We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Understanding predictability and exploration in human mobility.
- Authors
Cuttone, Andrea; Lehmann, Sune; González, Marta C.
- Abstract
Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.
- Subjects
TRAFFIC engineering; UBIQUITOUS computing; INFORMATION resources; PREDICTION models; SOCIAL mobility
- Publication
EPJ Data Science, 2018, Vol 7, Issue 1, p1
- ISSN
2193-1127
- Publication type
Article
- DOI
10.1140/epjds/s13688-017-0129-1