Enriching Large-Scale Trips With Fine-Grained Travel Purposes: A Semi-Supervised Deep Graph Embedding Framework

التفاصيل البيبلوغرافية
العنوان:	Enriching Large-Scale Trips With Fine-Grained Travel Purposes: A Semi-Supervised Deep Graph Embedding Framework
المؤلفون:	Liao, Chengwu, Chen, Chao, Guo, Suiming, Wang, Leye, Gu, Fuqiang, Luo, Jing, Xu, Ke
المصدر:	IEEE Transactions on Intelligent Transportation Systems; November 2023, Vol. 24 Issue: 11 p13228-13239, 12p
مستخلص:	Knowing why people travel is meaningful for human mobility understanding and smart services development. Unfortunately, in real-world scenarios, trip purpose cannot be automatically collected on a large scale, thus calling for effective prediction models. Nevertheless, since passengers’ trip purposes in the city are diverse and complicated, the prediction is very difficult especially at a fine-grained level. Worse still, the informative data sources and real purpose-labels about trips are commonly limited for model learning. To resolve the dilemma, we propose a semi-supervised deep embedding framework for predicting fine-grained trip purposes on a large scale. Specifically, we first derive augmented trip contexts from the vehicle’s GPS trajectory and public POI check-in data, then convert POI contexts into the graph structure. We further establish a Dual-Attention Graph Embedding Network with Autoencoder architecture (DAGE-A) to accomplish prediction and reconstruction simultaneously, in which category-aware graph attention networks are devised to model the POI semantics at trip’s origin/destination and extract complementary knowledge from unlabeled trips; and soft-attention is employed to aggregate different trip semantics appropriately for the final prediction. We conduct extensive experiments in Beijing and Shanghai, and results show our framework outperforms state-of-the-arts and could reduce labelling efforts by up to 20%. We also find that our model is generalized at different times and locations, and the performance varies for different trip purposes.
قاعدة البيانات:	Supplemental Index

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تدمد:	15249050 15580016
DOI:	10.1109/TITS.2022.3203464