Traffic Flow Synthesis Using Generative Adversarial Networks via Semantic Latent Codes Manipulation

Traffic data analysis and mining are elemental functions of Intelligent Transportation Systems. In recent year, tremendous sensors are deployed in order to collect big data, and equipment maintenance costs a lot. With the development of deep learning, especially especially Generative Adversarial Networks, we can generate realistic big artificial traffic flow data and use small real traffic data and synthesized traffic data in traffic data mining tasks. In this paper, we focus on discovering the semantics embedded in latent codes which are fed into Generative Adversarial Networks, and propose to use the interpolation of semantic latent code to generate semantic manipulation of traffic flow. We evaluate our approach using the publicly available data from Caltrans Performance Measurements Systems (PeMS), and experimental results show the the effectiveness of the proposed method.