Stacked LSTM deep learning model for traffic prediction in vehicle-to-vehicle communication

Vehicle-to-Vehicle (V2V) communication becomes an emerging topic because of its capability to provide efficient solution which guarantees more pleasant driving environment and eliminates the possibility of traffic accidents. However, the limitation of resource in V2V communication determines that a dynamic resource allocation strategy must be implemented to provide a balanced communication resource usage. Instead of focusing on the small topology of vehicular wireless communication, we look at a bigger picture of the scenery to deal with the challenge of limitation of resource in V2V communication. In this paper, we propose a long short-term memory (LSTM) based regression model to predict 24-hour traffic counts data. The main steps of our work are as follow: First, we collect 24-hour traffic counts data online and label those data. Second, we construct a stacked LSTM model to implement regression. Third, compared with the performance of logistic regression, the efficiency of our regression model is found out. Finally, we analyze the potential resource allocation patterns according to the regression results. Index Terms - Vehicle-to-Vehicle Communication, Long Short-Term Memory, Artificial Neural Networks.