Pavement condition analysis via vehicle mounted accelerometer data

Road anomalies and irregularities such as potholes and uneven surfaces are a common hazard in South East Asia and developing countries. Such hazards pose a threat to the safety and well-being of both civilians going about their daily routine and tourists who are exploring the city. Since bicycles and rickshaws are still a common mode of transport used by both civilians and tourists in many South East Asian countries, it is essential to improve the overall quality and smoothness of pavements which are traversed by these vehicles. Management of international sporting and recreational events also require satisfactory road and pavement conditions. Before pavement conditions can be improved, it is an essential prerequisite to obtain comprehensive information about road irregularities such as the location and also severity of the road irregularity (depth of the potholes and height of bumps). In this paper, we propose a method for obtaining mathematical models that represent the overall condition of the pavements that are part of a commonly traversed cycling route. Such mathematical models and coefficients can be stored in the cloud of an Internet of Things (IOT) data analytics systems subsequently leading to identification of regions with severe road irregularities