Learning Location Semantics and Dynamics for Traffic Origin-Destination Demand Prediction

Traffic Origin-Destination (OD) Demand Prediction is pivotal for real-time ride-hailing services and government traffic management, aiming to anticipate traffic patterns and volumes between specific locations. Traditional grid-based traffic OD demand prediction methods, however, often fail to accurately capture the intricate and context-driven demand patterns inherent in modern urban transportation systems. In this paper, we propose an innovative location semantics and dynamics learning framework for capturing location semantics and dynamics to improving origin-destination traffic demand prediction. Departing from conventional grid-based methods, our approach incorporates a semantic location generation module that dynamically organizes semantic locations based on Points of Interest (POI). Our semantic location generation module is designed to form semantic locations according to POI types, spatial proximity, and demand patterns. This model captures hierarchical relationships and varying importance levels across POI types and domains. To learn the context and function of each area, we design a POI context extractor which can analyze contextual information of POIs within a specified radius. The POI Type Encoder utilizes advanced word embedding techniques to encode the POIs, enabling the model to comprehend the semantic significance of diverse locations and their influence on traffic patterns. Extensive experiments conducted on real-world New York City Taxi datasets demonstrate the superior performance of the proposed framework over state-of-the-art methods, as indicated by experiment results across all evaluation measurement. These findings affirm that our proposed framework could improve effectiveness by learning location semantics and dynamics.