| Autor: |
Yazdizadeh, Ali, Patterson, Zachary, Farooq, Bilal |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Journal of Big Data Analytics in Transportation, 2021 |
| Druh dokumentu: |
Working Paper |
| Popis: |
Semi-supervised Generative Adversarial Networks (GANs) are developed in the context of travel mode inference with uni-dimensional smartphone trajectory data. We use data from a large-scale smartphone travel survey in Montreal, Canada. We convert GPS trajectories into fixed-sized segments with five channels (variables). We develop different GANs architectures and compare their prediction results with Convolutional Neural Networks (CNNs). The best semi-supervised GANs model led to a prediction accuracy of 83.4%, while the best CNN model was able to achieve the prediction accuracy of 81.3%. The results compare favorably with previous studies, especially when taking the large-scale real-world nature of the dataset into account. |
| Databáze: |
arXiv |
| Externí odkaz: |
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