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Using Big Data Analytics and Visualization to Create IoT-enabled Science Park Smart Governance Platform

机译:使用大数据分析和可视化创建基于物联网的科学园智能治理平台

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Science parks are important industrial clusters in the development of Taiwan's technology industry. Nearly 280.000 employees commute to the science parks on a daily basis. Thus, traffic congestion not only wastes the time of and creates extra fuel costs for the road users, but also leads the vehicles to release more pollutants in the environment. With the rise of Internet of Things technology, the science park administration has established multiple IoT-enabled systems since 2017, in order to collect data and monitor traffic flow and air quality in a more accurate manner. However, it is still a question that how emerging technology should be applied to provide accurate and timely information to assist administration to observe the historical trends and current status of traffic congestion and air quality, so as to formulate traffic control and air pollution prevention strategies. To that end, there are two purposes in this paper: (1) to establish a Science Park Smart Governance Platform to collect data collected from the IoT devices, and (2) to design and develop data visualization functions for the smart management of traffic and air quality. The research garners three results from the smart traffic monitoring service: (1) helping administration check the traffic status in real time, in order to facilitate traffic control; (2) presenting the historical trends of traffic flow on a typical day, month, and year, and allowing administration to understand in what intersections and at what periods traffic congestion is more prone to take place; (3) creating a predictive model of how traffic flow and weather can influence the traffic volume interactively to predict traffic flow for every intersection in the following 10 min, so that administration can operate the traffic lights in order to reduce traffic congestion. Besides the aforementioned results, three other results from the smart air quality monitoring service are presented in the study: (1) allowing administration to monitor real-time air quality status in various areas of the science parks; (2) presenting a historical trend of air quality, and allowing administration to understand in what month/time air pollution is occurring; (3) when the concentration of certain air pollutant exceeds a particular threshold, the smart environmental monitoring Chabot service will push warning messages to the managers.
机译:科技园是台湾科技产业发展的重要产业群。每天有近280.000名员工上下班到科学园。因此,交通拥堵不仅浪费时间并为道路使用者增加了额外的燃料成本,而且还导致车辆在环境中释放更多的污染物。随着物联网技术的兴起,自2017年以来,科学园管理局已建立了多个启用IoT的系统,以便以更准确的方式收集数据并监控交通流量和空气质量。但是,如何应用新兴技术提供准确及时的信息,以协助管理部门观察交通拥堵和空气质量的历史趋势和现状,从而制定交通控制和防止空气污染的策略,仍然是一个问题。为此,本文有两个目的:(1)建立一个科学园智能治理平台,以收集从物联网设备收集的数据;(2)设计和开发数据可视化功能,用于交通和交通的智能管理。空气质量。该研究从智能交通监控服务中获得了三个结果:(1)帮助行政部门实时检查交通状况,以促进交通控制。 (2)呈现典型日,月和年的交通流量的历史趋势,并让主管部门了解在哪个交叉路口和什么时间更容易发生交通拥堵; (3)建立交通流量和天气如何交互影响交通量的预测模型,以预测接下来10分钟内每个路口的交通流量,以便管理部门可以操作交通信号灯以减少交通拥堵。除上述结果外,研究还提供了智能空气质量监测服务的其他三个结果:(1)允许管理部门监视科学园区各个区域的实时空气质量状况; (2)呈现空气质量的历史趋势,并让主管部门了解在什么月份/时间发生空气污染; (3)当某些空气污染物的浓度超过特定阈值时,智能环境监控Chabot服务将向管理人员推送警告消息。

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