STrans-GAN: Spatially-Transferable Generative Adversarial Networks for Urban Traffic Estimation

Conditional traffic estimation is a vital problem in urban plan deployment, which can help evaluate urban construction plans and improve transportation efficiency. Conventional methods for conditional traffic estimation usually focus on supervised settings, which require a large amount of labeled training data. However, in many urban planning applications, the large amount of traffic data in a new city can be hard or impossible to acquire. To tackle the conditional traffic estimation problem in data scarcity situations, we formulate the problem as a spatial transfer generative learning problem. Compared to prior spatial transfer learning frameworks with only single source city, we propose to extracts knowledge from multiple source cities to improve the estimation accuracy and transfer stability, which is a technically more challenging task. As a solution, we propose a new cross-city conditional traffic estimation method — Spatially-Transferable Generative Adversarial Networks (STrans-GAN) with novel pre-training and fine-tuning algorithms. STransGAN preserves diverse traffic patterns from multiple source cities through traffic clustering, and incorporates meta-learning idea into the pre-training process to learn a well-generalized model. During fine-tuning, we propose to add a cluster matching regularizer to realize the flexible adaptation in different scenarios. Through extensive experiments on multiple-city datasets, the effectiveness of STrans-GAN is proved.