Predicting Citywide Road Traffic Flow Using Deep Spatiotemporal Neural Networks

Traffic flow forecasting has been a long-standing topic in intelligent transportation systems, and a renewed interest has been seen in recent years due to the development of artificial intelligence techniques. New deep neural networks have been developed to model traffic flow, but it is very challenging to predict citywide traffic flow at the road level in fine temporal scale owing to the influence of spatiotemporal dependencies and spatial sparsity. In this study, based on an in-depth analysis of traffic flow patterns, we propose a deep learning based spatiotemporal neural network model to predict citywide traffic flow for each road segment with high accuracy. Firstly, we examine the transformation of road network into its compact 2D image, where road segments correspond to pixels and their topological relationships are maintained in a large extent. Then, an end-to-end deep learning structure is designed to model traffic flow patterns. Specifically, recurrent convolutional network is employed to learn temporal dependencies and densely connected convolutional network is adopted to learn spatial dependencies and handle spatial sparsity. Our model attempts to aggregate the outputs of those hybrid networks using different weights, which is further enhanced by external information such as day of week. Experiments were conducted in Wuhan, China, where taxicab trajectory data were used to train and validate our model. When compared to current state of the art models, our model achieves higher accuracy in both single and multi-step traffic flow prediction tasks.

Automated summary

This study proposes a deep learning based spatiotemporal neural network model to predict citywide traffic flow for each road segment with high accuracy. The model transforms road network into a compact 2D image, uses recurrent convolutional network to learn temporal dependencies, and employs densely connected convolutional network to learn spatial dependencies and handle spatial sparsity.