research-article

Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction

Authors:

Deshuang HuangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 13, Issue 2

Article No.: 19, Pages 1 - 21

https://doi.org/10.1145/3510829

Published: 11 January 2022 Publication History

Abstract

Traffic flow prediction is the upstream problem of path planning, intelligent transportation system, and other tasks. Many studies have been carried out on the traffic flow prediction of the spatio-temporal network, but the effects of spatio-temporal flexibility (historical data of the same type of time intervals in the same location will change flexibly) and spatio-temporal correlation (different road conditions have different effects at different times) have not been considered at the same time. We propose the Deep Spatio-temporal Adaptive 3D Convolution Neural Network (ST-A3DNet), which is a new scheme to solve both spatio-temporal correlation and flexibility, and consider spatio-temporal complexity (complex external factors, such as weather and holidays). Different from other traffic forecasting models, ST-A3DNet captures the spatio-temporal relationship at the same time through the Adaptive 3D convolution module, assigns different weights flexibly according to the influence of historical data, and obtains the impact of external factors on the flow through the ex-mask module. Considering the holidays and weather conditions, we train our model for experiments in Xi’an and Chengdu. We evaluate the ST-A3DNet and the results show that we have better results than the other 11 baselines.

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https://doi.org/10.1109/TITS.2024.3486963
Wen YXu (ato) WZhang W(2025)A spatial-temporal hierarchical modeling framework for multi-step ride-hailing demand forecastingInternational Journal of General Systems10.1080/03081079.2024.2447491(1-21)Online publication date: 15-Jan-2025
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Index Terms

Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction
1. Applied computing
  1. Operations research
    1. Transportation
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 13, Issue 2

April 2022

392 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3508464

Editor:
Huan Liu
Arizona State University, USA

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Publication History

Published: 11 January 2022

Accepted: 01 May 2021

Revised: 01 March 2021

Received: 01 November 2020

Published in TIST Volume 13, Issue 2

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National Natural Science Foundation of China
Natural Science Foundation of Shaanxi Province

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https://doi.org/10.1109/TITS.2024.3486963
Wen YXu (ato) WZhang W(2025)A spatial-temporal hierarchical modeling framework for multi-step ride-hailing demand forecastingInternational Journal of General Systems10.1080/03081079.2024.2447491(1-21)Online publication date: 15-Jan-2025
https://doi.org/10.1080/03081079.2024.2447491
Liu HLiu QYang ZDu J(2025)TDMixer: Lightweight Long-Term Series Forecasting using Time-Continuous Embedding and Magnitude DecompositionDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_14(211-226)Online publication date: 11-Jan-2025
https://doi.org/10.1007/978-981-97-5779-4_14
Dai GKong WLiu YZhang BPeng XFan XHuang H(2024)Tucker Decomposition Enhanced Dynamic Graph Convolutional Networks for Crowd Flows PredictionACM Transactions on Intelligent Systems and Technology10.1145/3706116Online publication date: 2-Dec-2024
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