Traffic Inflow and Outflow Forecasting by Modeling Intra- and Inter-Relationship Between Flows

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Traffic Inflow and Outflow Forecasting by Modeling Intra- and Inter-Relationship Between Flows

Zhao, Yiji / Lin, Youfang / Zhang, Yongkai / Wen, Haomin / Liu, Yunxiao / Wu, Hao / Wu, Zhihao / Zhang, Shuaichao / Wan, Huaiyu

Abstract

Forecasting traffic inflows and outflows is crucial for intelligent transportation applications such as traffic management and risk assessment. Recently, deep learning models, which focus on capturing spatio-temporal correlations between stations (locations) by constructing Spatio-Temporal Feature Learners (STFL), have achieved promising performance in traffic inflows and outflows prediction. However, two unresolved issues limit the performance of these models. i) dynamic and heterogeneous intra- and inter-relationships between flows are ignored, and ii) the STFL in these models cannot capture the global information. To address the above issues, we propose a novel deep Spatio-Temporal Network framework based on Multi-Relational learning (MR-STN) for predicting traffic inflows and outflows. Specifically, a multi-relational learning module is designed to comprehensively model three kinds of relationships between flows while extracting diverse spatio-temporal features. In this module, an enhanced STFL is developed to capture both local and global information. Then, a feature fusion module is introduced to extract fused features for inflows and outflows respectively via a gated fusion mechanism. On this basis, the prediction module uses fusion features to generate future inflows and outflows. Finally, we implement the proposed framework with four state-of-the-art graph-based deep spatio-temporal models to demonstrate its generality and superiority. Extensive experiments on three datasets show that the proposed framework can significantly boost the performance of existing models.

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Document information

Title :

Traffic Inflow and Outflow Forecasting by Modeling Intra- and Inter-Relationship Between Flows

Contributors:

Zhao, Yiji (author) / Lin, Youfang (author) / Zhang, Yongkai (author) / Wen, Haomin (author) / Liu, Yunxiao (author) / Wu, Hao (author) / Wu, Zhihao (author) / Zhang, Shuaichao (author) / Wan, Huaiyu (author)

Published in:

IEEE Transactions on Intelligent Transportation Systems ; 23 , 11 ; 20202-20216

Publication date :

2022-11-01

Size :

3948794 byte

ISSN :

1524-9050 , 1558-0016

DOI :

https://doi.org/10.1109/TITS.2022.3187121

Type of media :

Article (Journal)