Inferring transportation modes from GPS trajectories using a convolutional neural network
Abstract
A CNN architecture is proposed to infer transportation modes from GPS trajectories. An adaptable and efficient layout for the input layer of the CNN is designed. Key factors in the CNN: remove anomalies, data augmentation, use the bagging concept. The proposed CNN achieves the accuracy of 84.8%, higher than other studies.
- Publication:
-
Transportation Research Part C: Emerging Technologies
- Pub Date:
- January 2018
- DOI:
- 10.1016/j.trc.2017.11.021
- arXiv:
- arXiv:1804.02386
- Bibcode:
- 2018TRPC...86..360D
- Keywords:
-
- Convolutional neural network;
- Deep learning;
- GPS data;
- Transportation mode Inference;
- Computer Science - Machine Learning;
- Statistics - Machine Learning
- E-Print:
- 12 pages, 3 figures, 7 tables, Transportation Research Part C: Emerging Technologies