Learning Roadway Surface Disruption Patterns Using the Bag of Words Representation

摘要

Accurately classifying roadway surface disruptions (RSDs) plays a crucial role to enhance quality transportation and road safety. To this end, smartphones are becoming an ad hoc tool to collect road data, while the user is at the steering wheel. In this paper, for the first time, sensed data are represented with a novel technique inspired in the bag of words representation. New results suggest that segments of accelerometer readings play a key role to characterize different classes of events, boosting classification performance. A novel data collection process was conducted in real-life environments, where the smartphones were freely placed at five user-surveyed locations, within a fleet of cars and trucks. To the best of our knowledge, this is the largest and most heterogenous data set for RSDs, and we make it publicly available. We approach the problem of identifying RSDs as one of supervised learning, where we contrast representative classifiers, most of them not previously reported. We exhaustively evaluated the performance of all classifiers in six data sets, most of them resembling actual data sets used in similar projects. We found that in all cases, the best classifier outperforms the best results reported so far. The proposed methodology was extensively evaluated through a sensitivity analysis to determine the relevance of the parameters. Experimental results reveal that the representation technique boosts considerably the classification performance when compared with the state of the art solutions, reducing in one order of magnitude the false-positivesegatives rate and surpassing the classification accuracy for about 10% in a multiclass data set.