Studying the Use of Low-Cost Sensing Devices to Report Roadway Pavement Conditions

Abdallah, Moatassem; Clevenger, Caroline M.; Monghasemi, Shahryar

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Studying the Use of Low-Cost Sensing Devices to Report Roadway Pavement Conditions

2024-09-01
By Abdallah, Moatassem ; Clevenger, Caroline M. ; Monghasemi, Shahryar

File Language:

English

Details

Creators:

Abdallah, Moatassem ; Clevenger, Caroline M. ; Monghasemi, Shahryar
Corporate Creators:

University of Colorado at Denver ; Mountain-Plains Consortium ; North Dakota State University
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:
Publication/ Report Number:

MPC-612
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report
Corporate Publisher:

Mountain-Plains Consortium
Abstract:

This report investigates the application of low-cost sensing technologies, including GPS, accelerometers, and smartphones, to monitor roadway pavement conditions in real time. By leveraging widely available sensors embedded in vehicles, this research demonstrates how machine learning models can detect and classify road anomalies, such as cracks and potholes, significantly improving road safety and reducing operational costs. The study also presents a mixed integer linear programming (MILP) model to optimize maintenance and repair (M&R) activities under budget constraints. These models help transportation agencies prioritize road repairs, ensure efficient resource allocation, and minimize traffic disruptions. By adopting low-cost sensor-based approaches, municipalities can move toward more proactive, data-driven maintenance strategies, ultimately improving road network longevity and user satisfaction.
Format:

PDF
Download URL:

https://rosap.ntl.bts.gov/view/dot/78991/dot_78991_DS1.pdf
File Type:

[PDF - 2.81 MB]
Collection(s):

Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
Main Document Checksum:

urn:sha-512:b658c80cf71359eebdc825b713ad2b422b04671945bcddf4af6588b5bd1c6a0bae84cb6615643f7155faa5fbf076fcc5366766374accacee285273404c12c8cc

File Language:

English

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