Public Transport Ridership

Predicting and understanding public transport ridership is crucial for optimizing service efficiency and improving passenger experience. Current research focuses on developing sophisticated predictive models, employing machine learning algorithms like LSTM networks, XGBoost, and even large language models trained on customer feedback data, to account for factors such as weather, time of day, and even the impact of events like pandemics. These models aim to improve accuracy and incorporate spatial and temporal dependencies in ridership patterns, ultimately leading to better resource allocation and more informed transit planning. The improved accuracy and insights gained from these models have significant implications for both transit agencies and urban planning, enabling more efficient and equitable public transportation systems.