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, the authors 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, the authors also design a deformable convolution kernel for CNN to make it better handle the input road network data. They extensively evaluate their 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.


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  • Accession Number: 01766310
  • Record Type: Publication
  • Files: TLIB, TRIS
  • Created Date: Jan 5 2021 2:01PM