Predicting Individual Irregular Mobility via Web Search-Driven Bipartite Graph Neural Networks

Abstract

Individual mobility prediction holds significant importance in urban computing, supporting various applications such as place recommendations. Current studies primarily focus on frequent mobility patterns including commuting trips to residential and workplaces. However, such studies do not accurately forecast irregular trips, which incorporate journeys that end at locations other than residences and workplaces. Despite their usefulness in recommendations and advertising, the stochastic, infrequent, and spontaneous nature of irregular trips makes them challenging to predict. To address the difficulty, this study proposes a web search-driven bipartite graph neural network, namely WS-BiGNN, for the individual irregular mobility prediction (IIMP) problem. Specifically, we construct bipartite graphs to represent mobility and web search records, formulating the IIMP problem as a link prediction task. First, WS-BiGNN employs user-user edges and POI-POI edges (POI: point-of-interest) to bolster information propagation within sparse bipartite graphs. Second, the temporal weighting module is created to discern the influence of past mobility and web searches on future mobility. Lastly, WS-BiGNN incorporates the search-mobility memory module, which classifies four interpretable web search-mobility patterns and harnesses them to improve prediction accuracy. We perform experiments utilizing real-world data in Tokyo from October 2019 to March 2020. The results showcase the superior performance of WS-BiGNN compared to baseline models, as supported by higher scores in Recall and NDCG. The exceptional performance and additional analysis reveal that infrequent behavior may be effectively predicted by learning search-mobility patterns at the individual level.