Semi-Supervised Federated Learning for Travel Mode Identification From GPS Trajectories

Zhu，Yuanshao1; Liu，Yi2; Yu，James J.Q.3; Yuan，Xingliang4

2022-03

10.1109/TITS.2021.3092015

IEEE Transactions on Intelligent Transportation Systems   影响因子和分区

1558-0016

Travel mode identification GPS trajectory federated learning deep learning semi-supervised learning

SCI ; EI

第一

20213410807329

Convolutional neural networks ; Recurrent neural networks ; Semi-supervised learning ; Trajectories

ENGINEERING

IEEE

1.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
2.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China. He is now with the Department of Computer Science, City University of Hong Kong, Hong Kong SAR 518057, China.
3.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China (e-mail: yujq3@sustech.edu.cn).
4.Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia.

Zhu，Yuanshao,Liu，Yi,Yu，James J.Q.,et al. Semi-Supervised Federated Learning for Travel Mode Identification From GPS Trajectories[J]. IEEE Transactions on Intelligent Transportation Systems,2022,23(3):2380-2391.

Zhu，Yuanshao,Liu，Yi,Yu，James J.Q.,&Yuan，Xingliang.(2022).Semi-Supervised Federated Learning for Travel Mode Identification From GPS Trajectories.IEEE Transactions on Intelligent Transportation Systems,23(3),2380-2391.

Zhu，Yuanshao,et al."Semi-Supervised Federated Learning for Travel Mode Identification From GPS Trajectories".IEEE Transactions on Intelligent Transportation Systems 23.3(2022):2380-2391.