Abstract: For the issue of missing data occurring in the data collection process of intelligent transportation systems, a road network traffic data recovery model is proposed in this paper based on the dynamic generative adversarial network. Firstly, the approach in this paper are constructed various missing traffic data matrices by considering the spatial-temporal properties of traffic data and the set missing patterns and missing rates. Then iteratively trains a GAN was composed of two fully connected neural networks based on a game idea. By introducing a novel dynamic adaptive mechanism, this study can automatically identify the optimal number of iterations of the generator and discriminator during the model computation, and finally generates the complete traffic data matrix and repair the missing values. California PeMS and Guangzhou traffic speed datasets are used to complete the D-GAN model construction, and multiple evaluation metrics are employed to assess the repair performance of D-GAN. Experimental results show that the repair accuracy of D-GAN is higher for random missing patterns compared with non-random missing patterns; and the repair accuracy of D-GAN degrades accelerated with increasing missing rates. However, the repair performance of D-GAN outperforms the baseline models (e.g., BGCP, prophet-RF, and GAIN) under various missing conditions.

Key words: intelligent transportation systems, traffic data recovery, generative adversarial network, game idea, dynamic adaptive mechanism