Transit Demand

Accurately predicting public transit demand is crucial for optimizing resource allocation and improving passenger experience. Current research focuses on developing robust predictive models, employing machine learning techniques like LSTM and XGBoost, to forecast ridership at both the trip and stop levels, considering factors such as weather, traffic, and even large-scale disruptions. These models are being rigorously tested and benchmarked against various conditions, including highly dynamic situations like pandemics and protests, to improve their reliability and adaptability. The ultimate goal is to enhance the efficiency and effectiveness of public transit systems through data-driven decision-making.