Reliable bus-arrival estimates can reduce passenger uncertainty and improve travel planning in urban public-transport systems. This work studies predictive approaches for estimating arrival times from transportation data and compares methods including Linear Regression, ARIMA, Long Short-Term Memory networks, and Gated Recurrent Units. The analysis contrasts traditional statistical and machine-learning techniques with recurrent neural models designed to capture temporal dependencies. By examining their suitability for arrival-time prediction, the study contributes evidence about how predictive modeling can support more accurate real-time passenger information and improve the practical usability of public transportation services.