Behavioral Dynamics at Signalized Intersections during a Green Phase: A Data-Driven Model for Speed Prediction in Mixed-Traffic Conditions

Abstract Growing traffic is a major concern worldwide, impacting both transportation system performance and road safety. In India, these challenges are intensified by highly heterogeneous traffic, highlighting the need to examine flow characteristics and driving behavior under mixed-class, non-lane-disciplined conditions. This study analyzes vehicular behavior at signalized intersections to quantify speed and acceleration profiles in such environments, using unmanned aerial vehicle (UAV) video data collected 100 m upstream and 40 m downstream of the stop line, segmented into 20-m intervals. The UAV-based videographic method is specifically chosen to overcome line-of-sight occlusion inherent in traditional ground-based observation techniques, thereby enabling continuous, high-quality trajectory collection in dense, mixed-traffic settings. Data were collected at four signalized intersections in the cities of Nashik and Nagpur, Maharashtra, India, covering four major vehicle classes: two-wheelers, cars, three-wheelers, and heavy vehicles (including buses). The analysis revealed that two-wheelers and cars exhibited similar speed behavior across all locations, with cars consistently showing the strongest polynomial fit and highest <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper R squared" display="inline" overflow="scroll"> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> values, often exceeding 0.9. Two-wheelers also showed good fit, with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper R squared" display="inline" overflow="scroll"> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> ranging from 0.58 to 0.74, with heavy vehicles and buses displaying comparable trends. Three-wheelers showed more erratic behavior, and heavy vehicles generally exhibited the weakest correlation. Descriptive statistics and ANOVA confirmed statistically significant differences among vehicle classes. Based on these findings, a universal polynomial model framework is developed to predict mean speeds as a function of distance from the stop line, with a consistent structure across vehicle classes and locations during the green phase. The framework provides a novel basis for simulating and calibrating heterogeneous, non-lane-based traffic, with green-phase speed, acceleration, and deceleration profiles enabling accurate microsimulation calibration to support signal timing design, safety evaluation, and operational planning in complex urban settings.