Semi-supervised GANs to Infer Travel Modes in GPS Trajectories
release_zt25bsihezerdiwaz2xo7h3tma
by
Ali Yazdizadeh and Zachary Patterson and Bilal Farooq
2021
Abstract
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.
In text/plain
format
Archived Files and Locations
application/pdf 797.4 kB
file_wo7a2olnbfbifcygkfd6ekmoje
|
arxiv.org (repository) web.archive.org (webarchive) |
1902.10768v2
access all versions, variants, and formats of this works (eg, pre-prints)