Origin-Destination Traffic Prediction based on Hybrid Spatio-Temporal Network

Predicting the Origin-Destination (OD) traffic is a fundamental problem and of great significance in transportation research and civil engineering. There are three expectations for a good OD traffic predictor: 1) higher accuracy; 2) longer horizon; 3) better applicability. This paper proposes a Hybr...

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Bibliographic Details
Main Authors: Chen, Tingyang, Nie, Lugang, Pan, Jiwei, Tu, Lai, Zheng, Bolong, Bai, Xiang
Format: Conference Proceeding
Language:English
Subjects:
Attention Mechanism
Automobiles
Civil engineering
Correlation
Data mining
Graph Convolutional Network
Hybrid Network
OD Traffic Prediction
Predictive models
Public transportation
Spatio-temporal Correlation
Urban areas
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Summary:Predicting the Origin-Destination (OD) traffic is a fundamental problem and of great significance in transportation research and civil engineering. There are three expectations for a good OD traffic predictor: 1) higher accuracy; 2) longer horizon; 3) better applicability. This paper proposes a Hybrid Spatio-Temporal Network (HSTN) model to predict OD traffic. The model emphasizes capturing more comprehensive spatial correlations among the sources of the traffic flows and temporal correlations between historical values and future prediction. HSTN is designed to have a Hybrid Spatial Module (HSM) and a Hybrid Temporal Module (HTM). HSM consists of three units to learn three types of spatial relationships and HTM consists of two units to quantity the influence of the input sequence on the target result. We evaluate HSTN on three real-world datasets of different travel modes in different cities. Results show that the proposed HSTN outperforms existing methods in both short-term and long-term predictions in all datasets.
ISSN:2374-8486
DOI:10.1109/ICDM54844.2022.00101