Improving Public Transport Systems Through Big Data Analytics: A Roadmap for Smarter Mobility Solutions

The following paper aims to discover the application of big data technology to optimise public transport, particularly in its performance aspects, customers' satisfaction, safety, and eco-friendly nature. Since there is an increase in complexity for mobility in the urban environment, the concepts of managing PTD based on old school approaches pose problem and are unable to address issues of demand, congestion and service irregularities. Based on real-time data from GPS, smart ticketing and passenger feedback system, this research explores a conceptual framework for decision making for enhancing service delivery. The flow comprises data gathering, preparation, and fusion, analytics in the form of prediction, monitoring, sentiment analysis, and machine learning. These tools facilitate real-time route optimization, demand prediction, and customers' satisfaction increases. Results show significant improvements: Lower turnaround time, higher on time performance and satisfaction level among passengers. Sustainability is introduced by a reduction in fuel consumption and emissions by 20%, as well as by the broader provision of hybrid and electrical vehicles. Measures in safety were improved as well reflected by the decrease of incident rates and the increase of response time. These research findings are evident of how big data hold the possibilities to revolutionalise PT systems to be more effective, reliable and centre around the passengers. These findings reveal the advantages of evidence-based management in urban commuting and outline possible further usage of new technologies to enhance transport.