Ermagun, Alireza2016-12-192016-12-192016-10https://hdl.handle.net/11299/183378University of Minnesota Ph.D. dissertation. October 2016. Major: Civil Engineering. Advisor: David Levinson. 1 computer file (PDF); xii, 138 pages.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. More precisely, it introduces distinctive network weight matrices to extract the existing spatial dependency between traffic links. The network weight matrices stem from the concepts of betweenness centrality and vulnerability in network science. Their elements are a function not simply of proximity, but of network topology, network structure, and demand configuration. The network weight matrices are tested in congested and uncongested traffic conditions in both simulation-based and real-world environments. 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. 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. However, the proposed network weight matrices substitute for traditional spatial weight matrices and exhibit the capability to overcome these deficiencies. The network weight matrices are theoretically defensible in account of acknowledging traffic theory. They capture the competitive and complementary nature of links and embed additional network dynamics such as cost of links and demand configuration. Building on real-world data analysis, the results contribute to the conclusion that in a network comprising links in parallel and series, both negative and positive correlations show up between links. The strength of the correlation varies by time-of-day and day-of-week. Strong negative correlations are observed in rush hours, when congestion affects travel behavior. This correlation occurs mostly in parallel links, and in far upstream links where travelers receive information about congestion (for instance from media, variable message signs, or personal observations of propagating shockwaves) and are able to switch to substitute paths. Irrespective of time-of-day and day-of-week, a strong positive correlation is observed between upstream and downstream sections. This correlation is stronger in uncongested regimes, as traffic flow passes through the consecutive links in a shorter time and there is no congestion effect to shift or stall traffic.enBetweenness centralityData detrendingNetwork weight matrixSpatial weight matrixTraffic flowVulnerabilityThesis or Dissertation