Identifying Near-Misses (Including Red-Light Running) and Reducing Conflict Through D-FYA at Signalized Intersections Using LiDAR Sensors

Li, Pengfei (Taylor); Yang, Xianfeng (Terry); Zhu, Peter; Wang, Peirong (Slade); Sijan, Shrestha

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Identifying Near-Misses (Including Red-Light Running) and Reducing Conflict Through D-FYA at Signalized Intersections Using LiDAR Sensors

2025-08-01
By Li, Pengfei (Taylor) ; Yang, Xianfeng (Terry) ; Zhu, Peter ; ...

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English

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Creators:

Li, Pengfei (Taylor) ; Yang, Xianfeng (Terry) ; Zhu, Peter ; Wang, Peirong (Slade) ; Sijan, Shrestha
Corporate Creators:

University of Texas at Arlington ; University of Maryland ; University of Utah. Department of Civil and Environmental Engineering
Corporate Contributors:

Utah. Department of Transportation. Research and Innovation Division ; United States. Department of Transportation. Federal Highway Administration
Subject/TRT Terms:

Bandwidth (Traffic Signals) Behavior Cooperation Crash Sensors Intersections Laser Radar Near-misses Near Crashes Pedestrian Behaviors Pedestrians Red Light Running Traffic Signal Systems
Publication/ Report Number:

UT-25.22 ; 5H09244H
Resource Type:

Tech Report
Geographical Coverage:

United States ; Utah
Edition:

Final Report: Oct 2022 to December 2025
Corporate Publisher:

Utah. Dept. of Transportation. Division of Research
Abstract:

The primary research objective of this project is to demonstrate the potential capability of emerging LiDAR sensing technologies in identifying and mitigating traffic conflicts (i.e., near-misses) at signalized intersections. The latest LiDAR sensing technologies allow for tracking vehicles and pedestrians (assigning each a unique temporary ID for identification). This capability can be used to mitigate traffic crashes. In this research, the collaborating team between the University of Utah, the University of Texas at Arlington, and the University of Maryland installed the application software developed by UTA and conducted extensive tests in the field, such as the sensors’ latency to identify instantaneous traffic conflicts, the algorithm’s reliability and accuracy, and a demonstration of safety-centric traffic control algorithms The outcome of this research is expected to provide decision support for UDOT to determine its large-scale deployment plan.
Format:

PDF
Funding:

238285
Collection(s):

US Transportation Collection
Main Document Checksum:

urn:sha-512:5fcb8cd235f3481ff97b0cfd4c7d78b78005c3a069d567702018548533b4320be1f3af28919f4370daa3fcfc129734ff4e395ceddb76ed2ca2cfcb5d96f30ef1
Download URL:

https://rosap.ntl.bts.gov/view/dot/88398/dot_88398_DS1.pdf
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[PDF - 20.82 MB ]

File Language:

English

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