  A trajectory, defined as a sequence of location measurements, contains
valuable information about movements of an individual. Its value of information
(VOI) may change depending on the specific application. However, in a variety
of applications, knowing the intrinsic VOI of a trajectory is important to
guide other subsequent tasks or decisions. This work aims to find a principled
framework to quantify the intrinsic VOI of trajectories from the owner's
perspective. This is a challenging problem because an appropriate framework
needs to take into account various characteristics of the trajectory, prior
knowledge, and different types of trajectory degradation. We propose a
framework based on information gain (IG) as a principled approach to solve this
problem. Our IG framework transforms a trajectory with discrete-time
measurements to a canonical representation, i.e., continuous in time with
continuous mean and variance estimates, and then quantifies the reduction of
uncertainty about the locations of the owner over a period of time as the VOI
of the trajectory. Qualitative and extensive quantitative evaluation show that
the IG framework is capable of effectively capturing important characteristics
contributing to the VOI of trajectories.
