Bus Arrival

Accurately predicting bus arrival times is crucial for improving public transit efficiency and ridership. Current research focuses on developing sophisticated data-driven models, employing machine learning techniques like XGBoost, graph neural networks, and recurrent neural networks (including GRU-based Seq2Seq architectures), to leverage both spatial and temporal data, including real-time traffic conditions and historical trip information. These models aim to overcome challenges posed by factors like varying dwell times, traffic congestion, and incomplete data availability, particularly in developing world contexts. Improved prediction accuracy translates directly to enhanced passenger experience and more efficient resource allocation within urban transportation systems.