Enhancing Winter Road Maintenance via Cloud Computing

doi:10.1109/MIC.2023.3265651

2023-07-01

PRE

Mohammad Hossein Tavakoli Dastjerdi

Zhen Liu *

Behnam Azmoon

Xiaoyong Yuan

DOI：10.1109/MIC.2023.3265651

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摘要

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.

Keyword:

Roads

Maintenance engineering

Snow

Meteorology

Temperature measurement

Decision making

Chemicals

Artificial intelligence

Convolutional neural networks

Autonomous systems