<p>This study investigates the unique traffic dynamics on Indian roads, characterized by a diverse mix of vehicle types and non-lane-based driving behavior, using a section in the Delhi-Panipat route as a case study. Unlike the strictly lane-disciplined traffic in Western countries, Indian traffic involves complex interactions among vehicles, making accurate prediction and modeling challenging. This research leverages high-fidelity trajectory data collected via Unmanned Aerial Vehicle (UAVs), capturing detailed vehicular movements on an eight-lane divided highway. The vehicles in the data set are categorized into six types, providing a rich basis for analyzing lateral positioning, speed, flow, and density distributions. The study employs exploratory data analysis to illustrate the relationship between speed, lateral position, and vehicle density for different vehicle types. One-way ANOVA (ANalysis Of VAriance) tests reveal significant differences in driving behaviors across lanes, while Games-Howell post-hoc tests identify specific lane pair distinctions. Furthermore, multiple linear regression (MLR) models highlight key predictors of lateral positioning, including vehicle speed, local density, flow rate, and vehicle type, with R-squared values of 0.695 for the northbound and 0.612 for the southbound sections. The findings demonstrate that vehicle types like cars, buses, and three-wheelers significantly influence lateral placement, with cars showing the highest lateral placement predictability. Variance Inflation Factors (VIF) indicate low multi-collinearity among most predictors, confirming the robustness of the model. This research enhances the understanding of lateral placement patterns in heterogeneous, non-lane-based traffic environments, providing a foundation for future studies to explore further variables and broader traffic scenarios.</p>