Explaining human mobility predictions through a pattern matching algorithm

Smolak, K., Rohm, W. and Sila-Nowicka, K. (2022) Explaining human mobility predictions through a pattern matching algorithm. EPJ Data Science, 11, 45. (doi: 10.1140/epjds/s13688-022-00356-4)

Abstract

Understanding what impacts the predictability of human movement is a key element for the further improvement of mobility prediction models. Up to this day, such analyses have been conducted using the upper bound of predictability of human mobility. However, later works indicated discrepancies between the upper bound of predictability and accuracy of actual predictions suggesting that the predictability estimation is not accurate. In this work, we confirm these discrepancies and, instead of predictability measure, we focus on explaining what impacts the actual accuracy of human mobility predictions. We show that the accuracy of predictions is dependent on the similarity of transitions observed in the training and test sets derived from the mobility data. We propose and evaluate five pattern matching based-measures, which allow us to quickly estimate the potential prediction accuracy of human mobility. As a result, we find that our metrics can explain up to 90% of its variability. We also find that measures that were proved to explain the variability of predictability measure, fail to explain the variability of predictions accuracy. This suggests that predictability measure and accuracy of predictions should not be compared. Our metrics can be used to quickly assess how predictable the data will be for prediction algorithms. We share developed metrics as a part of HuMobi, the open-source Python library.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sila-Nowicka, Ms Katarzyna
• Authors: Smolak, K., Rohm, W., and Sila-Nowicka, K.
• College/School: College of Social Sciences > School of Social and Political Sciences
• Journal Name: EPJ Data Science
• Publisher: SpringerOpen
• ISSN: 2193-1127
• ISSN (Online): 2193-1127
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in EPJ Data Science 11: 45
• Publisher Policy: Reproduced under a Creative Commons License