A Multitask Learning Framework for Predicting Origin–Destination Ride‐Sourcing Demand

ABSTRACT Accurate Origin–Destination (OD) ride‐sourcing demand prediction is crucial for supply–demand management platforms and ride‐hailing companies. However, unlike zone‐based demand prediction, OD demand prediction is more complex due to two challenges. Firstly, it necessitates simultaneous consideration of the geographical contexts of both origins and destinations. Secondly, the demands of different OD pairs tend to exhibit diverse spatial–temporal patterns. Therefore, directly training a normal deep model, like that commonly used for zone‐based demand prediction, may not be effective in simultaneously capturing the complex spatio–temporal pattern features relevant to these two challenges. To solve this problem, we introduce an end‐to‐end MultiTask Learning framework for OD demands Prediction, called MTL‐ODP, to mitigate challenges through the design of two progressive prediction tasks. Specifically, a zone‐based demand prediction module is first proposed to extract zone flow information from individual urban areas. Subsequently, an OD demand prediction module is introduced to model the spatial–temporal distribution pattern among OD pairs, leveraging the information extracted from the first module. In this way, these two challenges can be effectively decomposed and subsequently solved by two progressive modules, and accurate OD demand prediction results can be ultimately obtained. Extensive experiments on the for‐hire vehicles datasets in Beijing City validate the effectiveness of our multitask learning framework.