  Real-time pavement condition monitoring provides highway agencies with timely
and accurate information that could form the basis of pavement maintenance and
rehabilitation policies. Existing technologies rely heavily on manual data
processing, are expensive and therefore, difficult to scale for frequent,
networklevel pavement condition monitoring. Additionally, these systems require
sending large packets of data to the cloud which requires large storage space,
are computationally expensive to process, and results in high latency. The
current study proposes a solution that capitalizes on the widespread
availability of affordable Micro Electro-Mechanical System (MEMS) sensors, edge
computing and internet connection capabilities of microcontrollers, and
deployable machine learning (ML) models to (a) design an Internet of Things
(IoT)-enabled device that can be mounted on axles of vehicles to stream live
pavement condition data (b) reduce latency through on-device processing and
analytics of pavement condition sensor data before sending to the cloud
servers. In this study, three ML models including Random Forest, LightGBM and
XGBoost were trained to predict International Roughness Index (IRI) at every
0.1-mile segment. XGBoost had the highest accuracy with an RMSE and MAPE of
16.89in/mi and 20.3%, respectively. In terms of the ability to classify the IRI
of pavement segments based on ride quality according to MAP-21 criteria, our
proposed device achieved an average accuracy of 96.76% on I-70EB and 63.15% on
South Providence. Overall, our proposed device demonstrates significant
potential in providing real-time pavement condition data to State Highway
Agencies (SHA) and Department of Transportation (DOTs) with a satisfactory
level of accuracy.
