Parseh Designers Transportation Research Institute Quarterly Journal of Transportation Engineering 2008-6598 Articles in Press 2020 11 15 Expanding the eigenvector centrality for multi-layer graphs and its application in managing traffic and urban infrastructure Expanding the eigenvector centrality for multi-layer graphs and its application in managing traffic and urban infrastructure 119374 10.22119/jte.2020.119374 FA Zohreh Azimifar School of electronic an computer engineering, Shiraz university, Iran, Shiraz Mohammadamin Soltani Sarvestani Department of electronic and computer engineering, Shiraz university, Shiraz, IRan Ali Akbar Safavi Department of electronic and computer engineering, Shiraz university, Shiraz, Iran Ghasem Habibagahi Department of Civil engineering, Shiraz university, Shiraz, IRan Journal Article 2020 03 13 The large number of parameters in urban networks, makes them one of the most complicated problem in the real life and affects traffic and transportation engineering and urban design. The large number of parameters forces problem solvers to ignore lots of them. Eigenvector centrality measurement is a powerful tool that provides many valuable information from a large network for urban planners and managers, but has two drawbacks; 1) the eigenvector centrality measurement is not able to calculate the centrality values for a network with<br />more than one parameter (namely multi-layer network), 2) It does not return true values for some features. In this paper, we address to solve both drawbacks of eigenvector centrality measurement to use in urban network related problems. Two validation factors to evaluate the proposed method is innovated; similar layers and similar nodes validations. The proposed method passes two validation factors. Two case studies are solved to show the application of proposed method in urban related problems include traffic light phasing and detecting the central part of Shiraz city by two features; like length and link width. Experimental results and case studies, show the efficiency and reliability of proposed method. The large number of parameters in urban networks, makes them one of the most complicated problem in the real life and affects traffic and transportation engineering and urban design. The large number of parameters forces problem solvers to ignore lots of them. Eigenvector centrality measurement is a powerful tool that provides many valuable information from a large network for urban planners and managers, but has two drawbacks; 1) the eigenvector centrality measurement is not able to calculate the centrality values for a network with<br />more than one parameter (namely multi-layer network), 2) It does not return true values for some features. In this paper, we address to solve both drawbacks of eigenvector centrality measurement to use in urban network related problems. Two validation factors to evaluate the proposed method is innovated; similar layers and similar nodes validations. The proposed method passes two validation factors. Two case studies are solved to show the application of proposed method in urban related problems include traffic light phasing and detecting the central part of Shiraz city by two features; like length and link width. Experimental results and case studies, show the efficiency and reliability of proposed method.