Abstract
Next place prediction algorithms are invaluable tools, capable of increasing the efficiency of a wide variety of tasks, ranging from reducing the spreading of diseases to better resource management in areas such as urban planning. In this work we estimate upper and lower limits on the predictability of human mobility to help assess the performance of competing algorithms. We do this using GPS traces from 604 individuals participating in a multi year long experiment, The Copenhagen Networks study. Earlier works, focusing on the prediction of a participant’s whereabouts in the next time bin, have found very high upper limits ({>}90%). We show that these upper limits are highly dependent on the choice of a spatiotemporal scales and mostly reflect stationarity, i.e. the fact that people tend to not move during small changes in time. This leads us to propose an alternative approach, which aims to predict the next location, rather than the location in the next bin. Our approach is independent of the temporal scale and introduces a natural length scale. By removing the effects of stationarity we show that the predictability of the next location is significantly lower (71%) than the predictability of the location in the next bin.
Highlights
The understanding of human mobility patterns has changed greatly in the last couple of decades
4 Conclusion Our results show that it is possible to extract a wide range of upper and lower limits of predictability of human mobility depending on the filtering and discretization scheme chosen
We have shown that the predictability at large spatial scales and small temporal scales mostly reflect stationarity, namely that people stay in the same spatial bin
Summary
The understanding of human mobility patterns has changed greatly in the last couple of decades. The main results from these studies have been the discoveries of power laws governing step size and wait time distributions [ ], a universal probability density governing human mobility [ ], and simple models capturing many statistical features of human mobility [ – ]. It has been explored how mobility is affected by recency [ ], exploration [ ], and return to previously visited places [ ] and friends [ ].
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.