Participatory sensing networks rely on gathering personal data from mobile devices to infer global knowledge. Participatory sensing has been used for real-time traffic monitoring, where the global traffic conditions are based on information provided by individual devices. However, fewer initiatives address asphalt quality conditions, which is an essential aspect of the route decision process. This article proposes Streetcheck, a framework to classify road surface quality through participatory sensing. Streetcheck gathers mobile devices’ sensors such as Global Positioning System (GPS) and accelerometer, as well as users’ ratings on road surface quality. A classification system aggregates the data, filters them, and extracts a set of features as input for supervised learning algorithms. Twenty volunteers carried out tests using Streetcheck on 1,200 km of urban roads of Minas Gerais (Brazil). Streetcheck reached up to 90.64% of accuracy on classifying road surface quality.

中文翻译：

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参与式感应框架对路面质量进行分类

参与式传感网络依靠从移动设备收集个人数据来推断全球知识。参与式感应已用于实时交通监控，其中全局交通状况基于各个设备提供的信息。但是，解决沥青质量条件的倡议较少，这是路线决策过程的重要方面。本文提出了Streetcheck，这是一种通过参与式感应对路面质量进行分类的框架。Streetcheck会收集移动设备的传感器（例如全球定位系统（GPS）和加速度计）以及用户对路面质量的评级。分类系统汇总数据，过滤数据并提取一组功能，作为监督学习算法的输入。20名志愿者使用Streetcheck在1进行了测试 米纳斯吉拉斯州（巴西）的城市道路200公里。在对路面质量进行分类时，Streetcheck的准确性高达90.64％。