AI-Enabled Vision System for Intersection Analytics [Research Summary]

Adu-Gyamfi, Yaw; Schulte, Brent

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AI-Enabled Vision System for Intersection Analytics [Research Summary]

2025-09-01
By Adu-Gyamfi, Yaw ; Schulte, Brent

File Language:

English

Details

Creators:

Adu-Gyamfi, Yaw ; Schulte, Brent
Corporate Creators:

University of Missouri--Columbia
Corporate Contributors:

Missouri. Department of Transportation. Construction and Materials Division
Subject/TRT Terms:

Artificial Intelligence Automation Data Collection Intersections Laser Radar Metrics (Quantitative Assessment) Performance Physical Distribution Traffic Counting Trajectory
Publication/ Report Number:

TR202506
Resource Type:

Brief
Geographical Coverage:

United States
Edition:

Research Summary
Corporate Publisher:

Missouri. Department of Transportation. Construction and Materials Division
Abstract:

This project is designed to overcome the challenges faced by today’s traffic management systems by: 1. Developing a fully automated vision system architecture to generate Turning Movement Counts (TMCs) eliminating the need for manual directional inputs. 2. Integrating TMCs with SPaT data to generate key intersection performance metrics including delayed movements, red-light restricted views reduced counting accuracy for certain movements.
Format:

PDF
Collection(s):

US Transportation Collection
Main Document Checksum:

urn:sha-512:ffd217c317518d5b3aa0381a2b805b74ac39037be29ef05206461757c481286b2df921a69d2fa28e409714ebc264f4658915fda6d8607a05dde334b37065a155
Download URL:

https://rosap.ntl.bts.gov/view/dot/86976/dot_86976_DS1.pdf
File Type:

[PDF - 436.36 KB ]

File Language:

English

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