Pattern-adaptive generative adversarial network with sparse data for traffic state estimation

Accurate traffic state estimation is vital basis for traffic control and management applications. However, owing to the multi-modality of traffic state patterns, the estimation methods are hard to adapt to the state variation over the urban roads and time periods. Especially, the sparse sampling of traffic state makes the traffic pattern recognition easy to bias and increases uncertainty in the estimation. To address the problem of multi-modal traffic state estimation under sparse data, we propose a pattern-adaptive generative adversarial network, named PA-GAN. In the PA-GAN, the Bayesian Inference is introduced to place multi-modal posterior distributions over the network parameters. Therefore, to targeted state estimation for each pattern, the posterior sampling will adaptively activate the corresponding parameters of each traffic pattern according to the context features. Then, the PA-GAN learns traffic patterns from sparse sampling data by the traffic state generator and the discriminator, in which an error-feedback mechanism uses multi-level traffic features to correct the estimation under an encoder–decoder framework. To evaluate the proposed PA-GAN, we use two real-world datasets to conduct comprehensive case studies about multi-modal traffic patterns and sparse sampling. The experimental results demonstrate that the PA-GAN can outperform other estimation methods and that the Bayesian Inference can improve the adaptability ability of the learning network to various traffic states.