Apply Camera Perspective Correction to Ensure Spatial Consistency and Reliable Speed and Queue Estimates Across Cameras with Varying Angles and Orientations.

The study identified several lessons learned. 

• Apply perspective transformations to ensure spatial consistency and reliable speed and queue estimates across cameras with varying angles and orientations. Camera perspective correction is critical for standardizing measurements across camera locations and viewpoints to ensure that traffic metrics are directly comparable and support accurate before-and-after analyses.
• Expect airport traffic to exhibit unique operational behaviors not captured by standard vision models. Frequent stop-and-go movements, informal lane formations, and curbside loading require targeted model retraining and customized detection logic.
• Anticipate obstructions and operational variability that affect detection reliability. This necessitates careful selection of analysis periods and ongoing validation of model outputs.
• Use vehicle dwell time as a reliable performance metric to evaluate curbside operations. This enables meaningful before-and-after assessments of operational changes across both curbside and active travel lanes.