Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 22, Issue 1, Pages 219-230Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2019.2955794
Keywords
Roads; Predictive models; Correlation; Gallium nitride; Data models; Deep learning; Forecasting; Traffic prediction; generative adversarial nets; deep learning
Categories
Funding
- National Key Research and Development Program of China [2017YFB0802303]
- Hong Kong Innovation and Technology Fund [ITP/024/18LP]
- National Natural Science Foundation of China [61672283, 61772270]
- Natural Science Foundation of Jiangsu Province of China [BK20171420]
- China Computer Federation (CCF)-Tencent Open Research Fund
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Research on traffic flow prediction has been increasing, with a new model called TrafficGAN proposed for network-scale deep traffic prediction using Generative Adversarial Networks. By embedding CNN and LSTM models in TrafficGAN, along with a deformable convolution kernel for better data handling, significant improvements in short-term traffic flow prediction for road networks were achieved.
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.
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