  Understanding and predicting real-time vehicle mobility patterns on highways
are essential to address traffic congestion and respond to the emergency.
However, almost all existing works (e.g., based on cellphones, onboard devices,
or traffic cameras) suffer from high costs, low penetration rates, or only
aggregate results. To address these drawbacks, we utilize Electric Toll
Collection systems (ETC) as a large-scale sensor network and design a system
called VeMo to transparently model and predict vehicle mobility at the
individual level with a full penetration rate. Our novelty is how we address
uncertainty issues (i.e., unknown routes and speeds) due to sparse implicit ETC
data based on a key data-driven insight, i.e., individual driving behaviors are
strongly correlated with crowds of drivers under certain spatiotemporal
contexts and can be predicted by combining both personal habits and context
information. More importantly, we evaluate VeMo with (i) a large-scale ETC
system with tracking devices at 773 highway entrances and exits capturing more
than 2 million vehicles every day; (ii) a fleet consisting of 114 thousand
vehicles with GPS data as ground truth. We compared VeMo with state-of-the-art
benchmark mobility models, and the experimental results show that VeMo
outperforms them by average 10% in terms of accuracy.
