A Demonstration of GTI: A Scalable Graph-based Trajectory Imputation

被引：0
|
作者：
Isufaj, Keivin [1 ]
Choghari, Jade [2 ]
Elshrif, Mohamed M. [1 ]
机构：
[1] HBKU, QCRI, Ar Rayyan, Qatar
[2] Univ Waterloo, Waterloo, ON, Canada
来源：
31ST ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS, ACM SIGSPATIAL GIS 2023 | 2023年
关键词：
Trajectory Imputation; GPS; Spatial Data; Road Network; GTI;
D O I：
10.1145/3589132.3625633
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 di.erent scenarios that show how GTI can be used and customized to improve the quality of trajectory data in their corresponding spatial and temporal aspects.
引用
收藏
页码：468 / 471
页数：4
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