RoVi: Continuous transport infrastructure monitoring framework for preventive maintenance

Ground transport infrastructures require in-situ monitoring to evaluate their condition and deterioration and to design appropriate preventive maintenance strategies. Current monitoring practices provide accurate and detailed spatial measurements but often lack the required temporal resolution. This is because the large scale of these infrastructures and the expensive equipments required for monitoring activities do not allow running very frequent measurement campaigns. In this paper, we present RoVi, a novel smartphone-based framework for continuous monitoring of a number of health and condition indicators for variety of ground infrastructures and assets. These indicators include railroad track geometry features such as Cant, Twist, Curvature, and Alignment for different segment lengths as well as road and bike path roughness index (i.e., an equivalent to the International Roughness Index, the so called IRI). RoVi uses an optimized processing algorithm technique on data acquired by smartphones' inertial sensors and relies on sensing, processing power, and networking capabilities of smartphones carried by car/bike drivers and train passengers to provide real time space-time information for fine-grained monitoring of infrastructures. It utilizes the crowd sensing concept to fill in the gap between current sparse consecutive inspections. RoVi provides a reliable and accurate analytic tool for engineers and maintenance planners by offering them features and indicators they require for asset management and maintenance planning. We extract these features and indicators from noisy smartphone data utilizing adaptive signal processing techniques followed by feature calculations and geo-location visualization. Our fast data aggregation algorithm based on Delaunay triangulation updates profiles with new measurements arriving in real time from smartphones. By doing so, it tackles the notorious problem of smartphone GPS accuracy. Performance evaluation of our framework has been performed on measurements collected by smartphones and compared with the ground truth measurements collected by the highend measurement vehicles (i.e., ARAN for roads and UMF120 measurement train for railroads).