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Geospatial Analysis on Tehran Transportation Network

Mostafavi, Fahimeh | 2015

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 47254 (52)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Sarbazi-Azad, Hamid
  7. Abstract:
  8. A city is a complex system which consists of many interactive sub-systems. It is affected by diverse factors including governmental land policies, population growth, market behavior and transportation infrastructure. Traffic congestion is one of the most critical challenges of transport infrastructure which became everyday occurrence in the last decades. The traffic planners and traffic engineers have faced real obstacles due to the growth of motorization and the resulted external consequences. Furthermore the capacity of the traffic networks saturates during rush hours. Generally, network transport performance can be improved in two ways, optimizing network structure (Hard Strategy) or designing a routing strategy (Soft Strategy). In spite of all previous works done to solve the problem of traffic congestion, today’s demand cannot be always suitably met. To overcome the above mentioned challenges, this thesis relies on modeling and analyzing the transportation network of Tehran,the capital city of Iran. Various centrality measures such as Betweenness Centrality, Closeness Centrality, Degree Centrality, Straightness Centrality and Eigenvector Centrality have been evaluated here, by using Python simulator in order to analyze Tehran transportation network. The basic geospatial analyzing tool, used in this thesis, is the ArcGIS modeler. In this line, we also investigate the topological existing patterns of Tehran transportation network by using spatial statistics tools, provided in ArcGIS modeler. Furthermore, we propose the Subgraph Combination Prediction on Betweenness Centrality (SCPB) Algorithm to predict the betweenness centrality of a combined network based onits two primary graphs, using Graph theory and betweenness centrality algorithms. Working on real data, we also used ArcGIS modeler to clean the data and extract the obligatory information as well as presenting an applicable platform for further research
  9. Keywords:
  10. Traffic Congestion ; Graph Theory ; Road Transport ; Urban Transportation Network ; Tehran Transportation Network ; Urban Development

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