Road pavement condition diagnostics using smartphone-based data crowdsourcing in smart cities

The purpose of the paper is to analyse the effectiveness of a solution known as road condition tool (RCT) based on data crowdsourcing from smartphones users in the transport system. The tool developed by the author of the paper, enabling identification and assessment of road pavement defects by analysing the dynamics of vehicle motion in the road network. Transport system users equipped with a smartphone with the RCT mobile application on board record data of linear accelerations, speed, and vehicle location, and then, without any intervention, send them to the RCT server database in an aggregated form. The aggregated data are processed in the combined time and location criterion, and the road pavement condition assessment index is estimated for fixed 10 m long measuring sections. The measuring sections correspond to the sections of roads defined in the pavement management systems (PMS) used by municipal road infrastructure administration bodies. Both the research in question and the results obtained by the method proposed for purposes of the road pavement condition assessment were compared with a set of reference data of the road infrastructure administration body which conducted surveys using highly specialised measuring equipment. The results of this comparison, performed using binary classifiers, confirm the potential RCT solution proposed by the author. This solution makes it possible to global monitor the road infrastructure condition on a continuous basis via numerous users of the transport system, which guarantees that such an assessment is kept up to date.