4 research outputs found
A Force-Directed Approach for Offline GPS Trajectory Map Matching
We present a novel algorithm to match GPS trajectories onto maps offline (in
batch mode) using techniques borrowed from the field of force-directed graph
drawing. We consider a simulated physical system where each GPS trajectory is
attracted or repelled by the underlying road network via electrical-like
forces. We let the system evolve under the action of these physical forces such
that individual trajectories are attracted towards candidate roads to obtain a
map matching path. Our approach has several advantages compared to traditional,
routing-based, algorithms for map matching, including the ability to account
for noise and to avoid large detours due to outliers in the data whilst taking
into account the underlying topological restrictions (such as one-way roads).
Our empirical evaluation using real GPS traces shows that our method produces
better map matching results compared to alternative offline map matching
algorithms on average, especially for routes in dense, urban areas.Comment: 10 pages, 12 figures, accepted version of article submitted to ACM
SIGSPATIAL 2018, Seattle, US
Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph
We present a SE(3)-equivariant graph neural network (GNN) approach that
directly predicting the formation factor and effective permeability from
micro-CT images. FFT solvers are established to compute both the formation
factor and effective permeability, while the topology and geometry of the pore
space are represented by a persistence-based Morse graph. Together, they
constitute the database for training, validating, and testing the neural
networks. While the graph and Euclidean convolutional approaches both employ
neural networks to generate low-dimensional latent space to represent the
features of the micro-structures for forward predictions, the SE(3) equivariant
neural network is found to generate more accurate predictions, especially when
the training data is limited. Numerical experiments have also shown that the
new SE(3) approach leads to predictions that fulfill the material frame
indifference whereas the predictions from classical convolutional neural
networks (CNN) may suffer from spurious dependence on the coordinate system of
the training data. Comparisons among predictions inferred from training the CNN
and those from graph convolutional neural networks (GNN) with and without the
equivariant constraint indicate that the equivariant graph neural network seems
to perform better than the CNN and GNN without enforcing equivariant
constraints