Sup-HMM
Taming Volatile Trajectories: a Bayesian-optimized HMM for location-based service data.
Mobility representation engine
From GPS observations to transportation network representations.
A trajectory processing framework for recovering mobility paths from noisy observations and translating them across road and planning networks.
Sparse, noisy GPS trajectories
→Network-constrained mobility paths
→Road and planning representations
Map matching
NovaMatch infers network-constrained paths from sparse, noisy, and heterogeneous trajectory observations.
OSM, HERE, or user-defined geometry.
Recover continuity from incomplete trajectories.
For large mobility datasets and experiments.
Cross-network translation
Translate observed mobility onto networks that were never designed to represent GPS trajectories.
Structural representation · 03 / 03
PM-Tree asks a deeper question: how much trajectory information is actually needed to preserve path recoverability?
Interactive prototype
Run a compact sequence: observations, candidate roads, then the recovered path.
Research behind NovaMatch
Taming Volatile Trajectories: a Bayesian-optimized HMM for location-based service data.
How Much of a Trajectory Is Needed? Priority-guided hierarchical trajectory representation.
Mapping observed mobility into abstract links, connectors, and planning-network semantics.