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      Pavement network reliability: a dual-network based on fuzzy set methodology

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          Abstract

          Pavement Management System (PMS) is used to conduct efficient pavement management. Since the pavement network is generally large and complex in scale, the reliability of the pavement network cannot be easily determined by applying conventional serial or parallel reliability models, or even a combination of the two. The objective of this paper is to propose a novel model by employing fuzzy set theory and dual-network method to calculate the relative importance of each pavement route and estimate the reliability of a pavement network. A fuzzy condition model is developed to describe the condition state of pavement management segments, pavement links, pavement routes, and eventually the whole network. The spatial weight of each pavement route is represented by the node degree of the corresponding dual-network. A case study is conducted by using the pavement network of Travis County, Texas, to illustrate the applicability of the proposed methodology. The results show that the model is able to calculate the relative importance of each route and determine the reliability of the given pavement network. This paper could help transportation agencies obtain more insight information on relative importance of each roadway and make more effective decisions on pavement network maintenance with a limited budget.

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          Most cited references 27

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          A Fuzzy-Set-Theoretic Interpretation of Linguistic Hedges

           L. A. Zadeh (1972)
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            Dual graph representation of transport networks

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              Reliability analysis of slopes using fuzzy sets theory

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                Author and article information

                Contributors
                Journal
                IET-ITS
                IET Intelligent Transport Systems
                IET Intell. Transp. Syst.
                The Institution of Engineering and Technology
                1751-956X
                1751-9578
                29 October 2018
                28 November 2018
                March 2019
                : 13
                : 3
                : 475-484
                Affiliations
                [1 ] Department of Traffic and Transportation, Shijiazhuang Tiedao University , 17 N 2nd Ring Rd E, Qiao Dong District, Shijiazhuang, Hebei Province, 050000, People's Republic of China
                [2 ] Center for Transportation Research, The University of Texas , 3925 West Braker Lane, Stop D9300, Austin, Texas, 78759, USA
                [3 ] Department of Civil, Architectural & Environmental Engineering, The University of Texas , 1 University Station C1761, Austin, Texas, 78712-0278, USA
                Article
                IET-ITS.2018.5191 ITS.2018.5191.R1
                10.1049/iet-its.2018.5191

                This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License ( http://creativecommons.org/licenses/by-nc-nd/3.0/)

                Page count
                Pages: 0
                Product
                Categories
                Special Issue: Big Data Analytics and Artificial Intelligence (AI) Applications for Smart Transportation - Selected Papers from World Transportation Congress (WTC) 2018

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