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首页> 外文会议>International Conference on HCI in Mobility, Transport, and Automotive Systems >Monitoring Road Surface Conditions for Bicycles - Using Mobile Device Sensor Data from Crowd Sourcing
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Monitoring Road Surface Conditions for Bicycles - Using Mobile Device Sensor Data from Crowd Sourcing

机译：监测自行车的道路表面条件 - 使用来自人群采购的移动设备传感器数据

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This paper introduces an approach for monitoring cycleway conditions by collecting crowdsourced data from mobile devices. To collect the data, an application was developed and optimized to be used by many cyclists. The application uses acceleration and gyroscopic sensors to collect and upload road roughness data into a classification platform. A classification model classifies the monitored routes into three quality classes and synchronizes the results with the application. The methodology shows how to collect and classify road surface conditions of cycleways. By using the K-Nearest Neighbor machine learning algorithm as a classifier, we were able to achieve a forecast accuracy above 90% on average. We report on our experiences with classification accuracy of four different classifiers as well as the experimental evaluations of the system. The results support the potential development of a community portal that provides detected cycleway conditions from the up-to-date mobile crowdsensing application.
机译：本文介绍了一种通过从移动设备收集众群数据来监控周期条件的方法。要收集数据，开发并优化了许多骑自行车者使用的应用程序。应用程序使用加速和陀螺仪传感器将道路粗糙度数据收集和上传到分类平台。分类模型将被监视的路由分类为三个质量类，并将结果与​​应用程序同步。该方法显示了如何收集和分类自行车道的道路表面条件。通过使用K-最近邻的机器学习算法作为分类器，我们能够平均达到90％以上的预测精度。我们报告了我们的四种不同分类器的分类准确性的经验以及系统的实验评估。结果支持社区门户网站的潜在开发，该门户网站提供了从最新的移动众包应用程序提供检测到的周刊条件。

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