Citywide Traffic Flow Prediction Based on Multiple Gated Spatio-temporal Convolutional Neural Networks

Cen Chen · Kenli Li · Sin G. Teo · Xiaofeng Zou · Keqin Li · Zeng Zeng

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

Abstract

Traffic flow prediction is crucial for public safety and traffic management, and remains a big challenge because of many complicated factors, e.g., multiple spatio-temporal dependencies, holidays, and weather. Some work leveraged 2D convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to explore spatial relations and temporal relations, respectively, which outperformed the classical approaches. However, it is hard for these work to model spatio-temporal relations jointly. To tackle this, some studies utilized LSTMs to connect high-level layers of CNNs, but left the spatio-temporal correlations not fully exploited in low-level layers. In this work, we propose novel spatio-temporal CNNs to extract spatio-temporal features simultaneously from low-level to high-level layers, and propose a novel gated scheme to control the spatio-temporal features that should be propagated through the hierarchy of layers. Based on these, we propose an end-to-end framework, multiple gated spatio-temporal CNNs (MGSTC), for citywide traffic flow prediction. MGSTC can explore multiple spatio-temporal dependencies through multiple gated spatio-temporal CNN branches, and combine the spatio-temporal features with external factors dynamically. Extensive experiments on two real traffic datasets demonstrates that MGSTC outperforms other state-of-the-art baselines.

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References

2003Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results 2014Urban Computing 2004Travel-Time Prediction With Support Vector Regression 2017Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction 2019Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction 1996Combining kohonen maps with arima time series models to forecast traffic flow 2006A Bayesian Network Approach to Traffic Flow Forecasting 2013Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning

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2021Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning 2021Traffic flow prediction models – A review of deep learning techniques 2021A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing 2023Artificial intelligence-based traffic flow prediction: a comprehensive review

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