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Issue title: Special Section: Iteration, Dynamics and Nonlinearity
Guest editors: Manuel Fernández-Martínez and Juan L.G. Guirao
Article type: Research Article
Authors: Li, Xina; * | Hsiao, J.b
Affiliations: [a] Department of Information Engineering, Zhengzhou Institute of Economic and Trade Management, Zhengzhou, China | [b] Department of Industrial Engineering and Operations Research, Columbia University, New York, NY, USA
Correspondence: [*] Corresponding author. Xin Li, Department of Information Engineering, Zhengzhou Institute of Economic and Trade Management, Zhengzhou 451191, China. E-mail: lx_network@163.com.
Abstract: The current traffic evacuation path control system has high risk coefficient and path congestion, and low efficiency and system error coefficient. For this problem, a fuzzy control system of traffic evacuation path based on genetic method is proposed and designed in this paper. The data server, geographic information server, computing server, and application server are used to construct the system framework. The logical structure is divided into data source layer, data access layer, scheduling layer, computing model layer, and application interface layer. The function module is mainly composed of static data management module, emergency management module, dynamic data interface module, dynamic traffic assignment module, guidance information release module, and user management module. The system hardware is designed by using the logical structure in combination with the function module. In the system software, the coordinator-operator mode is introduced into the real-time computing operation mechanism. The interaction of the coordinator and the operator is to implement the user specified operational function. Traffic data is forecast by autoregressive model. It is substituted into the objective function of intelligent traffic evacuation and the genetic method is used to solve the objective function. At last, fuzzy control result of optimal traffic evacuation path is obtained. Experimental results show that the average risk coefficient in the evacuation process is about 0.27, the average time consuming is 0.3 h, and the congestion of the evacuation path is relatively low, so the fault tolerance coefficient of the system can be controlled within a reasonable range. The system has a good overall operation effect and is feasible.
Keywords: Big data, intelligent transportation, evacuation path, fuzzy control, system
DOI: 10.3233/JIFS-169741
Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 4, pp. 4205-4213, 2018
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