Automated Real-Time Weather Detection System Using Artificial Intelligence

Ahmed, Mohamed M; Khan, Md Nasim; Mohamed, Ahmed; Das, Anik; Li, Lizhe; University of Wyoming. Department of Civil and Architectural Engineering; University of Cincinnati

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Automated Real-Time Weather Detection System Using Artificial Intelligence

2023-12-01
By Ahmed, Mohamed M ; Khan, Md Nasim ; Mohamed, Ahmed ; ...

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Details:

Creators:

Ahmed, Mohamed M ; Khan, Md Nasim ; Mohamed, Ahmed ; Das, Anik ; Li, Lizhe

Ahmed, Mohamed M ; Khan, Md Nasim ; Mohamed, Ahmed ; Das, Anik ; Li, Lizhe Less -
Corporate Creators:

University of Wyoming. Department of Civil and Architectural Engineering ; University of Cincinnati

University of Wyoming. Department of Civil and Architectural Engineering ; University of Cincinnati Less -
Contributors:

Garcia, Vince
Corporate Contributors:

Wyoming. Department of Transportation
Subject/TRT Terms:

[+]

AlexNet Closed Circuit Television Convolutional Neural Network Digital Cameras Machine Learning Neural Networks ResNet Roadside Webcams Snowplows Weather Conditions Winter Maintenance
Publication/ Report Number:

WY- 2402F
Resource Type:

Tech Report
Geographical Coverage:

Wyoming ; United States

Wyoming ; United States Less -
Edition:

Final Report, December 2023
Corporate Publisher:

Wyoming. Department of Transportation
Abstract:

Adverse weather conditions have long been recognized as a leading contributing factor to motor vehicle crashes, primarily due to their adverse effects on visibility and road surfaces. Providing drivers with real-time weather information is essential to ensure safe driving in adverse weather conditions. Nonetheless, the identification of road weather and surface conditions presents a substantial challenge, frequently demanding the deployment of costly weather stations and manual identification and verification processes. Many Departments of Transportations (DOTs) in the U.S., including the Wyoming Department of Transportation (WYDOT), have installed roadside webcams primarily for operational awareness. In this project, we leveraged the easily accessible data sources of webcam images and in-vehicle dash camera videos to develop a cost-effective automated weather and surface detection system. For road weather condition detection, our developed models encompass a range of weather and surface conditions, including dry, rainy, snowy, wet/slushy, blowing snow, and more. In the case of in-vehicle dash cameras, our models focus on detecting clear, rainy, snowy, and foggy weather conditions. We applied both machine learning and deep learning algorithms, utilizing multiple pre-trained Convolutional Neural Network (CNN) models. Our proposed study and pre-trained models provide more accurate and consistent real-time weather information that can benefit road users and transportation agencies.
Format:

PDF
Funding:

RS05220
Collection(s):

US Transportation Collection
Main Document Checksum:

[+]

urn:sha-512:b967becaa24a3aedf6656a1514578a0853d095722c9db97421fafc2a850f3bcd78abe70c2ec0d87fa42180ff80e84f35825f099f4883dcaa5803a0628b913e0d
Download URL:

https://rosap.ntl.bts.gov/view/dot/75476/dot_75476_DS1.pdf
File Type:

[PDF-6.84 MB]