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Issue title: Applications of intelligent & fuzzy theory in engineering technologies and applied science
Guest editors: Álvaro Rocha
Article type: Research Article
Authors: Guan, Shuqia | Yun, Juna | Zhang, Qinkangb; * | Aurora, Annac
Affiliations: [a] School of Management, Wuhan University of Technology, Wuhan Hubei, China | [b] Wuhan University of Technology Huaxia College, Wuhan Hubei, China | [c] Business Management, University of Navarra, Pamplona, Spain
Correspondence: [*] Corresponding author. Qinkang Zhang, Wuhan University of Technology Huaxia College, Wuhan Hubei 430070, China. Tel.: +86 150 7104 6210; E-mail: [email protected].
Abstract: Intelligent transport systems (ITS) have powerful technologies that work together to improve transportation performance. The use of ITS provide effective solutions for intelligent decision making in traffic engineering, reducing traffic congestion, and increasing traffic safety. This study explains the working mechanisms of how ITS improve the operating efficiency of urban traffic and describes the development of a scientific evaluation model that can calculate the net contribution from, and assess the operational performance of, ITS and, in addition, remove interference from other factors. From correlation analysis and Granger causality tests, four key indicators are presented. In addition, a combination of difference-in-differences and matching methods solves the problem of sample-selection deviation and endogenousness perfectly, and are useful in calculating the net contribution of ITS. According to the results, the contribution rate of ITS to the urban traffic operating efficiency in Guangzhou, China has been 6.32%. This value quantitatively demonstrates that ITS have a positive effect on urban traffic operating efficiency, and proves the effectiveness of ITS contributions to intelligent decision-making in traffic engineering.
Keywords: Intelligent transportation system, intelligent decision, difference-in-differences, matching, contribution rate
DOI: 10.3233/JIFS-169061
Journal: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 4, pp. 2213-2220, 2016
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