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Filtering Duplicated Location in Tracking Traffic Data

机译:过滤在跟踪流量数据中的重复位置

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Intelligent Transportation System (ITS) has been becoming an integral part of life in city, giving at the result of great impact by utilizing the communication, computing and sensor technologies to solve the relating problem of transportation such as traffic congestions. Traffic congestion is used to curse to citizen and an ongoing problem in almost urban areas. The purpose of this paper is mainly to provide the data without noises as much as possible to Traffic Detection System (TDS) based on GPS_enable Mobile phone. The system is constructed into two parts: Client side (Mobile device) and Cloud Backend Server. In this work, the process of transportation mode filtering is carried out on the Client side applying Moving Average Filtering method and then the filtering location duplicated data continues to work out on Server side based on the Client's result. In order to solve the server side issue, the distance based clustering method, OPTICS: Ordering Point To Identify the Clustering Structure, is mainly utilized. Afterward, the accuracy of the system is measured by Purity, F-measurement and Entropy method. To execute closeness between GPS points, the distance between them is measured by using Haversine Formula.
机译:智能交通系统(其)一直成为城市生活中不可或缺的一部分,通过利用通信,计算和传感器技术来解决诸如交通拥堵等交通问题的巨大影响而产生了很大的影响。交通拥堵用于诅咒到公民以及几乎城市地区的持续问题。本文的目的主要是基于GPS_ENABLE手机的交通检测系统(TDS)提供数据,而不是声音的数据。该系统被构造成两部分:客户端(移动设备)和云端服务器。在这项工作中,在客户端应用移动平均滤波方法上执行运输模式过滤过程,然后基于客户端的结果,过滤位置复制数据继续在服务器端解决。为了解决服务器侧问题,基于距离的聚类方法,光学:排序点来标识聚类结构,主要利用。之后,通过纯度,F测量和熵方法测量系统的准确性。为了在GPS点之间执行密闭,通过使用Haversine公式测量它们之间的距离。

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