IMAP: Individual huMAn mobility Patterns visualizing platform

TL;DR

IMAP is a visualization platform that captures and displays human mobility patterns by abstracting location sequences into places, improving understanding of flexible human movement for urban planning and social research.

Contribution

The paper introduces IMAP, a novel platform that visualizes human mobility patterns using location abstraction and a modified PrefixSpan algorithm, addressing low accuracy in existing models.

Findings

Provides a visual graph of visited places.

Displays frequent mobility patterns.

Enhances understanding of human movement flexibility.

Abstract

Understanding human mobility is essential for the development of smart cities and social behavior research. Human mobility models may be used in numerous applications, including pandemic control, urban planning, and traffic management. The existing models' accuracy in predicting users' mobility patterns is less than 25%. The low accuracy may be justified by the flexible nature of the human movement. Indeed, humans are not rigid in their daily movement. In addition, the rigid mobility models may result in missing the hidden regularities in users' records. Thus, we propose a novel perspective to study and analyze human mobility patterns and capture their flexibility. Typically, the mobility patterns are represented by a sequence of locations. We propose to define the mobility patterns by abstracting these locations into a set of places. Labeling these locations will allow us to detect close-to-reality hidden patterns. We present IMAP, an Individual huMAn mobility Patterns visualizing platform. Our platform enables users to visualize a graph of the places they visited based on their history records. In addition, our platform displays the most frequent mobility patterns computed using a modified PrefixSpan approach.