TY - GEN
T1 - Multilayered LSTM with parameter transfer for vehicle speed data imputation
AU - Kwon, Jungmin
AU - Cha, Chaeyeon
AU - Park, Hyunggon
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
PY - 2021
Y1 - 2021
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.
KW - Data imputation
KW - Multilayered LSTM
KW - Parameter transfer
KW - Traffic speed data
KW - V2I
UR - http://www.scopus.com/inward/record.url?scp=85109040770&partnerID=8YFLogxK
U2 - 10.1109/ISCAS51556.2021.9401352
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 -