… the trajectory of vehicles between sparse updates using a maximum likelihood approach. …
segments of an arterial road using only sparse updates from probe vehicles. Using the same …

… iterate over the whole trajectory to maximize accuracy. We test the approach experimentally
on a simultaneous trajectory estimation and … We begin with the marginal likelihood equation, …

… If we let x = x + δx, the joint likelihood between the state perturbation and the measurements
(both at the measurement times) is … and then update the guess according to x …

… of trajectories for an … trajectory patterns (mined from historical trajectories) to scale down
the candidates of concatenation and a suffix-tree-based index to manage the trajectories …

… through efficient variable reordering and incremental sparse updates, which we believe will
… prior distribution of the combined state and the likelihood of measurements, we compute the …

… We run a maximum likelihood estimation of the variables of six standard long-tailed distributions
(… We then update the known trajectory as ˆLΩ = {li | i ∈ Ω} ∪ {ˆli | i ∈ ΩH}, and the set of …

… (Contextual information and Sparse GPS trace) Known: The task is to update the state of
the system by refining state-transition probability from partial trajectory traces and present …

… the terms for the likelihood from Section 3.2 … trajectory data in the least squares sense. To
the best of our knowledge, this work is the first time MCMC and sparse factorization updating …

… are encountered and only update the trajectory where required, … We define the likelihood as
a distribution in the exponential … states and allow the trajectory to stay sparse but still reason …

… Moreover, we derive a novel online update scheme for sparse … we compute the measurement
likelihood of the selected pair … same time, generate a smooth trajectory through inference. …