Data Interpolation of Traffic Flow Algorithm Using Wavelet Transform for Traffic Generative Modeling

The real-time data acquisition is an important foundation for Parallel Transportation Systems (PTSs). In practical application, due to the sensor or communication faults, it leads to traffic flow data missing, which will affect the computational experiments and decision-making of the parallel transportation system. Inspired by the idea of parallel intelligence, in this work, we use discrete wavelet transform (DWT) to decompose the complete traffic flow data into low-frequency data and high-frequency data. These decomposed sequences are used as training data for Generative Adversarial Network (GAN) to generate two sequences with different frequencies. A Denoising Autoencoder (DAE) is introduced to interpolate the missing traffic flow data. And it is trained by the dataset through combining the real-time traffic flow data and the generated data. The computational experiments are carried out by using the PeMS dataset. The experimental results show that our algorithm can generate more realistic traffic flow data and improve the performance of data interpolation for missing traffic flow data.