Text-to-Traffic Generative Adversarial Network for Traffic Situation Generation

Traffic situation generation is of importance in the intelligent transportation field, evaluating and simulating the macroscopic traffic conditions. The government often uses the historical traffic data on the same weekday to analyze the future traffic situations, which works unfavorably due to some traffic-related information deficiency, such as weather, location, traffic accidents, social activities and so on. Therefore, how to accurately generate the traffic situation is a challenging problem. Fortunately, massive traffic-related information spread in social media often indicates the traffic situation variation trend, which provides the sufficient information for the traffic situation generation. In this paper, we propose a novel Text-to-Traffic generative adversarial network framework ( $\text{T}^{2}$ GAN), which fuses the traffic data and the semantic information collected from social media to generate the traffic situation. To reduce the huge gap between the above two modalities and improve the authenticity of the generated traffic situation, we raise a global-local loss. Additionally, we build a heterogeneous dataset containing the traffic-related text data collected from social media and the corresponding traffic passenger flow data. Experimental results show that the proposed methods are obviously better than many outstanding traffic situation generation methods based on neural networks.