Effective Traffic Forecasting with Multi-Resolution Learning

Abstract

Traffic forecasting plays a vital role in traffic management systems. Recently, deep learning models have been applied to citywide traffic forecasting. However, the existing work models and predicts traffic at a single (dense) resolution, making it challenging to capture long-range spatial dependencies or high-level traffic dynamics. This shortcoming limits the accuracy of prediction and results in computationally expensive models. We propose a traffic forecasting model based on deep convolutional networks to improve the accuracy of citywide traffic forecasting. Our model uses a hierarchical architecture that captures traffic dynamics at multiple spatial resolutions. Based on this architecture, we apply a multi-task learning scheme, which trains the model to predict traffic at different resolutions. Our model helps provide a coherent understanding of traffic dynamics by capturing spatial dependencies between different regions of a city. Experimental results on multiple real datasets show that our model can achieve competitive results compared to complex state-of-the-art approaches while being more computationally efficient.