Toward Crowdsourcing-Based Road Pavement Monitoring by Mobile Sensing Technologies

In crowdsourcing applications, the quality of the crowdsourced data is decisive to the success of subsequent system-level mining processes. We proposed a smartphone probe car (SPC) system to monitor road pavement. An SPC is essentially an ordinary vehicle with a mounted smartphone that runs sensing programs to objectively assess bumping caused by road anomalies such as potholes and bumps. The proposed system has several features. First, to allow dynamic forming of SPCs, we develop a signal processing heuristic for the extraction of the vertical acceleration components from the accelerometer readings (upon which bumping detection and road surface anomaly assessment rely). By these means, the proposed system provides a driver-friendly environment, requiring neither complicated installation nor driverassisted training processes, and thus is possible to achieve hassle-free mass deployment such that drivers would be willing to participate in crowdsourcing. Second, based on the underdamped oscillation model, we propose a road anomaly indexing heuristic that is representable for road anomalies rather than vehicle conditions. This will later facilitate the system-level data mining processes in the servers. Third, a prototype SPC system was implemented and extensive field tests were undertaken to verify the performance of our system framework. Furthermore, we experimentally adopted a DENCLUE-like algorithm to mine road anomaly information from reported events to demonstrate any potential benefit from future investigation of data mining process at the system level. We believe the research works introduced in this paper consist the first step toward building an "ecosystem" of SPC-based crowdsourcing traffic and road monitoring applications.