Evaluating traffic conflicts and congestion based on right-turning driving behaviour using evasive actions driven pet via UAV video analysis: a case study of uncontrolled heterogeneous T-intersection in India
File(s)
Accepted version
Author(s)
Bhavsar, Yagnik
Zaveri, Mazad S
Raval, Mehul S
Shukla, Pancham
Zaveri, Shaheriar B
Type
Journal Article
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 non-chalant 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 an aggregated 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 (compared to 38.94% using PET alone and 11.05% using CS), and localised 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. Our methodology enables computer vision-based automated assessment of both road traffic safety and operational performance
at uncontrolled T-intersections under non-uniform traffic conditions, providing a valuable tool for road traffic monitoring systems.
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 (compared to 38.94% using PET alone and 11.05% using CS), and localised 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. Our methodology enables computer vision-based automated assessment of both road traffic safety and operational performance
at uncontrolled T-intersections under non-uniform traffic conditions, providing a valuable tool for road traffic monitoring systems.
Date Acceptance
2026-07-15
Citation
Technologies
ISSN
2227-7080
Publisher
MDPI AG
Journal / Book Title
Technologies
Copyright Statement
Copyright This paper is embargoed until publication. Once published the Version of Record (VoR) will be available on immediate open access.
License URL
Publication Status
Accepted