An effective traffic control measure is the one that can significantly reduce delays incurred due to rising traffic congestion and improves travel time reliability. To achieve this, an accurate estimation of delay is very critical. The method of delay estimation varies with the type of data source available, and the type of data collected. This paper aims at studying various methods of delay estimation at an intersection for data types from three data sources—location-based data (videography), Wi-Fi sensor data, and GPS based probe data. The challenges associated with executing each of these methods are also discussed. Besides, a scalable and reliable data source among the three was chosen to calibrate and validate the delay equation suggested by Highway Capacity Manual 2000 (HCM 2000) to suit Indian traffic conditions. A linear regression model was fitted for the progression factor (PF) of the HCM delay equation with an R squared value of 0.85. Validation of the calibrated model yielded an average Mean Absolute Percentage Error (MAPE) of 12.59%. The calibrated model can be used for the estimation of delay based on historic traffic arrival patterns and signal timings.