Modeling trajectories with recurrent neural networks

• Main Authors: WU, Hao, CHEN, Ziyang, SUN, Weiwei, ZHENG, Baihua, WANG, Wei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: 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; Databases and Information Systems; Numerical Analysis and Scientific Computing; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/3847; https://ink.library.smu.edu.sg/context/sis_research/article/4849/viewcontent/0430.pdf
• Institution: Singapore Management University

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...