TY - GEN
T1 - Bidirectional imputation of spatio-temporal data based on LSTM with parameter transfer
AU - Kwon, Jungmin
AU - Cha, Chaeyeon
AU - Park, Hyunggon
N1 - Funding Information:
This work was supported 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 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 bidirectional imputation algorithm for spatio-temporal traffic speed data based on Long Short-Term Memory (LSTM) architecture with parameter transfer in vehicle to infrastructure (V2I) networks. We consider a scenario in V2I networks where an Road Side Units (RSU) on the road does not operate temporarily and thus the traffic speed data cannot be collected. This makes any services that rely on the traffic speed data at the RSU be unavailable. For uninterrupted and seamless V2I services, an efficient and low complexity data imputation algorithm is imperative. The proposed algorithm is based on the architecture that includes multiple LSTM layers with parameter transfers, which can explicitly take into account the spatio-temporal characteristics of the traffic speed data. By transferring parameters from one LSTM layer to its adjacent LSTM layer, the complexity associated with the algorithm can be significantly reduced. The proposed algorithm includes bidirectional imputation, which can further improve imputation accuracy. Our simulation and experiment results confirm that the time for training and data imputation of the proposed algorithm can be significantly reduced while maintaining imputation accuracy.
AB - In this paper, we propose a bidirectional imputation algorithm for spatio-temporal traffic speed data based on Long Short-Term Memory (LSTM) architecture with parameter transfer in vehicle to infrastructure (V2I) networks. We consider a scenario in V2I networks where an Road Side Units (RSU) on the road does not operate temporarily and thus the traffic speed data cannot be collected. This makes any services that rely on the traffic speed data at the RSU be unavailable. For uninterrupted and seamless V2I services, an efficient and low complexity data imputation algorithm is imperative. The proposed algorithm is based on the architecture that includes multiple LSTM layers with parameter transfers, which can explicitly take into account the spatio-temporal characteristics of the traffic speed data. By transferring parameters from one LSTM layer to its adjacent LSTM layer, the complexity associated with the algorithm can be significantly reduced. The proposed algorithm includes bidirectional imputation, which can further improve imputation accuracy. Our simulation and experiment results confirm that the time for training and data imputation of the proposed algorithm can be significantly reduced while maintaining imputation accuracy.
KW - Bidirectional imputation
KW - LSTM
KW - Parameter transfer
KW - Spatio-temporal data
KW - V2I
UR - http://www.scopus.com/inward/record.url?scp=85119373831&partnerID=8YFLogxK
U2 - 10.1109/WCNC49053.2021.9417392
DO - 10.1109/WCNC49053.2021.9417392
M3 - Conference contribution
AN - SCOPUS:85119373831
T3 - IEEE Wireless Communications and Networking Conference, WCNC
BT - 2021 IEEE Wireless Communications and Networking Conference, WCNC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Wireless Communications and Networking Conference, WCNC 2021
Y2 - 29 March 2021 through 1 April 2021
ER -