Construction of intelligent traffic information recommendation system based on long short-term memory

Abstract

Traffic information service can improve road utilization and reduce traffic congestion and accidents. In this paper, we design the intelligent traffic information recommendation system based on deep learning. The recommendation system first preprocesses the traffic flow data through Internet of Things (IoT) technology, and then it uses the deep learning network to predict traffic parameters. The traffic congestion duration and spatial diffusion evolution trend are predicted respectively based on long short-term memory (LSTM), which is a typical time-recurrent neural network of deep learning. To the best of our knowledge, it is the first time to construct the intelligent traffic information recommendation system to improve the practicality of traffic information service. The experimental results show that the proposed recommendation system can expand the time horizon of traffic congestion prediction and further improve the reliability and predictability of decision-making basis for traffic managers and travelers.