Passenger Request Prediction

Passenger request prediction focuses on accurately forecasting passenger travel patterns to optimize transportation systems and services. Current research emphasizes leveraging graph neural networks (GNNs) and deep learning models, such as convolutional neural networks (CNNs), to capture complex spatial and temporal dependencies in passenger data, often incorporating features like origin-destination flows and contextual information. These advancements aim to improve resource allocation (e.g., ride-sharing drivers, public transit vehicles), enhance operational efficiency, and ultimately lead to better transportation planning and user experiences. The field's impact extends to smart city development and the optimization of various transportation modes, from ride-hailing services to public buses.