A traffic prediction method for missing data scenarios: graph convolutional recurrent ordinary differential equation network
Abstract Traffic prediction plays an increasingly important role in intelligent transportation systems and smart cities. Both travelers and urban managers rely on accurate traffic information to make decisions about route selection and traffic management. Due to various factors, both human and natur...
Saved in:
Main Authors: | Ming Jiang, Zhiwei Liu, Yan Xu |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2025-01-01
|
Series: | Complex & Intelligent Systems |
Subjects: | |
Online Access: | https://doi.org/10.1007/s40747-024-01768-7 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
D-MGDCN-CLSTM: A Traffic Prediction Model Based on Multi-Graph Gated Convolution and Convolutional Long–Short-Term Memory
by: Linliang Zhang, et al.
Published: (2025-01-01) -
sAMDGCN: sLSTM-Attention-Based Multi-Head Dynamic Graph Convolutional Network for Traffic Flow Forecasting
by: Shiyuan Zhang, et al.
Published: (2025-01-01) -
Comparative Forecasting of Some Key Economic Indicators Using Artificial Neural Networks and Ordinary Differential Equations: A Case Study of the Turkish Economy
by: Bahatdin Daşbaşı, et al.
Published: (2024-12-01) -
A Network Traffic Prediction Model Based on Layered Training Graph Convolutional Network
by: Yulian Li, et al.
Published: (2025-01-01) -
Asymptotic relationships between two higher order ordinary differential equations
by: Takasi Kusano
Published: (1983-01-01)