Smart City Gnosys

Smart city article details

Title	Traffic Speed Prediction With Missing Data Based On Tgcn
ID_Doc	58678
Authors	Ge L.; Li H.; Liu J.; Zhou A.
Year	2019
Published	Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
DOI	http://dx.doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00130
Abstract	Traffic speed prediction is an important part of intelligent transportation systems (ITS). This paper proposes a novel approach for traffic speed prediction with missing data. We use Temporal Graph Convolutional Networks (TGCN) which integrates spatio-temporal component and external component to capture the dependencies between traffic speed and various influence factors including road structure, POI and social factors. Meanwhile, there usually exist missing values in the traffic speed data, we use the tensor decomposition method to impute the missing values. Experiments show that the proposed TGCN model outperforms state-of-the-art baselines and tensor decomposition method can improve the prediction performance of TGCN. © 2019 IEEE.
Author Keywords	Graph convolution; Intelligent transportation systems; Missing data imputation; Spatio-temporal data prediction

Similar Articles

Id	Similarity	Authors	Title	Published
58675	0.895	Pan X.; Hou F.; Li S.	Traffic Speed Prediction Based On Time Classification In Combination With Spatial Graph Convolutional Network	IEEE Transactions on Intelligent Transportation Systems, 24, 8 (2023)
52566	0.894	Chen J.; Yang L.; Yang Y.; Peng L.; Ge X.	Spatio-Temporal Graph Neural Networks For Missing Data Completion In Traffic Prediction	International Journal of Geographical Information Science, 39, 5 (2025)
29827	0.894	Chen B.; Hu K.; Li Y.; Miao L.	Hybrid Spatio-Temporal Graph Convolution Network For Short-Term Traffic Forecasting	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2022-October (2022)
5615	0.892	Jiang M.; Liu Z.; Xu Y.	A Traffic Prediction Method For Missing Data Scenarios: Graph Convolutional Recurrent Ordinary Differential Equation Network	Complex and Intelligent Systems, 11, 2 (2025)
8054	0.891	Zhang C.; Zhang S.; James Y.J.Q.; Yu S.	An Enhanced Motif Graph Clustering-Based Deep Learning Approach For Traffic Forecasting	Proceedings - IEEE Global Communications Conference, GLOBECOM (2020)
29828	0.886	Dai R.; Xu S.; Gu Q.; Ji C.; Liu K.	Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction With Navigation Data	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2020)
21123	0.885	Hu J.; Lin X.; Wang C.	Dstgcn: Dynamic Spatial-Temporal Graph Convolutional Network For Traffic Prediction	IEEE Sensors Journal, 22, 13 (2022)
47127	0.883	Liu Y.; Rasouli S.; Wong M.; Feng T.; Huang T.	Rt-Gcn: Gaussian-Based Spatiotemporal Graph Convolutional Network For Robust Traffic Prediction	Information Fusion, 102 (2024)
58677	0.883	Lian P.; Li Y.; Liu B.; Feng X.	Traffic Speed Prediction Using Multivariate Time Series Dynamic Graph Neural Network; [基于多元时间序列动态图神经网络的交通速度预测]	Journal of Geo-Information Science, 27, 3 (2025)
58588	0.881	Wang X.	Traffic Flow Prediction Method Based On Deep Learning And Graph Neural Network	International Journal of High Speed Electronics and Systems (2025)