Multilayered LSTM with parameter transfer for vehicle speed data imputation

Jungmin Kwon; Chaeyeon Cha; Hyunggon Park

doi:10.1109/ISCAS51556.2021.9401352

Multilayered LSTM with parameter transfer for vehicle speed data imputation

Jungmin Kwon, Chaeyeon Cha, Hyunggon Park

Electronic and Electrical Engineering

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

4 Scopus citations

Abstract

In this paper, we propose a multilayered Long Short-Term Memory (LSTM) architecture with parameter transfer for a traffic speed data imputation in the vehicle to infrastructure (V2I) networks. We consider a scenario in V2I networks where a Road Side Unit (RSU) on the road cannot temporarily collect the traffic speed data because of its malfunction, which causes services that use the traffic speed data at the RSU to be unavailable. Therefore, it is imperative to develop an efficient and low complexity data imputation algorithm for uninterrupted and seamless V2I services. We propose an architecture that uses a multilayered LSTM (M-LSTM) network with parameter transfers, which can explicitly consider the characteristics of temporal dependency in the traffic speed data. The temporal dependency of the traffic speed data enables the parameters trained from each LSTM layer to be transferred to its adjacent LSTM layer and used for its parameter training, thereby significantly reducing the overall training and imputing complexity. Our simulation and experiment results confirm that the time for training and data imputation can be significantly reduced while maintaining imputation accuracy.

Original language	English
Title of host publication	2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728192017
DOIs	https://doi.org/10.1109/ISCAS51556.2021.9401352
State	Published - 2021
Event	53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Daegu, Korea, Republic of Duration: 22 May 2021 → 28 May 2021

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	2021-May
ISSN (Print)	0271-4310

Conference

Conference	53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021
Country/Territory	Korea, Republic of
City	Daegu
Period	22/05/21 → 28/05/21

Bibliographical note

Funding Information:
This work was supported in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00024, Supervised Agile Machine Learning Techniques for Network Automation based on Network Data Analytics Function) and supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF2020R1A2B5B01002528).

Publisher Copyright:
© 2021 IEEE

Keywords

Data imputation
Multilayered LSTM
Parameter transfer
Traffic speed data
V2I

Access to Document

10.1109/ISCAS51556.2021.9401352

Cite this

Kwon, J., Cha, C., & Park, H. (2021). Multilayered LSTM with parameter transfer for vehicle speed data imputation. In 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings Article 9401352 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2021-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS51556.2021.9401352

@inproceedings{d5528988ef834652aa05fe6565fd5c88,

title = "Multilayered LSTM with parameter transfer for vehicle speed data imputation",

abstract = "In this paper, we propose a multilayered Long Short-Term Memory (LSTM) architecture with parameter transfer for a traffic speed data imputation in the vehicle to infrastructure (V2I) networks. We consider a scenario in V2I networks where a Road Side Unit (RSU) on the road cannot temporarily collect the traffic speed data because of its malfunction, which causes services that use the traffic speed data at the RSU to be unavailable. Therefore, it is imperative to develop an efficient and low complexity data imputation algorithm for uninterrupted and seamless V2I services. We propose an architecture that uses a multilayered LSTM (M-LSTM) network with parameter transfers, which can explicitly consider the characteristics of temporal dependency in the traffic speed data. The temporal dependency of the traffic speed data enables the parameters trained from each LSTM layer to be transferred to its adjacent LSTM layer and used for its parameter training, thereby significantly reducing the overall training and imputing complexity. Our simulation and experiment results confirm that the time for training and data imputation can be significantly reduced while maintaining imputation accuracy.",

keywords = "Data imputation, Multilayered LSTM, Parameter transfer, Traffic speed data, V2I",

author = "Jungmin Kwon and Chaeyeon Cha and Hyunggon Park",

note = "Funding Information: This work was supported in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00024, Supervised Agile Machine Learning Techniques for Network Automation based on Network Data Analytics Function) and supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF2020R1A2B5B01002528). Publisher Copyright: {\textcopyright} 2021 IEEE; 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021 ; Conference date: 22-05-2021 Through 28-05-2021",

year = "2021",

doi = "10.1109/ISCAS51556.2021.9401352",

language = "English",

series = "Proceedings - IEEE International Symposium on Circuits and Systems",

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Kwon, J, Cha, C & Park, H 2021, Multilayered LSTM with parameter transfer for vehicle speed data imputation. in 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings., 9401352, Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2021-May, Institute of Electrical and Electronics Engineers Inc., 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021, Daegu, Korea, Republic of, 22/05/21. https://doi.org/10.1109/ISCAS51556.2021.9401352

Multilayered LSTM with parameter transfer for vehicle speed data imputation. / Kwon, Jungmin; Cha, Chaeyeon; Park, Hyunggon.
2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. 9401352 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2021-May).

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

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N1 - Funding Information: This work was supported in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00024, Supervised Agile Machine Learning Techniques for Network Automation based on Network Data Analytics Function) and supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF2020R1A2B5B01002528). Publisher Copyright: © 2021 IEEE

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N2 - In this paper, we propose a multilayered Long Short-Term Memory (LSTM) architecture with parameter transfer for a traffic speed data imputation in the vehicle to infrastructure (V2I) networks. We consider a scenario in V2I networks where a Road Side Unit (RSU) on the road cannot temporarily collect the traffic speed data because of its malfunction, which causes services that use the traffic speed data at the RSU to be unavailable. Therefore, it is imperative to develop an efficient and low complexity data imputation algorithm for uninterrupted and seamless V2I services. We propose an architecture that uses a multilayered LSTM (M-LSTM) network with parameter transfers, which can explicitly consider the characteristics of temporal dependency in the traffic speed data. The temporal dependency of the traffic speed data enables the parameters trained from each LSTM layer to be transferred to its adjacent LSTM layer and used for its parameter training, thereby significantly reducing the overall training and imputing complexity. Our simulation and experiment results confirm that the time for training and data imputation can be significantly reduced while maintaining imputation accuracy.

AB - In this paper, we propose a multilayered Long Short-Term Memory (LSTM) architecture with parameter transfer for a traffic speed data imputation in the vehicle to infrastructure (V2I) networks. We consider a scenario in V2I networks where a Road Side Unit (RSU) on the road cannot temporarily collect the traffic speed data because of its malfunction, which causes services that use the traffic speed data at the RSU to be unavailable. Therefore, it is imperative to develop an efficient and low complexity data imputation algorithm for uninterrupted and seamless V2I services. We propose an architecture that uses a multilayered LSTM (M-LSTM) network with parameter transfers, which can explicitly consider the characteristics of temporal dependency in the traffic speed data. The temporal dependency of the traffic speed data enables the parameters trained from each LSTM layer to be transferred to its adjacent LSTM layer and used for its parameter training, thereby significantly reducing the overall training and imputing complexity. Our simulation and experiment results confirm that the time for training and data imputation can be significantly reduced while maintaining imputation accuracy.

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DO - 10.1109/ISCAS51556.2021.9401352

M3 - Conference contribution

AN - SCOPUS:85109040770

T3 - Proceedings - IEEE International Symposium on Circuits and Systems

BT - 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021

Y2 - 22 May 2021 through 28 May 2021

ER -

Multilayered LSTM with parameter transfer for vehicle speed data imputation

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this