Newspaper article-based agent control in smart city simulations

Abstract

The latest research on smart city technologies mainly focuses on utilizing cities' resources to improve the quality of the lives of citizens. Diverse kinds of control signals from massive systems and devices such as adaptive traffic light systems in smart cities can be collected and utilized. Unfortunately, it is difficult to collect a massive dataset of control signals as doing so in the real-world requires significant effort and time. This paper proposes a deep generative model which integrates a long short-term memory model with generative adversarial network (LSTM-GAN) to generate agent control signals based on the words extracted from newspaper articles to solve the problem of collecting massive signals. The discriminatory network in the LSTM-GAN takes continuous word embedding vectors as inputs generated by a pre-trained Word2Vec model. The agent control signals of sequential actions are simultaneously predicted by the LSTM-GAN in real time. Specifically, to collect the training data of smart city simulations, the LSTM-GAN is trained based on the Corpus of Contemporary American English (COCA) newspaper dataset, which contains 5,317,731 sentences, for a total of 93,626,203 word tokens, from written texts. To verify the proposed method, agent control signals were generated and validated. In the training of the LSTM-GAN, the accuracy of the discriminator converged to 50%. In addition, the losses of the discriminator and the generator converged from 4527.04 and 4527.94 to 2.97 and 1.87, respectively.