Analyzing Riding Activities on the World's Longest Continuous Cycling Path Using Non-Intrusive IoT Sensors

F. Filali, Fatima Tayeb, Hamadi Chihaoui
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引用次数: 1

Abstract

On February 2020, Qatar entered the Guinness World Record by opening the world's longest continuous cycling track developed by the Public Works Authority - Ashghal. Gaining insights of the usage of the so called Olympic Cycling Track (OCT) such as biker count, travel time, speed, riding periodicity, and location origin, allows for the development of better cycling user experience, operational planning and maintenance. This paper attempts to conduct this analysis based on data collected from WaveTraf™ a sensing system that anonymously detects and tracks the movement of Bluetooth and WiFi-enabled devices. Data from four WaveTraf IoT sensors, deployed along the track, is cleaned, prepossessed and analysed to reveal riding patterns in the OCT track. An effective data cleaning technique was applied to detect and clean the noise in the data caused by detected devices from roads close of the OCT track. Analysis results demonstrate clear seasonality and trend in the riding pattern which was proven to be associated to the weather conditions as well as the normal work schedules.
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使用非侵入式物联网传感器分析世界上最长的连续自行车道上的骑行活动
2020年2月,卡塔尔通过开放由公共工程局开发的世界上最长的连续自行车道- Ashghal,进入了吉尼斯世界纪录。了解所谓的奥林匹克自行车道(OCT)的使用情况,如骑自行车的人数、旅行时间、速度、骑行周期和位置来源,可以开发更好的自行车用户体验、运营规划和维护。本文试图根据从WaveTraf™收集的数据进行分析,WaveTraf™是一种匿名检测和跟踪蓝牙和wifi设备运动的传感系统。沿着轨道部署的四个WaveTraf物联网传感器的数据被清理、预处理和分析,以揭示OCT轨道上的骑行模式。采用了一种有效的数据清洗技术来检测和清除OCT轨道附近道路上被检测设备引起的数据噪声。分析结果表明,骑乘模式具有明显的季节性和趋势,这被证明与天气条件和正常的工作时间表有关。
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