The t-SNE technique is a perfect visual method for this map, and gave a complete and clear description. To indicate the change of hot topics, we divided the timespan into — and —, as shown in Figure 9. The top three keywords are model, infrastructure and impact.
Railroad infrastructure increased the shipment of goods
The related keywords experienced a significant increase; in particular, keyword impact-related topics included climate, urban studies, land use, resilience and accessibility, which indicated this role. However, this network only shows information based on the collected records, and its difference from the co-citation network is the limitation of this relatively incomplete data. Therefore, the co-citation analysis further solves the data incompleteness in the next section. Co-citation analysis has been defined as the frequency with which two articles are cited together in another article [ 59 ].
In this section, co-citation analysis identifies the underlying intellectual structures of the knowledge in the field of transportation infrastructure according to references.
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The co-citation network was generated based on valid records between and , and the top 50 most cited publications in each year were used to construct a network of references cited in that year. As shown in Figure 10 , the synthesized network contains references and co-citation clusters after the clustering process. This network has a modularity of 0. The mean silhouette is 0. The major clusters that we focus on in this paper were sufficiently high. The areas in different colors indicate the time at which co-citation links in those areas appeared for the first time.
Areas in green were generated earlier than areas in yellow. Each cluster can be labeled by title terms, keywords, and abstract terms of articles citing the cluster. We can see that studies related to new application, cost overruns and case study appeared earlier, and urban transportation and public-private partnerships appeared more recently.
Engineering Economics and Finance for Transportation Infrastructure
In addition, cluster areas of new transport infrastructure, cost overruns and evidence study are relatively bigger, which means that these studies received more attention. According to the LLR, labels of the largest 62 clusters were summarized as shown in Appendix A and the most active citer can be checked in Appendix B. In addition, the timeline visualization in CiteSpace depicted clusters along horizontal timelines. As shown in Figure 11 , each cluster was displayed from left to right and clusters were arranged vertically in descending order of their size.
The colored curves represent co-citation links added in the year of the corresponding color. Large-sized nodes or nodes with red tree rings received particular attention because they were either highly cited or had citation bursts, or both. We can see that the three most-cited references in a particular year are displayed.
The labels of these references were placed in the lowest position. Figure 11 shows the top 2 largest clusters, listed as cluster 0 and cluster 1. The periods in which the clusters were sustained were different, which means that the difference of topic activity. For example, topic 0 cost overrun was active during the period from to and most of the top active topics were active about 20 years.
Furthermore, the top ten largest clusters include cost overrun, quantitative spatial economics, prioritizing highway defragmentation location, local development, land value, regional economic growth, new transportation infrastructure, public-private partnerships, infrastructure change region, recent laboratory research and microbial engineering. All of these clusters have relative network sub-structures and research status, and trends hide in these references.
For example, for the cluster around spatial economics, to was the most active timespan for citers. The analysis above shows the research base and fronts that mine the potential research challenges and trends. In addition, main research topics were further analyzed according to the selected and filtered data above. Table 3 shows the temporal properties of major clusters. We can see that most of the representative references are related to the spillover effect of the transportation infrastructure.
For example, Cluster 0 cost overrun is the largest cluster, containing 94 references from to The mean year of all references is and the year of the most representative cited articles in this cluster is , too. The timeline visualization reveals the top three cited references from the period of to We can see that the period to was full of high-impact contributions—large colored citation circles and red citation bursts. We chose the top three cited circles and nine references to analyze the main research topics. Similarly, in the other five clusters, the top three circles and nine representative references were chosen to further analyze the hot research status and research trends.
Appendix C shows the high-impact members of the other clusters. These authors may be not the most highly cited authors, but they play important roles in the corresponding fields. The co-citation network above was divided into co-citation clusters. These clusters were labeled by index terms from their own citers.
These keywords show the most representative research topics related to transportation infrastructure. The left part of Figure 12 shows the word cloud based on cluster labels filtered by the same or similar labels of clusters. In this figure, the keyword size represents the frequency of cluster labels. It is clear that the main research topics include economic, region or urban development and spatial effect analysis. However, the cluster data only analyzed the label information, and did not identify other potentially relevant information.
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Therefore, a report of automatically generated narratives was used to analyze the word cloud distribution further, as shown in the right part of Figure The narratives include the main subjects in the titles and abstracts of the top references in the top 62 clusters that are relatively complete. We can see that hot research topics consist of urban development, project, economic, cost and policies. In particular, we identified some potential topics that were excluded in the left graph, such as land, risk, panel data and policies.
By means of the two-step summary, potential keywords could be easily identified. Word cloud distribution of co-citation cluster results. Tool: Tagxedo www. Data source: Labels of the top 62 clusters, narrative summary report of the co-citation network. Additionally, key concepts identified from the titles of citing articles in Cluster 0 were algorithmically organized according to hierarchical relations derived from co-occurring concepts. Figure 13 shows the main concept tree of Cluster 0. The largest branch of such a hierarchy typically reflects the main concepts of scholarly publications produced by the specialty behind the cluster.
The main logical categories include transport infrastructure, projects, cost overruns and impact.
It is notable that the transportation infrastructure branch highlights the characteristics large, resilience, spatial and complexity , research methods modeling, econometric and network mapping and research questions quality, risk, performance and PPP. In other words, sub-categories in this figure indicate the characteristics, questions, objects, dimensions and methods related to transportation infrastructure.
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The identity of category labels based on the title data obeys the logical tree algorithm of the software Citespace. This figure not only shows the main research topics but the logical relationships among these topics. To understand the hierarchy better, the key concepts in the top 7 Clusters 0— 6 were identified in one hierarchy. Figure 14 summarizes the concept tree generated by Citespace according to the reference titles in Cluster 0— 6.
The categories colored blue were identified automatically. For the systematic expression of the hierarchy, some branches colored green are used to conclude the fragmented research questions. This means the topic of spillover effect is the intensive research issue, and there are many countries analyzing the impacts of transportation infrastructure on the national scale. Compared with Figure 13 , this hierarchy identified more specific topics, such as rail and road research. Although the amount of data in Figure 14 is about seven times greater than in Figure 13 , the hierarchy framework becomes more clear and systematic after filtering out repeated data.
More importantly, this systematic hierarchy can help to identify the hottest and most representative research issues quickly. This scientometric review based on over publications from to presented the systematic knowledge structure related to impacts of transportation infrastructure on sustainable development.