Edge Computing-Enabled Road Condition Monitoring: System Development and Evaluation

• URL: http://arxiv.org/abs/2310.05321v1
• Date: Mon, 9 Oct 2023 00:55:41 GMT
• Title: Edge Computing-Enabled Road Condition Monitoring: System Development and Evaluation
• Authors: Abdulateef Daud, Mark Amo-Boateng, Neema Jakisa Owor, Armstrong Aboah, Yaw Adu-Gyamfi
• Abstract summary: Real-time pavement condition monitoring provides highway agencies with timely and accurate information. Existing technologies rely heavily on manual data processing, are expensive and therefore, difficult to scale for frequent, networklevel pavement condition monitoring. This 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.
• Score: 5.296678854362804
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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