Improved Signalized Intersection Performance Using Computer Vision and Artificial Intelligence: Research Project Capsule [24–4SS]

Moomen, Milhan; Rupnow, Tyson; Codjoe, Julius; Louisiana Transportation Research Center

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Improved Signalized Intersection Performance Using Computer Vision and Artificial Intelligence: Research Project Capsule [24–4SS]

2024-01-01
By Moomen, Milhan ; Rupnow, Tyson ; Codjoe, Julius

[PDF-643.98 KB]

English

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Creators:

Moomen, Milhan ; Rupnow, Tyson ; Codjoe, Julius

Moomen, Milhan ; Rupnow, Tyson ; Codjoe, Julius Less -
Corporate Creators:

Louisiana Transportation Research Center
Corporate Contributors:

Louisiana. Dept. of Transportation and Development ; Louisiana State University

Louisiana. Dept. of Transportation and Development ; Louisiana State University Less -
Subject/TRT Terms:

[+]

Artificial Intelligence Computer Vision Drones Performance Measurement Signalized Intersections Traffic Surveillance Video
Publication/ Report Number:

24-4SS
Resource Type:

Brief
Geographical Coverage:

United States
Edition:

Research Project Capsule
Corporate Publisher:

Louisiana Transportation Research Center
Abstract:

The primary objectives of this research are to: • Assess the feasibility and accuracy of using computer vision technology for performance evaluation at signalized intersections. • Provide intersection video footage data captured by drones. • Use computer vision and artificial intelligence to automatically convert data from video recordings at selected intersections into trajectories of road users. • Use computer vision and artificial intelligence to count road users, and detect queuing and demand for each approach at selected intersections using drone footage. • Develop tools to facilitate DOTD traffic engineers in understanding road users’ behaviors, evaluating intersection performance measures, and assisting in determining effective measures for improving safety and efficiency at intersections.
Format:

PDF
Funding:

TT-Fed/TT-Reg-5
Collection(s):

US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:c073ff8ae606bcca0855635d592aa43bc89472e86f7d878b2e14c8c3755476a4
Download URL:

https://rosap.ntl.bts.gov/view/dot/73189/dot_73189_DS1.pdf
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Improved Signalized Intersection Performance Using Computer Vision and Artificial Intelligence: Research Project Capsule [24–4SS]

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