A Spatial-Temporal Approach for Multi-Airport Traffic Flow Prediction Through Causality Graphs

Accurate airport traffic flow estimation is crucial for the secure and orderly operation of the aviation system. Recent advances in machine learning have achieved promising prediction results in the single-airport scenario. However, these works overlook the variational spatial interactions hidden am...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2024-01, Vol.25 (1), p.1-13
Main Authors: Du, Wenbo, Chen, Shenwen, Li, Zhishuai, Cao, Xianbin, Lv, Yisheng
Format: Article
Language:English
Subjects:
Ablation
Adaptation models
Airport security
Airport traffic flow
Airports
Atmospheric modeling
Aviation
Causality
causality graph
Data mining
Deep learning
Feature extraction
Graphs
Heterogeneity
Machine learning
Modules
Prediction algorithms
Predictive models
Spatial data
spatiotemporal analysis
Traffic flow
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:Accurate airport traffic flow estimation is crucial for the secure and orderly operation of the aviation system. Recent advances in machine learning have achieved promising prediction results in the single-airport scenario. However, these works overlook the variational spatial interactions hidden among airports and show limited performances on the traffic flow prediction task for the aviation system which is composed of several airports. In this paper, we consider the multi-airport scenario and propose a novel spatio-temporal hybrid deep learning model to efficiently capture spatial correlations as well as temporal dependencies in a parallelized way. Specifically, we introduce the causal inference among airports to model their interactions and thus construct adaptive causality graphs in a data-driven manner to address the heterogeneity of airports. Furthermore, given that multi-source features are not applicable for all airports, a feature mask module is designated to adaptively select the features in spatial information mining. Extensive experiments are conducted on the real data of top-30 busiest airports in China. The results show that our spatio-temporal deep learning approach is superior to state-of-the-art methodologies and the improvement ratio is up to 4.7% against benchmarks. Ablation studies emphasize the power of the proposed adaptive causality graph and the feature mask module. All of these prove the effectiveness of the proposed methodology.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3308903