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Road transportation is of critical importance for a nation, having profoundeffects in the economy, the health and life style of its people. With thegrowth of cities and populations come bigger demands for mobility and safety,creating new problems and magnifying those of the past. New tools are needed toface the challenge, to keep roads in good conditions, their users safe, andminimize the impact on the environment.  This dissertation is concerned with road quality assessment and aggressivedriving, two important problems in road transportation, approached in thecontext of Intelligent Transportation Systems by using Machine Learningtechniques to analyze acceleration time series acquired with smartphone-basedopportunistic sensing to automatically detect, classify, and characterizeevents of interest.  Two aspects of road quality assessment are addressed: the detection and thecharacterization of road anomalies. For the first, the most widely cited worksin the literature are compared and proposals capable of equal or betterperformance are presented, removing the reliance on threshold values andreducing the computational cost and dimensionality of previous proposals. Forthe second, new approaches for the estimation of pothole depth and thefunctional condition of speed reducers are showed. The new problem of potholedepth ranking is introduced, using a learning-to-rank approach to sortacceleration signals by the depth of the potholes that they reflect.  The classification of aggressive driving maneuvers is done with automaticfeature extraction, finding characteristically shaped subsequences in thesignals as more effective discriminants than conventional descriptorscalculated over time windows.  Finally, all the previously mentioned tasks are combined to produce a robustroad transport evaluation platform.

A Machine Learning Approach for Smartphone-based Sensing of Roads and Driving Style