Delhi Metro Operations Optimization

The Delhi Metro, one of the most extensive and sophisticated urban transit systems globally, faces continuous challenges in optimizing operations due to fluctuating passenger demand and complex scheduling dynamics. This paper presents a machine learning-based approach to analyze and predict operational patterns within the Delhi Metro network. Utilizing datasets comprising metro routes, stops, schedules, and trip frequencies, the study explores spatial and temporal variations in metro operations across different times of the day and days of the week. Key operational metrics, such as stop-wise route frequency and trip intervals, are examined through data visualization and exploratory analysis. A Random Forest Regressor model is developed to predict the number of routes at specific metro stops based on time, day, and location. The system supports two user types: Admin, who manages users and FAQ content, and Users, who can register, log in, and access route prediction insights and FAQs. The results demonstrate the potential of machine learning in enhancing metro scheduling, optimizing resource allocation, and supporting data-driven decision-making for more efficient and sustainable urban transportation.