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Feasibility Study on Detection of Transportation Information Exploiting Twitter as a Sensor

机译:浅析推特作为传感器的运输信息的可行性研究

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The concept of a smart community has recently been attracting great attention as a means of utilizing energy effectively. One of the modules constituting the smart community is an intelligent transportation system, in which various sensors track movements of people and vehicles in real time to optimize migration pathways or means. Social media have the potential to serve as sensors, since people often post transportation information on such media. This paper presents a feasibility study on detecting information, focusing on train status information, by exploiting Twitter as a sensor. We dealt with two issues: (1) for the ambiguity of textual information expressed in tweets, we utilized heuristic rules in text manipulation, and (2) for the differences in the numbers of tweets among train lines, we optimized parameter values in statistical analysis for each train line. The experimental results show that the F-measure of detecting the information was more than 0.85 and the time taken to detect the information was less than 4 minutes. As a result we confirmed the high potential of detecting transportation information through Twitter.
机译:智能社区的概念最近一直吸引了极大的关注,因为有效利用能量的手段。构成智能社区的模块之一是智能运输系统,其中各种传感器实时跟踪人员和车辆的运动以优化迁移途径或手段。社交媒体有可能作为传感器作为传感器,因为人们经常在这种媒体上发布运输信息。本文通过利用Twitter作为传感器来介绍检测信息,专注于列车状态信息的可行性研究。我们处理了两个问题:(1)对于推文中表达的文本信息的歧义,我们在文本操纵中使用了启发式规则,(2)对于火车线之间推文数量的差异,我们优化了统计分析中的参数值每个火车线。实验结果表明,检测信息的F措施大于0.85,检测信息所需的时间少于4分钟。因此,我们通过Twitter确认了检测运输信息的高潜力。

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