Connected vehicle communication technology is rapidly developing in recent years, and
host vehicle (HV) can send or receive the basic safety message (BSM) from the remote …

Abstract Structured Bayesian networks (SBNs) are a recently proposed class of probabilistic
graphical models which integrate background knowledge in two forms: conditional …

Optimal stopping problems consider the question of deciding when to stop an observation-
generating process in order to maximize a return. We examine the problem of …

The ubiquitous GPS-enabled devices (eg, vehicles and mobile phones) have led to the
unexpected growth in trajectory data that can be well utilized for intelligent city management …

Most of the existing algorithms cannot effectively solve the data sparse problem of trajectory
prediction. This paper proposes a novel sparse trajectory prediction method based on LZ …

Existing moving object's trajectory prediction algorithms suffer from the data sparsity
problem, which affects the accuracy of the trajectory prediction. Aiming to the problem, we …

With increasing popularity of smartphone apps such as fitness assistants, location‐based
services, more and more pedestrians now travel in short distances with purposes of leisure …

Existing trajectory prediction methods suffer from the “data sparsity” and neglect “time
awareness”, which leads to low accuracy. Aiming to the problem, we propose a fast time …

Eine zuverlässige Prognose der Lieferzeit für Logistiktransporte generiert einen
signifikanten Mehrwert für die Logistikqualität der Supply-Chain-Teilnehmer. Materialplaner …

Probabilistic graphical models, eg Bayesian Networks, have been traditionally introduced to
model and reason with uncertainty. A graph structure is crafted to capture knowledge of …