| Abstract |
With the rapidly development of transportation, real time road condition detection has become a major challenge that accompanies the development of transportation. Real time data collection and analysis become the crucial problem. Meanwhile, Crowdsensing technique helps to share data and extract information to measure, map, analyze, estimate or infer any processes of common interest from mobile devices. In order to solve the problem, this paper demonstrates a road condition detection approach based on crowdsensing. The embed acceleration sensor of mobile phone are applied to excellently detect the vibration situation. Thus, the acceleration status and GPS data can be collected from running vehicles, representing the road condition and traveling tracks. After data collection, learning algorithms are used to deduce the actual road condition. This paper proposes a double-clustering based approach to implement road environment monitoring based on crowdsensing. The first level clusters the accelerating feature matrix to find out the corresponding coordinate category, and then the second level clustering is performed on the GPS coordinates to deduce the actual road condition. Finally, the moderate results obtained from on-road experiments prove the correctness and efficiency of our approach. © 2019 IEEE. |
