PREDICTIVE ANALYTICS AND RIDE DYNAMICS MODELING IN UBER USING DATA SCIENCE METHODS

The rapid expansion of ride-hailing platforms such as Uber has transformed urban mobility and created new opportunities for analyzing travel behavior. This paper presents a spatiotemporal analysis of Uber ride data to extract patterns in demand across time and space. Using publicly available datasets and data science tools, we identify temporal ride trends, highdemand pickup zones, and correlations with contextual factors such as time of day and day of the week. The study also explores predictive modeling approaches and provides actionable recommendations for improving service efficiency and urban transportation planning.