research-article

Dynamic Multi-View Graph Neural Networks for Citywide Traffic Inference

Authors:

Junyu DongAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 4

Article No.: 53, Pages 1 - 22

https://doi.org/10.1145/3564754

Published: 24 February 2023 Publication History

Abstract

Accurate citywide traffic inference is critical for improving intelligent transportation systems with smart city applications. However, this task is very challenging given the limited training data, due to the high cost of sensor installment and maintenance across the entire urban space. A more practical scenario to study the citywide traffic inference is effectively modeling the spatial and temporal traffic patterns with limited historical traffic observations. In this work, we propose a dynamic multi-view graph neural network for citywide traffic inference with the method CTVI+. Specifically, for the temporal dimension, we propose a temporal self-attention mechanism that is capable of learning the dynamics of traffic data with the time-evolving traffic volume variations. For spatial dimension, we build a multi-view graph neural network, employing the road-wise message passing scheme to capture the region dependencies. With the designed spatial-temporal learning paradigms, we enable our traffic inference model to encode the dynamism from both spatial and temporal traffic patterns, which is reflective of intra- and inter-road traffic correlations. In our evaluation, CTVI+ achieves consistent better performance compared with different baselines on real-world traffic volume datasets. Further ablation study validates the effectiveness of key components in CTVI+. We release the model implementation at https://github.com/dsj96/TKDD.

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Cited By

Liu KZhu YWang XJi HHuang C(2024)Spatio-Temporal Graph Attention Convolution Network for Traffic Flow ForecastingTransportation Research Record: Journal of the Transportation Research Board10.1177/03611981231225208Online publication date: 30-Jan-2024
https://doi.org/10.1177/03611981231225208
Diao ZWang XZhang DXie GChen JPei CMeng XXie KZhang G(2024)DMSTG: Dynamic Multiview Spatio-Temporal Networks for Traffic ForecastingIEEE Transactions on Mobile Computing10.1109/TMC.2023.332803823:6(6865-6880)Online publication date: Jul-2024
https://doi.org/10.1109/TMC.2023.3328038
Chen KLiang YHan JFeng SZhu MYang H(2024)Semantic-fused multi-granularity cross-city traffic predictionTransportation Research Part C: Emerging Technologies10.1016/j.trc.2024.104604162(104604)Online publication date: May-2024
https://doi.org/10.1016/j.trc.2024.104604
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Index Terms

Dynamic Multi-View Graph Neural Networks for Citywide Traffic Inference

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 17, Issue 4

May 2023

364 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3583065

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 24 February 2023

Online AM: 27 September 2022

Accepted: 04 September 2022

Revised: 03 August 2022

Received: 03 May 2022

Published in TKDD Volume 17, Issue 4

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National Natural Science Foundation of China
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National Key Research and Development Program of China

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Cited By

Liu KZhu YWang XJi HHuang C(2024)Spatio-Temporal Graph Attention Convolution Network for Traffic Flow ForecastingTransportation Research Record: Journal of the Transportation Research Board10.1177/03611981231225208Online publication date: 30-Jan-2024
https://doi.org/10.1177/03611981231225208
Diao ZWang XZhang DXie GChen JPei CMeng XXie KZhang G(2024)DMSTG: Dynamic Multiview Spatio-Temporal Networks for Traffic ForecastingIEEE Transactions on Mobile Computing10.1109/TMC.2023.332803823:6(6865-6880)Online publication date: Jul-2024
https://doi.org/10.1109/TMC.2023.3328038
Chen KLiang YHan JFeng SZhu MYang H(2024)Semantic-fused multi-granularity cross-city traffic predictionTransportation Research Part C: Emerging Technologies10.1016/j.trc.2024.104604162(104604)Online publication date: May-2024
https://doi.org/10.1016/j.trc.2024.104604
Zhang XGong YZhang CWu XGuo YLu WZhao LDong X(2024)Spatio-temporal fusion and contrastive learning for urban flow predictionKnowledge-Based Systems10.1016/j.knosys.2023.111104282:COnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.knosys.2023.111104
Zhou XWang JWang JGuan Q(2024)Predicting air quality using a multi-scale spatiotemporal graph attention networkInformation Sciences10.1016/j.ins.2024.121072680(121072)Online publication date: Oct-2024
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Chen ZLin S(2024)A binary-domain recurrent-like architecture-based dynamic graph neural networkAutonomous Intelligent Systems10.1007/s43684-024-00067-94:1Online publication date: 25-Jun-2024
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Wang JYan BYu Y(2022)Citywide Traffic Volume Inference using Traffic Sensing Data with Missing Values2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)10.1109/CCIS57298.2022.10016336(720-724)Online publication date: 26-Nov-2022
https://doi.org/10.1109/CCIS57298.2022.10016336

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