Publications
論文誌 (国際) Predicting Individual Irregular Mobility via Web Search-Driven Bipartite Graph Neural Networks
Jiawei Xue (Purdue University), Takahiro Yabe (Massachusetts Institute of Technology), Kota Tsubouchi, Jianzhu Ma (Tsinghua University), Satish V. Ukkusuri (Purdue University)
IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE)
2024.11.21
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 than residences and workplaces. Despite their usefulness in recommendations and advertising, the stochastic, spontaneous nature of irregular trips makes them challenging to predict. To address the difficulty, this study search-driven bipartite graph neural network, namely WS-BiGNN, for the individual irregular mobility prediction 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- propagation within sparse bipartite graphs. Second, the temporal weighting module is created to discern the 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. real-world data in Tokyo from October 2019 to March 2020. The results showcase the superior performance of WS- to baseline models, as supported by higher scores in Recall and NDCG.
Paper :
Predicting Individual Irregular Mobility via Web Search-Driven Bipartite Graph Neural Networks
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