Spatial Origin-Destination Flow Imputation Using Graph Convolutional Networks

Due to the limitation of data collection techniques and privacy issues, the problem of missing spatial origin-destination flows frequently occurs. Data imputation provides great support for the acquisition of complete flow data, which enables us to better understand regional connections and mobility...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 22; no. 12; pp. 7474 - 7484
Main Authors: Yao, Xin, Gao, Yong, Zhu, Di, Manley, Ed, Wang, Jiaoe, Liu, Yu
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Biological system modeling
Convolution
Data collection
data imputation
Data models
graph convolution
graph embedding
Graph neural networks
Gravity
Neural networks
Origin-destination flow
Predictive models
Sampling methods
Spatial databases
spatial interaction network
Structured data
Training
ISSN:1524-9050, 1558-0016
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Due to the limitation of data collection techniques and privacy issues, the problem of missing spatial origin-destination flows frequently occurs. Data imputation provides great support for the acquisition of complete flow data, which enables us to better understand regional connections and mobility patterns. However, existing models or approaches neglect the network structure of spatial flows, thus resulting in inappropriate estimates and a low performance. The development of graph neural networks offers a powerful tool to deal with graph-structured data. In this article, we proposed a spatial interaction graph convolutional network model, which combines graph convolution and a mapping function to predict flow data from the perspective of network learning. This model utilizes geographical unit embedding in local spatial networks to improve prediction accuracy. A negative sampling technique is adopted to reduce misestimation. Experiments on Beijing taxi trip data verified the usefulness of our model in spatial flow prediction. We also demonstrated that a biased training sample had a negative impact on the model's performance. More attributes of geographical units, a more proper negative sampling rate and a larger training set can increase the prediction accuracy of flow data.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2020.3003310