Volume Delay Functions (VDFs) are essential for predicting congestion levels and are crucial for infrastructure planning and traffic management. They help quantify the impact of traffic volume on travel times, allowing planners to make informed decisions about road network improvements and traffic management interventions. This study calibrates VDFs using crowd-sourced data with videography data, focusing on urban mid-block sections in Delhi. Unlike traditional approaches, this research determines free-flow speeds using real-time crowd-sourced data and establishes road capacity from field data using 5-minute volume observations. The methodology is applied to 8 different locations in Delhi. The results indicate that among the tested VDFs, which include the Bureau of Public Roads (BPR), conical, and Akcelik models, the calibrated BPR model provided the best fit. This model reduced the mean absolute percentage error (MAPE) for all locations by approximately 60%. Additionally, the BPR model was validated at a separate location, confirming its accuracy. By incorporating real-time crowd-sourced data, this research provides insights for more accurate traffic modeling.