Gating Memory Network Multilayer Perceptron for Traffic Forecasting in Internet of Vehicles Systems

With the increasing number of distributed edge intelligence (DEI) sensors in cities, Internet of Vehicles (IoV) systems can obtain more fine-grained information. The information plays a crucial role in tasks, such as road monitoring and traffic forecasting. However, graph-based methods used in IoV systems mainly have two limitations: i) They struggle to achieve both efficiency and high prediction performance, and ii) Most lack user-friendly interfaces to intuitively display data collected by DEI sensors and prediction results. To alleviate the first limitation, we propose a novel deep learning model, the gating memory network multilayer perceptron (GMMLP). In the model, the traditional time-consuming graph-based operations are replaced with multilayer perceptrons (MLPs). A gating mechanism is employed to help reduce redundant information from raw input. An integrated memory network is utilized to memorize common patterns implicitly. To alleviate the second limitation and make GMMLP more accessible, we design a DEI-based human-computer interaction and visualization system called the traffic easy access system (TEAS). Traffic departments and ordinary users can access roads in the state recorded by DEI sensors on multiple terminals. Professionals can easily train and test traffic forecasting models by using this system. We validate the performance of our proposed model on multiple datasets. The experimental results demonstrate that our model achieves excellent performance in both inference efficiency and prediction accuracy.