Trajectory Data Cleansing Using HMM

Qin Wang; Min Te Sun; Kazuya Sakai

doi:10.1109/ICPPW.2017.15

Trajectory Data Cleansing Using HMM

Qin Wang, Min Te Sun, Kazuya Sakai

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The vehicle trajectory dataset is often contaminated by GPS errors and low sampling rate. Consequently, it is important to cleanse the trajectory dataset before it can be used for any research or application. In this paper, we propose a HMM based system to cleanse and rebuild the missing traveling routes of vehicles in a given trajectory dataset. Considering the candidates of each entry as variables, a set of formulae for the transition probability and observation probability are proposed. The experiments using the OpenStreetMap of Beijing and the taxi trajectory dataset collected by Microsoft Research Lab, Asia confirm that our proposed system significantly improves the quality of the dataset.

Original language	English
Title of host publication	Proceedings - 46th International Conference on Parallel Processing Workshops, ICPPW 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	8-15
Number of pages	8
ISBN (Electronic)	9781538610442
DOIs	https://doi.org/10.1109/ICPPW.2017.15
State	Published - 5 Sep 2017
Event	46th International Conference on Parallel Processing Workshops, ICPPW 2017 - Bristol, United Kingdom Duration: 14 Aug 2017 → …

Publication series

Name	Proceedings of the International Conference on Parallel Processing Workshops
ISSN (Print)	1530-2016

Conference

Conference	46th International Conference on Parallel Processing Workshops, ICPPW 2017
Country/Territory	United Kingdom
City	Bristol
Period	14/08/17 → …

Keywords

Hidden Markov model
HMM
Trajectory data cleansing

Access to Document

10.1109/ICPPW.2017.15

Cite this

Wang, Q., Sun, M. T., & Sakai, K. (2017). Trajectory Data Cleansing Using HMM. In Proceedings - 46th International Conference on Parallel Processing Workshops, ICPPW 2017 (pp. 8-15). Article 8026063 (Proceedings of the International Conference on Parallel Processing Workshops). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPPW.2017.15

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title = "Trajectory Data Cleansing Using HMM",

abstract = "The vehicle trajectory dataset is often contaminated by GPS errors and low sampling rate. Consequently, it is important to cleanse the trajectory dataset before it can be used for any research or application. In this paper, we propose a HMM based system to cleanse and rebuild the missing traveling routes of vehicles in a given trajectory dataset. Considering the candidates of each entry as variables, a set of formulae for the transition probability and observation probability are proposed. The experiments using the OpenStreetMap of Beijing and the taxi trajectory dataset collected by Microsoft Research Lab, Asia confirm that our proposed system significantly improves the quality of the dataset.",

keywords = "Hidden Markov model, HMM, Trajectory data cleansing",

author = "Qin Wang and Sun, {Min Te} and Kazuya Sakai",

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Wang, Q, Sun, MT & Sakai, K 2017, Trajectory Data Cleansing Using HMM. in Proceedings - 46th International Conference on Parallel Processing Workshops, ICPPW 2017., 8026063, Proceedings of the International Conference on Parallel Processing Workshops, Institute of Electrical and Electronics Engineers Inc., pp. 8-15, 46th International Conference on Parallel Processing Workshops, ICPPW 2017, Bristol, United Kingdom, 14/08/17. https://doi.org/10.1109/ICPPW.2017.15

Trajectory Data Cleansing Using HMM. / Wang, Qin; Sun, Min Te; Sakai, Kazuya.
Proceedings - 46th International Conference on Parallel Processing Workshops, ICPPW 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 8-15 8026063 (Proceedings of the International Conference on Parallel Processing Workshops).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The vehicle trajectory dataset is often contaminated by GPS errors and low sampling rate. Consequently, it is important to cleanse the trajectory dataset before it can be used for any research or application. In this paper, we propose a HMM based system to cleanse and rebuild the missing traveling routes of vehicles in a given trajectory dataset. Considering the candidates of each entry as variables, a set of formulae for the transition probability and observation probability are proposed. The experiments using the OpenStreetMap of Beijing and the taxi trajectory dataset collected by Microsoft Research Lab, Asia confirm that our proposed system significantly improves the quality of the dataset.

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Trajectory Data Cleansing Using HMM

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