Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2017

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 Neura lNetwork (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 topo-logical structure on trajectory modeling. Our experimental study based on real taxi trajectory datasets shows that both of our approaches largely outperform the existing approaches.

Keywords

Artificial intelligence, Inverse problems, Markov processes, Reinforcement learning, Taxicabs, Topology, Trajectories, Building blockes, Inverse reinforcement learning, Logical structure, Long-term dependencies, Recurrent neural network (RNN), Topological constraints, Trajectory data, Trajectory modeling, Recurrent neural networks

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Proceedings of the 26th International Joint Conference on Artificial Intelligence IJCAI-17, Melbourne, Australia, August 19-25

First Page

3083

Last Page

3090

ISBN

9780999241103

Identifier

10.24963/ijcai.2017/430

Publisher

IJCAI

City or Country

San Francisco, CA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.24963/ijcai.2017/430

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