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Road Travel Time Prediction Based on Improved Graph Convolutional Network
- Source :
- Mobile Information Systems, Vol 2021 (2021)
- Publication Year :
- 2021
- Publisher :
- Hindawi Limited, 2021.
-
Abstract
- Travel time prediction is playing an increasingly important part in advanced traveler information system (ATIS), which is of great significance to alleviate urban traffic congestion. Although graph convolutional networks have been widely used in road network traffic prediction, spatiotemporal dynamic modeling of urban traffic is still an intractable task. In this study, we propose an improved graph convolutional network (IGC-Net) for travel time prediction. Specifically, we design a modified adjacency matrix by fusing distance and correlation matrix with original adjacency matrix to capture spatial dynamic feature. We then establish three components based on temporal property to capture recent, daily-periodic, and weekly periodic correlations. The comparison experiments with baseline models and variants on a real-world dataset in Beijing are conducted. The results show that the IGC-Net outperforms baseline models in different prediction horizons and has stronger robustness for dynamic traffic prediction.
- Subjects :
- Article Subject
Computer Networks and Communications
Computer science
Covariance matrix
TK5101-6720
computer.software_genre
Computer Science Applications
System dynamics
Traffic congestion
Robustness (computer science)
Information system
Telecommunication
Graph (abstract data type)
Adjacency matrix
Data mining
Baseline (configuration management)
computer
Subjects
Details
- Language :
- English
- Volume :
- 2021
- Database :
- OpenAIRE
- Journal :
- Mobile Information Systems
- Accession number :
- edsair.doi.dedup.....0ef0439a06b13a0365ce05395a4723d1