Quantifying Intrinsic Value of Information of Trajectories

TL;DR

This paper introduces an information gain-based framework to quantify the intrinsic value of individual trajectories by measuring how much they reduce uncertainty about a person's locations over time.

Contribution

It presents a novel, principled method to assess the intrinsic value of trajectories considering various characteristics and prior knowledge.

Findings

The IG framework effectively captures key factors influencing VOI.

Qualitative and quantitative evaluations demonstrate the framework's capability.

The approach transforms discrete measurements into continuous representations for analysis.

Abstract

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.