Track: Modeling and Simulation
Abstract
A major issue in urban settings is the heavy traffic and vehicle congestion due to poor traffic control or overpopulation. Nevertheless, this issue emphasizes the significance of proper traffic flow control and prediction to improve traffic management. This study provides a systematic literature review of novel twenty (20) papers that used the deep learning approach to solve the complex traffic management problem. The several studies gathered showed that long short-term memory (LSTM) models are effective for urban traffic flow prediction. The related studies shows that additional datasets, and a hybrid model, lead to better prediction accuracies. Based from the result of systematic literature, after criteria was se, this study proposed an AT-Conv-LSTM model that uses environmental data and data from social media.