Evaluating AI-Powered Predictive Analytics for Public Transport Demand in Dubai

This thesis examines the application of AI-driven predictive analytics to model demand for metro and tram rides in Dubai, utilizing machine learning models in SAS Viya, as Dubai advances its innovative city initiatives. Research focuses on how seasonal variations, the use of metro lines, and types of communities affect trends in passenger flow. Gradient Boosting emerged as the most reliable predictive model, achieving the highest KS values in all scenarios. It effectively captured the increase in ridership during winter on the Red Metro Line and identified summer declines on the Green Line. The findings highlight significant seasonal fluctuations in residential neighborhoods, underscoring the need for adaptable infrastructure planning and scheduling. In contrast, the commercial and industrial zones exhibited more stable patterns of ridership. By incorporating seasonal comparisons, line-specific segmentation, and spatial analysis, the study improves the understanding of how environmental and geographic factors influence the number of riders. Gradient Boosting’s ability to capture these variations provides a foundation for strategic, AI-driven resource allocation and transit scheduling. This research supports Dubai’s smart mobility goals by demonstrating the practical application of AI in improving public transportation systems. By facilitating more accurate predictions and segmentations of ridership demand in various contexts, this study provides actionable information for transit authorities and urban planners seeking to boost efficiency, sustainability, and user satisfaction in one of the fastest-growing cities globally.