Riding quality evaluation through mobile crowd sensing

Public transport plays an importation role in our daily life. The information related to passengers satisfaction is very beneficial for optimizing the transportation service. This paper investigates an application of mobile crowd sensing to detect and analyze the riding quality of public transport vehicles. The lightweight system leverages sensors equipped on participants' smartphones to collect surrounding information. By analyzing the uploaded data at a server, we are able to estimate both aggressive driving behaviors and environment contexts. Series of data processing methods are exploited to overcome the affection of body movement and road condition, and crowd sourcing is applied to improve the robustness of the results. We have tested this system in 3 different transportation in 3 cities. The results indicate that the system can provide sufficient accuracy (up to 91% with 7 phones) to identify dozens of riding-comfort metrics.