{"type":"ScholarlyArticle","title":"Modeling Trajectories with Recurrent Neural Networks","authors":[{"name":"Hao Wu","slug":"hao-wu","given":"Hao","family":"Wu","affiliation":"† School of Computer Science , Fudan University , Shanghai , China; † Shanghai Key Laboratory of Data Science , Fudan University , Shanghai , China"},{"name":"Ziyang Chen","slug":"ziyang-chen","given":"Ziyang","family":"Chen","affiliation":"† School of Computer Science , Fudan University , Shanghai , China; † Shanghai Key Laboratory of Data Science , Fudan University , Shanghai , China"},{"name":"Weiwei Sun","slug":"weiwei-sun","given":"Weiwei","family":"Sun","affiliation":"† School of Computer Science , Fudan University , Shanghai , China; † Shanghai Key Laboratory of Data Science , Fudan University , Shanghai , China"},{"name":"Baihua Zheng","slug":"baihua-zheng","given":"Baihua","family":"Zheng","affiliation":"‡ Singapore Management University , Singapore"},{"name":"Wei Wang","slug":"wei-wang","given":"Wei","family":"Wang","affiliation":"† School of Computer Science , Fudan University , Shanghai , China; † Shanghai Key Laboratory of Data Science , Fudan University , Shanghai , China"}],"abstract":"Modeling trajectory data is a building block for many smart-mobility initiatives. Existing approaches apply shallow models such as Markov chain and inverse reinforcement learning to model trajectories, which cannot capture the long-term dependencies. On the other hand, deep models such as Recurrent Neural Network (RNN) have demonstrated their strength of modeling variable length sequences. However, directly adopting RNN to model trajectories is not appropriate because of the unique topological constraints faced by trajectories. Motivated by these findings, we design two RNN-based models which can make full advantage of the strength of RNN to capture variable length sequence and meanwhile to address the constraints of topological structure on trajectory modeling. Our experimental study based on real taxi trajectory datasets shows that both of our approaches largely outperform the existing approaches.","doi":"10.24963/ijcai.2017/430","url":"https://ijcai.org/proceedings/2017/430","pdf":"https://ijcai.org/proceedings/2017/0430.pdf","markdown":"https://ijcai.org/proceedings/2017/430.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/430","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":430,"pages":{"first":3083,"last":3090},"keywords":["Machine Learning: Deep Learning","Multidisciplinary Topics and Applications: AI and Ubiquitous Computing Systems","Uncertainty in AI: Sequential Decision Making","Machine Learning: Neural Networks"],"videos":[],"references":[{"position":1,"raw":"[Cho et al., 2014] Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Hol- ger Schwenk, and Yoshua Bengio. 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