A Delay-Based Deep Learning Approach for Urban Traffic Volume Prediction

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Abstract
Reliable traffic flow prediction can greatly support the Intelligent Transportation System (ITS) to generate more effective traffic management decisions. Previous volume predictions mainly focused on the single road with simple flow patterns, such as suburban highways. However, with the development of the urban transportation system, the reliable flow information support becomes more significant for forming a solid ITS. Besides, travel delay is another widely neglected problem but can affect the prediction result significantly. Specifically, vehicles need some time to move from one place to another, and this time is called the travel delay. For further enhancing the prediction performance under the urban scenario, we propose a delay-based deep learning framework (MDGRU) to improve the accuracy of the short-term traffic flow prediction, in which travel delay is handled in the form of a weighted matrix enrolled into a multivariate input stacked Recurrent Neural Network (RNN). Multivariate input makes this approach has a stronger mining ability for spatial relationships capture, and the stacked structure leads to a more accurate pattern learning process. The results show that our approach is accurate and reliable.

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