Traffic Condition Analysis Based on Users Emotion Tendency of Microblog

Analysis of traffic condition is of great significance to urban planning and public administration. However, traditional traffic condition analysis approaches mainly rely on sensors, which are high-cost and limit their coverage. To solve these problems, we propose a semi-supervised learning method which uses the social network data instead and analyzes the traffic condition based on user’s emotion tendency. First we train the Gated Recurrent Unit (GRU) model to estimate the sentiment of microblog with traffic information, then using the emotional tendency to predict whether traffic jams happen or not. In order to reduce the data annotated by manpower, we propose a new idea to employ the Conditional Generative Adversarial Networks (CGAN) to generate samples which are as a supplement to the training set of GRU. Finally compared with the GRU model trained by solely the manual annotation data, our method improves the classification accuracy by 4.07%. We also use our model to predict the time and roads of traffic jams in 4 Chinese cities which is proved to be effective.