Deep Collaborative Intelligence-Driven Traffic Forecasting in Green Internet of Vehicles

Abstract

Accompanied with the development of green wireless communication, the green Internet of Vehicles (GIoV) has been a latent solution for future transportation. Among them, intelligent traffic forecasting for key nodes in GIoV is a significant research topic. Much research had been devoted to this issue, and graph learning-based approaches seemed to be a promising solution. However, existing research works concentrated more on graph-structured features in GIoV yet neglected global reliability. To deal with such issue, this work combines both deep embedding and graph embedding together and proposes a deep collaborative intelligence-driven traffic forecasting model in GIoV. By establishing more reliable feature spaces for traffic flow prediction, forecasting efficiency is expected to be promoted. Specifically, deep embedding is utilized to generate more abstract representation for basic features of road networks, and graph embedding is employed to update feature representation for different timestamps. Their collaboration contributes to considerable reliability. In addition, experiments are also conducted on a real-world dataset, and the results indicate that forecasting deviation receives about 15%-25% reduction.