Optimizing Smart Transportation with Real-Time Vehicle Tracking Systems for Enhanced Efficiency and Passenger Experience

This paper presents a novel automated data infrastructure for predicting bus delay patterns in urban transportation systems, addressing the pressing need for enhanced reliability and punctuality of public bus services. Urban transportation is vital for daily commuting, but disruptions due to traffic congestion, accidents, and adverse weather conditions often lead to service shortcomings. Our approach harnesses real-time data sources such as GPS trackers, traffic sensors, weather reports, and historical travel data, employing advanced data analytics and machine learning techniques to forecast bus delays with remarkable accuracy. These predictions empower transportation authorities and commuters to proactively manage and mitigate delays, significantly improving the daily commuting experience and contributing to the broader goal of building smarter and more efficient urban transportation systems. This paper outlines the development, implementation, and potential impact of this innovative automated data infrastructure, emphasizing its importance in the context of smart transportation and urban planning.