Modeling human mobility under uncertain conditions and individual preferences remains a
difficult and unsolved problem. Data-driven deep learning approaches require extensive
trajectory data for training, while more traditional methods often assume deterministic
conditions or simple minimum-cost paths. We propose an implicit neural representation
(INR) to learn continuous, latent fields of stochastic traffic properties over space and time.
We successfully impute speeds on a road network with hundreds of thousands of edges …

Generating trajectories from an implicit neural representation (INR) model to predict human
mobility in uncertain traffic conditions includes receiving geocoordinate data representing
vehicle motion observations of a traffic pattern; receiving a road network based on the
geocoordinate data; training the INR model to learn continuous, latent fields of stochastic
traffic properties over space and time based on the geocoordinate data; utilizing the INR
model to extract spatio-temporal speed distributions from the geocoordinate data; applying a …