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.

中文翻译：

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量化轨迹信息的内在价值

轨迹，定义为一系列位置测量，包含有关个人运动的宝贵信息。其信息价值 (VOI) 可能会根据特定应用而变化。然而，在各种应用中，了解轨迹的内在 VOI 对于指导其他后续任务或决策很重要。这项工作旨在找到一个有原则的框架，从所有者的角度量化轨迹的内在 VOI。这是一个具有挑战性的问题，因为合适的框架需要考虑轨迹的各种特征、先验知识和不同类型的轨迹退化。我们提出了一个基于信息增益（IG）的框架作为解决这个问题的原则方法。我们的 IG 框架将具有离散时间测量的轨迹转换为规范表示，即具有连续均值和方差估计的时间连续，然后将一段时间内所有者位置的不确定性的减少量化为 VOI轨迹。定性和广泛的定量评估表明，IG 框架能够有效地捕捉有助于轨迹 VOI 的重要特征。