Abstract:
Recent advances in location tracking technologies have led to availability of the extensive spatio-temporal datasets. Extracting meaningful information from these datasets is a crucial for informed decision-making across the different industries. Objectives of the study encompass capturing the real-time trains trajectories, conducting a comprehensive analysis, and lastly visualizing results for the improved railway’s monitoring efficiency. The study also focuses on leveraging the PostgreSQL and its extensions to manage the moving objects data efficiently, particularly in context of Pakistan Railways. In order to achieve these objectives, study collected the comprehensive inventory of trains, stations, and schedules data from Pakistan Railways. Also, dynamic train footprints were obtained using the custom developed Python scripts and web APIs. Demanding preprocessing ensured data accuracy and data consistency. The heart of study lies in leveraging the PostgreSQL's capabilities for executing complex spatio-temporal analyses that encompassed detailed speed assessment, stop time analysis, and evaluation of the signal visibility. The findings of the study highlighted hidden patterns and the insights within train trajectories. Stop time analysis highlighted the fact that about 12% of the trains time is being consumed during station stops. Speed analysis identified that about 40 km/h is the average speed of the trains. Signal visibility analysis performed and resulted that MobilityDB queries are computationally efficient and easy to understand compared to PostgreSQL queries. Study's outcomes are not only beneficial for the railway sector but also emphasize the broader potential of the data-driven decision-making in optimizing the operational processes across various
