Historical Ridership Data

Historical ridership data analysis aims to understand and predict passenger volume in public transportation systems, informing service optimization and resource allocation. Current research focuses on improving the accuracy of automated passenger counting data through denoising techniques and data fusion from multiple sources (e.g., fare collection systems), often employing machine learning models like LSTM networks, XGBoost, and probabilistic graph neural networks. These advancements enhance the reliability of ridership predictions, leading to more efficient transit operations and improved passenger experience.