Meta-MSNet: Meta-Learning Based Multi-Source Data Fusion for Traffic Flow Prediction

Traffic flow prediction is a challenging task while most existing works are faced with two main problems in extracting complicated intrinsic and extrinsic features. In terms of intrinsic features, current methods don't fully exploit different functions of short-term neighboring and long-term pe...

Full description

Saved in:
Bibliographic Details
Published in:IEEE signal processing letters 2021, Vol.28, p.6-10
Main Authors: Fang, Shen, Pan, Xianbing, Xiang, Shiming, Pan, Chunhong
Format: Article
Language:English
Subjects:
Coders
Convolution
Data fusion
Data integration
Decoding
deep learning
Encoders-Decoders
Feature extraction
graph convolution
Learning
meta-learning
Predictive models
Task analysis
Traffic flow
traffic flow prediction
traffic network
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Traffic flow prediction is a challenging task while most existing works are faced with two main problems in extracting complicated intrinsic and extrinsic features. In terms of intrinsic features, current methods don't fully exploit different functions of short-term neighboring and long-term periodic temporal patterns. As for extrinsic features, recent works mainly employ hand-crafted fusion strategies to integrate external factors but remain generalization issues. To solve these problems, we propose a meta-learning based multi-source spatio-temporal network (Meta-MSNet). The Meta-MSNet is designed with an encoder-decoder structure. The encoder captures neighboring temporal dependencies while the decoder extracts periodic features. Furthermore, two meta-learning based fusion modules are designed to integrate multi-source external data both on temporal and spatial dimensions. Experiments on three real-world traffic datasets have verified the superiority of the proposed model.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2020.3037527