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

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 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. The 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 assessments 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. © 2026 by the authors.