However, the proposed network weight matrices substitute for traditional spatial weight matrices and exhibit the capability to overcome these deficiencies. Not only do they overlook the competitive nature of traffic links, but they also ignore the role of network topology and demand configuration in measuring the spatial dependence between traffic links. The results of the analysis lead to a conclusion that traditional spatial weight matrices are unable to capture the realistic spatial dependency between traffic links in a network. The network weight matrices are tested in congested and uncongested traffic conditions in both simulation-based and real-world environments. Their elements are a function not simply of proximity, but of network topology, network structure, and demand configuration. The network weight matrices stem from the concepts of betweenness centrality and vulnerability in network science. More precisely, it introduces distinctive network weight matrices to extract the existing spatial dependency between traffic links.
This dissertation introduces concepts, theories, and methods dealing with network econometrics to gain a deeper understanding of how the components interact in a complex network.
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