Corridor-Wide Surveillance Using Unmanned Aircraft Systems

Zhang, Yu; Kourtellis, Achilleas; Post, Joseph; Tang, Hualong; Yelchuri, Keerthana; Porter, Brian

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Corridor-Wide Surveillance Using Unmanned Aircraft Systems

2023-12-01
By Zhang, Yu ; Kourtellis, Achilleas ; Post, Joseph

File Language:

English

Details

Creators:

Zhang, Yu ; Kourtellis, Achilleas ; Post, Joseph ; Tang, Hualong ; Yelchuri, Keerthana ; Porter, Brian
Corporate Creators:

University of South Florida. National Institute for Congestion Reduction
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:
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report September 2021 - August 2022
Corporate Publisher:

University of South Florida. National Institute for Congestion Reduction
Abstract:

In the second phase of the project, the research team continued traffic data collection with unmanned aerial systems (UAS) and dual sensors (RGB and thermal cameras) for roadways under normal operational conditions and during incident clearance. The research team conducted a thorough literature review of incident detection methods. Based on the review outcomes, the research team extracted traffic features from learning the video data and trained several learning models for identifying non-congestion conditions caused by incidents. This method was tested on a two-minute video with data captured by a drone at a location at which traffic was passing through an incident site. The results show that some machine learning models (support vector machine, K nearest neighbor, and random forest) performed very well in F1 scoring.
Format:

PDF
Funding:

69A3551947136, 79075-14, 79075-15
Download URL:

https://rosap.ntl.bts.gov/view/dot/89333/dot_89333_DS1.pdf
File Type:

[PDF - 2.25 MB]
Collection(s):

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

urn:sha-512:abc9b752d07362a9e76c2ffb634ee077420719f49b03ada75142985927e8915f9cdc9dc1e93b5844c83ea79094065bbb847e6973332fe1bd320177abd74711bb

File Language:

English

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