Africas Transport Infrastructure (Directions in Development)

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Apart from the Delft University of Technology, the top productive institutions did not have higher relative centrality. This means that institutions that published more articles did not play an equally important role in the collaboration network.

The institutions with higher centrality would have greater potential. Furthermore, Figure 6 shows the country collaboration network in — and —; clusters are displayed in different colored circles and they are arranged vertically in the order of their size. In addition, the colored lines represent co-citation links among different countries. Additionally, apart from the top central countries, Spain, Netherlands and Canada had higher published frequencies, which indicates their higher relative potentials. According to the clustering results, we can see the change of research interests.

The labels of clusters were generated by log-likelihood ratio method in the software. It is notable that during —, an overview of popular topics included infrastructure surveillance, local development and evidence; during — those consisted of transportation decision, regional development and infrastructure surveillance. In addition, the clustering members experienced an increase and transfer. Every citation and cited work was assigned to a specific research discipline according to the journals in a global map of science generated from over 10, journals indexed in the WOS [ 58 ].

Therefore, this study built an overlay map to show the dual-map of the science sketch database that perfectly described the interdisciplinary research.

From Mine to Coast: Transport Infrastructure and the Direction of Trade in Developing Countries

Figure 7 shows the main disciplines of collected citing articles and cited articles. The left part of the graph shows the distributed disciplines of citing articles and the right part describes that of cited articles. In addition, the color curves represent the fluctuant relations. It is clear that the journals of citing articles related to transportation infrastructure are mainly distributed in disciplines such as mathematics, systems, economics and physics.

The distribution of cited articles indicates the application fields and research foundations. More importantly, transportation infrastructure papers are published in almost all major disciplines, which means transportation infrastructure studies play important roles in multidisciplinary research. Additionally, the dual-map overlay shows the information about the field studies more macro compared with article clustering analysis. Thus, Figure 8 shows the interdisciplinary co-occurring network of the literature based on the WOS discipline categories.

Innovation Africa - Revolutionizing Africa's transportation infrastructure space

The links among different nodes mean the existence of collaboration among different disciplines. Interdisciplinary research is quite obvious in the field of transportation infrastructure. Keywords catch the core content of a paper, and in this section, the collected keywords show the situation and development of research using the software CiteSpace. According to the valid records collected, the keyword co-occurring network includes nodes and links shown in Figure 9. The node size represents the frequency of a keyword in all records and links among nodes indicate different keywords occurring in the same record.

The t-SNE view was used to lay out the keyword map. 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. 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. 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.

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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.

In addition, cluster areas of new transport infrastructure, cost overruns and evidence study are relatively bigger, which means that these studies received more attention.

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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.

Advanced Transport Planning and Development - TRL

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.

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  • 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.