Understanding and predicting human mobility is a key problem in different applications. Existing works on human mobility prediction mainly focus on the prediction of the next location (or a set of locations) that will be visited by the user in a specified time. These methods do not take into account the locations sequence and the departure times. In this research, given the historical data and the user’s trajectory in the first part of the current day (e.g trajectory in the morning), we introduce the concept of full trajectory prediction for the rest of the day (e.g. prediction of the trajectory in the afternoon). We emphasize that the full trajectory includes the sequence of the locations, the staying times, and the departure times. The proposed method has been examined on either labeled trajectories and geographical trajectories and the results show the effectiveness of the proposed method.
@inproceedings{DBLP:conf/huc/SadriSR17,
author = {Amin Sadri and
Flora Dilys Salim and
Yongli Ren},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/huc/SadriSR17.bib},
booktitle = {Adjunct Proceedings of the 2017 ACM International Joint Conference
on Pervasive and Ubiquitous Computing and Proceedings of the 2017
ACM International Symposium on Wearable Computers, UbiComp/ISWC
2017, Maui, HI, USA, September 11-15, 2017},
doi = {10.1145/3123024.3123140},
editor = {Seungyon Claire Lee and
Leila Takayama and
Khai N. Truong},
pages = {189--192},
publisher = {ACM},
timestamp = {Mon, 16 Sep 2019 01:00:00 +0200},
title = {Full trajectory prediction: what will you do the rest of the day?},
url = {https://doi.org/10.1145/3123024.3123140},
year = {2017}
}
© 2021 Flora Salim - CRUISE Research Group.