Evaluating traffic conflicts and congestion based on right-turning driving behaviour at uncontrolled heterogeneous T-intersection using evasive actions driven PET via UAV video analysis
Author(s)
Bhavsar, Yagnik M
Zaveri, Mazad S
Raval, Mehul S
Shukla, Pancham
Zaveri, Shaheriar B
Type
preprint
Abstract
Adherence to right-of-way (RoW) rules at uncontrolled T-intersections helps avoid accidents and alleviate congestion. In non-uniform traffic, right-turning behaviour can be characterised by distinct driving traits, such as non-compliance (failure to yield), a nonchalant attitude, and competitive behaviour. This paper presents a cost-effective computer vision framework using UAV videos to analyse right-turning behaviour and assess safety and operational performance (congestion) at uncontrolled T-intersections. A conflict cone of a vehicle is defined to automatically detect a right-of-way violation (RoWV) and yield. The impact of driving-related parameters and external traffic on non-compliant behaviour is analysed using the Tweedie generalised linear model. This paper proposes a modified surrogate safety measure, condPET, and a novel parameter, congValue, to identify critical conflicts and congestion due to non-compliant behaviour. Lateral evasive action is used to detect a constrained path because of nonchalant and competitive behaviours. Results indicate that only 7.50% of vehicles yielded, 6.25% of conflicts were critical, and congestion occurred for 44.00% of the total video time. Overall, 45.34% of vehicles created a constrained path, and 26.00% committed RoW violations, causing congestion and increasing the average travel time on major roads by 2.0 and 3.5 times, respectively.
Date Issued
2026-06-17
Citation
Preprints.org, 2026
Journal / Book Title
Preprints.org
Copyright Statement
Copyright © 2026 The Authors. This work is licensed under a Creative Commons Attribution 4.0 International License.
License URL
Description
Preprint version
Identifier
10.20944/preprints202606.1272.v1
Subjects
congestion
computer vision
heterogeneous traffic
right-of-way violations
right-turning behaviour
traffic conflict techniques
uncontrolled t-intersections
Unmanned Aerial Vehicles (UAV) congestion
computer vision
heterogeneous traffic
right-of-way violations
right-turning behaviour
traffic conflict techniques
uncontrolled t-intersections
Unmanned Aerial Vehicles (UAV)