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Enhancing Winter Road Maintenance via Cloud Computing
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PRE
AI
DOI：10.1109/MIC.2023.3265651.png)
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Abstract
En 中文
In this article, we develop an AI-enhanced cloud computing framework to enable the autonomous decision-making quality and precision of winter road operations for connected living, namely, the Smart Maintenance Decision Support System (SmartMDSS). With support from the Federal Highway Administration and Michigan Department of Transportation, we are among the first to develop AI-enabled tools for practical winter road maintenance decision making. On the front end, SmartMDSS provides a user-friendly graphical interface showing all of the valuable data and winter operations for specific points on the road and sends necessary warnings and notifications. On the back end, SmartMDSS extracts and analyzes data and makes winter road maintenance decisions. A convolutional neural network has been successfully employed to identify the snow coverage on the road surface. Decision-making algorithms were proposed and implemented to support the road engineers and operators for real-time winter maintenance operations.
Keywords:
Roads
Maintenance engineering
Snow
Meteorology
Temperature measurement
Decision making
Chemicals
Artificial intelligence
Convolutional neural networks
Autonomous systems
Journal
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4.4
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2.0K
Citations:
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