Traffic networks of major cities are becoming increasingly congested as the majority of people work in cities while living on the outskirts. Furthermore, in the Western world, the number of cars per capita increases every year. The consequence of this is a higher number of traffic jams, leading to longer travel times, increased fuel consumption, and worsened air quality. By analyzing traffic networks, we obtain key information about critical parts of the network, which can then be improved through urban planning. Traffic networks can be compared to metabolic networks, as both are complex dynamic systems with flow as a key parameter. The main objective of this thesis is to examine the application of approaches for analyzing metabolic networks in the analysis of traffic networks. We relied on Python packages OSMnx and COBRApy for this purpose. COBRApy is a software package primarily designed for modelling and analyzing metabolic networks, but due to the similarities between biological and traffic networks, we adapted it for traffic network analysis. We obtained traffic network data through the OSMnx package, which serves as an interface to OpenStreetMap data. In our traffic model, we used algorithms for the analysis of metabolic networks, offering the possibility of conducting an extensive range of analyses, such as evaluating traffic flow through the city, identifying key road segments, etc. This approach provides a fully automated framework for establishing and analyzing models of large traffic networks.
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