Bidirectional Spatial-Temporal Adaptive Transformer for Urban Traffic Flow Forecasting

Changlu Chen · Yanbin Liu · Ling Chen · Chengqi Zhang

186 citations0/0 files storedIEEE Transactions on Neural Networks and Learning Systems

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Traffic Prediction and Management Techniques Traffic control and management Air Quality Monitoring and Forecasting

Abstract

Urban traffic forecasting is the cornerstone of the intelligent transportation system (ITS). Existing methods focus on spatial-temporal dependency modeling, while two intrinsic properties of the traffic forecasting problem are overlooked. First, the complexity of diverse forecasting tasks is nonuniformly distributed across various spaces (e.g., suburb versus downtown) and times (e.g., rush hour versus off-peak). Second, the recollection of past traffic conditions is beneficial to the prediction of future traffic conditions. Based on these properties, we propose a bidirectional spatial-temporal adaptive transformer (Bi-STAT) for accurate traffic forecasting. Bi-STAT adopts an encoder-decoder architecture, where both the encoder and the decoder maintain a spatial-adaptive transformer and a temporal-adaptive transformer structure. Inspired by the first property, each transformer is designed to dynamically process the traffic streams according to their task complexities. Specifically, we realize this by the recurrent mechanism with a novel dynamic halting module (DHM). Each transformer performs iterative computation with shared parameters until DHM emits a stopping signal. Motivated by the second property, Bi-STAT utilizes one decoder to perform the present → past recollection task and the other decoder to perform the present → future prediction task. The recollection task supplies complementary information to assist and regularize the prediction task for a better generalization. Through extensive experiments, we show the effectiveness of each module in Bi-STAT and demonstrate the superiority of Bi-STAT over the state-of-the-art baselines on four benchmark datasets. The code is available at https://github.com/chenchl19941118/Bi-STAT.git.

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References

2018Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 2019Graph WaveNet for Deep Spatial-Temporal Graph Modeling 2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting 2019Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting 2017Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction 2003Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results 2020GMAN: A Graph Multi-Attention Network for Traffic Prediction 2020Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting

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