TrajVAE: A Variational AutoEncoder model for trajectory generation
Large-scale trajectory dataset is always required for self-driving and many other applications. In this paper, we focus on the trajectory generation problem, which aims to generate qualified trajectory dataset that is indistinguishable from real trajectories, for fulfilling the needs of large-scale...
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| Veröffentlicht in: | Neurocomputing (Amsterdam) Jg. 428; S. 332 - 339 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
07.03.2021
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | Large-scale trajectory dataset is always required for self-driving and many other applications. In this paper, we focus on the trajectory generation problem, which aims to generate qualified trajectory dataset that is indistinguishable from real trajectories, for fulfilling the needs of large-scale trajectory data by self-driving simulation and traffic analysis tasks in data sparse cities or regions. We propose two advanced solutions, namely TrajGAN and TrajVAE, which utilize LSTM to model the characteristics of trajectories first, and then take advantage of Generative Adversarial Network (GAN) and Variational AutoEncoder (VAE) frameworks respectively to generate trajectories. In order of compare the similarity of existing trajectories in our dataset and the generated trajectories, we utilize multiple trajectory similarity metrics. Through several experiments, we demonstrate that our method is more accurate and stable than the baseline. |
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| AbstractList | Large-scale trajectory dataset is always required for self-driving and many other applications. In this paper, we focus on the trajectory generation problem, which aims to generate qualified trajectory dataset that is indistinguishable from real trajectories, for fulfilling the needs of large-scale trajectory data by self-driving simulation and traffic analysis tasks in data sparse cities or regions. We propose two advanced solutions, namely TrajGAN and TrajVAE, which utilize LSTM to model the characteristics of trajectories first, and then take advantage of Generative Adversarial Network (GAN) and Variational AutoEncoder (VAE) frameworks respectively to generate trajectories. In order of compare the similarity of existing trajectories in our dataset and the generated trajectories, we utilize multiple trajectory similarity metrics. Through several experiments, we demonstrate that our method is more accurate and stable than the baseline. |
| Author | Chen, Wei Zhou, Rui Fang, Junhua Xu, Jiajie Liu, Chengfei Chen, Xinyu |
| Author_xml | – sequence: 1 givenname: Xinyu surname: Chen fullname: Chen, Xinyu email: xychen1015@stu.suda.edu.cn organization: School of Computer Science and Technology, Soochow University, SuZhou, China – sequence: 2 givenname: Jiajie surname: Xu fullname: Xu, Jiajie email: xujj@suda.edu.cn organization: School of Computer Science and Technology, Soochow University, SuZhou, China – sequence: 3 givenname: Rui surname: Zhou fullname: Zhou, Rui email: rzhou@swin.edu.au organization: Faculty of SET, Swinburne University of Technology, Melbourne, Australia – sequence: 4 givenname: Wei surname: Chen fullname: Chen, Wei email: 20124227003@suda.edu.cn organization: School of Computer Science and Technology, Soochow University, SuZhou, China – sequence: 5 givenname: Junhua surname: Fang fullname: Fang, Junhua email: jhfang@suda.edu.cn organization: School of Computer Science and Technology, Soochow University, SuZhou, China – sequence: 6 givenname: Chengfei surname: Liu fullname: Liu, Chengfei email: cliu@swin.edu.au organization: Faculty of SET, Swinburne University of Technology, Melbourne, Australia |
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| Title | TrajVAE: A Variational AutoEncoder model for trajectory generation |
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