Spatial-temporal similarity for trajectories with location noise and sporadic sampling

Guanyao Li, Chih Chieh Hung, Mengyun Liu, Linfei Pan, Wen-Chih Peng, S. H.Gary Chan

研究成果: Conference contribution同行評審

9 引文 斯高帕斯(Scopus)

摘要

With the rapid advances and the penetration of the Internet of Things and sensors, a massive amount of trajectory data, given by discrete locations at certain timestamps, have been extracted or collected. Knowing the similarity between trajectories is fundamental to understanding their spatial-temporal correlation, with direct and far-reaching applications in contact tracing, companion detection, personalized marketing, etc. In this work, we consider the general and realistic sensing scenario that the locations of the trajectories may be noisy, and that these trajectories are sporadically sampled with randomness and asynchrony from the underlying continuous paths. Most of the prior work on trajectory similarity has not sufficiently considered the temporal dimension, or the issues of location noise and sporadic sampling, while others have limitations of strong assumptions such as a fixed known speed of users or the availability of a large amount of training data.We propose a novel and effective spatial-temporal measure termed STS (Spatial-Temporal Similarity) to evaluate the spatial-temporal overlap between any two trajectories. In order to account for the location noise and sporadic sampling, STS models each location in a trajectory as an observable outcome drawn from a probability distribution. With that, it efficiently reduces the need for training data by estimating a personalized spatial-temporal probability distribution of the object position from its own trajectory. Based on that, it subsequently computes the co-location probability and hence derives the similarity of any two trajectories. We have conducted extensive experiments to evaluate STS using real large-scale indoor (mall) and outdoor (taxi) datasets. Our results show that STS is substantially more accurate and robust than the state-of-the-art approaches, with an improvement of 63% on precision and 85% on mean rank.

原文English
主出版物標題Proceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
發行者IEEE Computer Society
頁面1224-1235
頁數12
ISBN(電子)9781728191843
DOIs
出版狀態Published - 4月 2021
事件37th IEEE International Conference on Data Engineering, ICDE 2021 - Virtual, Chania, Greece
持續時間: 19 4月 202122 4月 2021

出版系列

名字Proceedings - International Conference on Data Engineering
2021-April
ISSN(列印)1084-4627

Conference

Conference37th IEEE International Conference on Data Engineering, ICDE 2021
國家/地區Greece
城市Virtual, Chania
期間19/04/2122/04/21

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