@inproceedings{3e9348d2eda54608811c1dd649bbaa42,
title = "A Demonstration of GTI: A Scalable Graph-based Trajectory Imputation (Demo Paper)",
abstract = "This demo presents GTI; a graph-based trajectory imputation framework that aims to impute sparse trajectory datasets to boost their accuracy. GTI can act as a pre-processing step to increase the accuracy of any trajectory data management system or trajectory-based application. Unlike the large majority of existing trajectory imputation frameworks, GTI assumes that the underlying road network is not available. Audience will be able to interact with GTI through different scenarios that show how GTI can be used and customized to improve the quality of trajectory data in their corresponding spatial and temporal aspects.",
keywords = "GPS, GTI, road network, spatial data, trajectory imputation",
author = "Keivin Isufaj and Jade Choghari and Elshrif, {Mohamed Mokhtar}",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023 ; Conference date: 13-11-2023 Through 16-11-2023",
year = "2023",
month = nov,
day = "13",
doi = "10.1145/3589132.3625633",
language = "English",
series = "GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems",
publisher = "Association for Computing Machinery",
editor = "Damiani, {Maria Luisa} and Matthias Renz and Ahmed Eldawy and Peer Kroger and Nascimento, {Mario A.}",
booktitle = "31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023",
address = "United States",
}