TrafficGAN: Network-Scale Deep Traffic Prediction With Generative Adversarial Nets

Traffic flow prediction has received rising research interest recently since it is a key step to prevent and relieve traffic congestion in urban areas. Existing methods mostly focus on road-level or region-level traffic prediction, and fail to deeply capture the high-order spatial-temporal correlations among the road links to perform a road network-level prediction. In this paper, we propose a network-scale deep traffic prediction model called TrafficGAN, in which Generative Adversarial Nets (GAN) is utilized to predict traffic flows under an adversarial learning framework. To capture the spatial-temporal correlations among the road links of a road network, both Convolutional Neural Nets (CNN) and Long-Short Term Memory (LSTM) models are embedded into TrafficGAN. In addition, we also design a deformable convolution kernel for CNN to make it better handle the input road network data. We extensively evaluate our proposal over two large GPS probe datasets in the arterial road network of downtown Chicago and Bay Area of California. The results show that TrafficGAN significantly outperforms both traditional statistical models and state-of-the-art deep learning models in network-scale short-term traffic flow prediction.

I. Introduction

Traffic congestion has become an ubiquitous problem for many big cities in both developed and developing countries, which can result in various issues including decreasing the road traffic capacity, slowing down the vehicle speed and increasing the air pollution. Currently, Intelligent Transportation System (ITS) has been widely adopted to improve traffic conditions. As one important goal in ITS, accurately predicting traffic conditions in real time can help the drivers better plan their routes, shorten travel time and improve traffic conditions, and thus effectively alleviate the issues of air pollution, energy consumption and traffic accidents. Therefore, the study of traffic flow prediction is of great importance in ITS and has attracted increasing research interest recently.