method can be applied to a specific prefabricated building project. Moreover, Project A was used as a case study to compare fault tree analysis (FTA), fishbone diagram, and 4MIE methods with the NQPB method to derive the potential advantages of the NQPB method. Finally, further analysis led to recommendations for quality management of prefabricated building projects in general as well as for Project A. This study can be used as a reference for quality control and assurance managers in all stages of prefabricated buildings and provide new ideas and methods for the quality analysis and management of prefabricated buildings.

With the expansion of the city scale, the passenger traffic of urban rail transit is increasing, and more and more rail transit stations and lines are overloaded, which puts higher requirements on the safety and reliability of urban rail transit networks. In order to analyze the topological nature of the complex network of the subway, this paper takes Suzhou City as an example, selects 93 subway stations from the three metro lines in Suzhou as of the present sample as sample data, uses complex network theory to conduct research, and builds a neighboring site based. Undirected unweighted network model. The study found that the average traffic network of Suzhou Metro is 2, the clustering coefficient is 0, and the average shortest path is 13. Through the analysis of the Suzhou Metro transportation network, it may be instructive for the future construction of the Suzhou Metro network.

Objective To investigate the small-worldness and the betweenness centrality of the nodes in the brain structural networks and its relationship with the course and the central role of the brain regions in the transmission of information across the whole brain in depression. Methods The diffusion tensor imaging data were obtained from 27 depression patients and 33 healthy controls. The brain structural networks were constructed using the complex network theory. Results The brain structural networks had small-world properties in both groups. When compared with the healthy, the betweenness centrality of the nodes of the networks in depression significantly decreased in right superior frontal gyrus (orbital part) (P=0.00035, region survived critical FDR threshold for multiple comparisons), and left putamen (P=0.00054, region survived critical FDR threshold for multiple comparisons). Significant negative correlation was found between the betweenness centrality of left hippocampus and the course in the depression(r=0.50, P=0.016). Conclusion Both of the brain structural networks in depression patients and normal people have the property of small-worldness. But the central role of orbit frontal cortex and putamen in the transmission of information across the whole brain is declined, and the betweenness centrality of hippocampus is negatively related with the course in the depression. Key words: Depression; Diffusion tensor imaging; Brain structural network; Small-world; Betweenness centrality

The mechanical reliability of Water Distribution Networks (WDNs) is a relevant technical and scientific issue. During planned maintenance or unplanned interruptions, the affected area must be isolated by valves shutdown. This operation involves the alteration of the network structure, i.e., the domain of the hydraulic system, and for this reason the isolation valve system plays a central role. Some studies started to consider the presence of the isolation valve system in WDNs reliability analysis.Accordingly, this work uses the Complex Network Theory to analyse the isolation valve system performance and to assess the importance of the segments generated by valves shutdown. Differently from the classic complex network theory approach, in this work the recently proposed WDN-relevance-based betweenness centrality is applied to the segment graph to introduce information about the relevance of the different elements into the network, considering geometric and hydraulic parameters, such as length, demand, risk of disconnection, etc. The proposed strategy also suggests an improvement in the representation of the segment graph with respect to the presence of parallel edges.The strategy is presented using a small network, while it is demonstrated and discussed using a real WDN. The results indicate that the WDN-relevance-based betweenness centrality allows to effectively assess the importance of the segments generated by valves shutdowns, also providing indications to improve the isolation valve system design.

Empirical Study of Chinese Airline Network Structure Based on Complex Network Theory

Analysis of Internet topologies has shown that the Internet topology has negative curvature, measured by Gromov's “thin triangle condition”, which is tightly related to core congestion and route reliability. In this work we analyze the discrete Ricci curvature of the Internet, defined by Ollivier [1], Lin et al. [2], etc. Ricci curvature measures whether local distances diverge or converge. It is a more local measure which allows us to understand the distribution of curvatures in the network. We show by various Internet data sets that the distribution of Ricci cuvature is spread out, suggesting the network topology to be non-homogenous. We also show that the Ricci curvature has interesting connections to both local measures such as node degree and clustering coefficient, global measures such as betweenness centrality and network connectivity, as well as auxilary attributes such as geographical distances. These observations add to the richness of geometric structures in complex network theory.

With the development of the public transportation, bus network becomes complicated and hard to evaluate. Transfer time is a vital indicator to evaluate bus network. This paper proposed a method to calculate transfer times using Space P. Four bus networks in China have been studied in this paper. Some static properties based on graph theory and complex theory are used to evaluate bus topological structure. Moreover, a bus network evolution model to reduce transfer time is proposed by adding lines. The adding method includes four types among nodes with random choice, large transfer time, degree, and small degree. The results show that adding lines with nodes of small degree is most effective comparing with the other three types.

To investigate local and global efficiency changes characterized by small-world properties based on resting-state functional MRI, such as centrality and clustering coefficient, in mild traumatic brain injury (MTBI) patients; and to associate these findings with axonal injury as measured by diffusion tensor imaging (DTI) as well as with post-concussive symptom (PCS). Thirty patients (mean age 35 ± 13 years) with clinically defined MTBI and 45 age-matched healthy controls (mean age 37 ± 10 years) participated in the experiments. Resting-state functional MRI was performed using gradient echo planar imaging sequence with 3 Tesla MRI scanner to obtain functional small-world networks. Out of all participants, 20 MTBI patients and 20 controls had available DTI data with three b-values (0, 500, 1000) s/mm2 and 30 directions for diffuse axonal injury analyses. Compared with controls, MTBI patients showed lower relative betweenness centrality (P = 0.01), but significantly higher clustering coefficient (P = 0.04), and these two metrics correlated negatively in patients (r = -0.77; P < 0.001). Regions with lower betweenness centrality (e.g., frontal and occipital) corresponded with the regions of reduced FA in patients, while global FA reduction correlated with betweenness centrality (r = 0.48; P = 0.03) and clustering coefficient (r = -0.46; P = 0.04) in MTBI patients. In addition, there was significantly higher thalamocortical connectivity that correlated with clustering coefficient (r = 0.39; P = 0.03) in patients. Also, patients with higher clustering coefficient tended to have less PCS score with negative correlation (r = -0.4; P = 0.04). Our results demonstrated significant functional small-world properties changes in patients with MTBI, and suggest decreased global efficiency, possibly due to diffuse axonal injury and local network upregulation including increased thalamo-cortical connectivity. 2 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:518-527.