Abstract
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.
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Acknowledgement
This work is supported by the Nature Science Foundation of China (No. 61402386, No. 61305061, No. 61502105, No. 61572409, No. 81230087 and No. 61571188), Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No. MJUKF201743), and Education and scientific research projects of young and middle-aged teachers in Fujian Province under Grand No. JA15075. Fujian Province 2011 Collaborative Innovation Center of TCM Health Management and Collaborative Innovation Center of Chinese Oolong Tea Industry—Collaborative Innovation Center (2011) of Fujian Province.
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Wang, S., Cao, D., Lin, D., Chao, F. (2018). Traffic Condition Analysis Based on Users Emotion Tendency of Microblog. In: Chao, F., Schockaert, S., Zhang, Q. (eds) Advances in Computational Intelligence Systems. UKCI 2017. Advances in Intelligent Systems and Computing, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-66939-7_26
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DOI: https://doi.org/10.1007/978-3-319-66939-7_26
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