通过确定拥堵地点引出最佳方法

Mohammad Alodat, I. Abdullah
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引用次数: 0

摘要

在本文中,利用基础设施,利用有线和无线通信技术,帮助驾驶员找到不包含任何障碍的方法,并以评估道路的速度达到目标。采用模糊逻辑、神经网络和遗传算法相结合的融合或一夫多妻技术提取切割节点(CN)。我们使用Cut-Node (CN)来识别障碍物并避开它们,例如发生在照明、天气条件、事故、拥堵和道路维护中的变化。Cut-Node (CN)有助于了解周围环境,支持安全驾驶,增加流量和到达指定目标(终点)的最快路径。本文提出了三种提取Cut-Node (CN)的模型:1)直觉模糊集(IFS)。2)直觉模糊集数据库表示不精确的数据。3)直觉模糊神经网络遗传算法(IFNN-GA)。
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Elicit the Best Ways through Identify Congestion Places
In this paper the exploitation of infrastructure and take advantage of communication techniques wired and wireless to help drivers in finding ways does not contain any obstacles and reach to the target with commitment in speed of assessed road. We use Fusion or Polygamy Technology with Fuzzy Logic, Neural Network and Genetic Algorithm for extract Cut-Node (CN). We use Cut-Node (CN) in order to identify obstacles and avoid them such as changes that occur in the lighting, weather conditions, accidents, congestion and maintenance of roads. Cut-Node (CN) useful in understanding the surrounding environment, supporting safe driving, increases the flow and fastest path to arrive at the designated target (end point). Three models have been proposed to extract Cut-Node (CN) as follows: 1) Intuitionistic Fuzzy Set (IFS). 2) The Intuitionistic Fuzzy Set Data Base is representing imprecise data. 3) Intuitionistic Fuzzy Neural Network with Genetic Algorithm (IFNN-GA).
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Management Dimension for Assessing Organizations' Readiness toward Business Intelligence Systems Multi-user Searchable Encryption with Fine-Grained Access Control without Key Sharing Mobile Online Services and Measuring User Satisfaction: Study of Kuwait Elicit the Best Ways through Identify Congestion Places Design a New Proposed Route Optimization Scheme Based NEMO-Centric MANEMO (NCM)
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