TY - GEN
T1 - A Joint Traffic Flow Estimation and Prediction Approach for Urban Networks
AU - Jiang, Ruiyuan
AU - Wang, Shangbo
AU - Zhang, Yuli
AU - Selis, Valerio
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Classical methods of traffic flow prediction with missing data are generally implemented in two sequential stages: a) imputing the missing data by certain imputation methods such as kNN, PPCA based methods etc.; b) using parametric or non-parametric methods to predict the future traffic flow with the completed data. However, the errors generated in missing data imputation stage will be accumulated into prediction stage, and thus will negatively influence the prediction performance when missing rate becomes large. To solve this problem, this paper proposes a Joint Traffic Flow Estimation and Prediction (JT-FEP) approach, which considers the missing data as additional unknown network parameters during a deep learning model training process. By updating missing data and the other network parameters via backward propagation, the model training error can generally be evenly distributed across the missing data and future data, thus reducing the error propagation. We conduct extensive experiments for two missing patterns i.e. Completely Missing at Random (CMAR) and Not Missing at Random (NMAR) with various missing rates. The experimental results demonstrate the superiority of JTFEP over existing methods.
AB - Classical methods of traffic flow prediction with missing data are generally implemented in two sequential stages: a) imputing the missing data by certain imputation methods such as kNN, PPCA based methods etc.; b) using parametric or non-parametric methods to predict the future traffic flow with the completed data. However, the errors generated in missing data imputation stage will be accumulated into prediction stage, and thus will negatively influence the prediction performance when missing rate becomes large. To solve this problem, this paper proposes a Joint Traffic Flow Estimation and Prediction (JT-FEP) approach, which considers the missing data as additional unknown network parameters during a deep learning model training process. By updating missing data and the other network parameters via backward propagation, the model training error can generally be evenly distributed across the missing data and future data, thus reducing the error propagation. We conduct extensive experiments for two missing patterns i.e. Completely Missing at Random (CMAR) and Not Missing at Random (NMAR) with various missing rates. The experimental results demonstrate the superiority of JTFEP over existing methods.
KW - CMAR
KW - JTFEP
KW - missing data
KW - NMAR
KW - traffic flow prediction
UR - http://www.scopus.com/inward/record.url?scp=85164131538&partnerID=8YFLogxK
U2 - 10.1109/PerComWorkshops56833.2023.10150266
DO - 10.1109/PerComWorkshops56833.2023.10150266
M3 - Conference Proceeding
AN - SCOPUS:85164131538
T3 - IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)
SP - 9
EP - 14
BT - 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023
Y2 - 13 March 2023 through 17 March 2023
ER -