Empowering C-V2X Through Advanced Joint Traffic Prediction in Urban Networks

Chaojin Mao, Liang Zhao, Zhi Liu, Geyong Min, Ammar Hawbani, Keping Yu

Abstract: Cellular vehicle-to-everything (C-V2X) can provide ubiquitous mobile computing and communication services for vehicles, acting as a key technology to realize future urban intelligent transportation systems (ITS). Due to the lack of long-term insight into complex and dynamic urban road states, the existing historical road information-based strategies for C-V2X applications are inadequate to satisfy their high-performance requirements. Fortunately, it is feasible to provide fine-grained future road states for C-V2X decision-making by predicting traffic states to address this issue. To this end, this article proposes a fine-grained joint traffic prediction method in the urban road network with high-spatial complexity (ROUTE). ROUTE uniquely forecasts both micro-level (individual vehicle states) and macro-level traffic, thus supporting the diverse requirements of C-V2X applications. ROUTE is comprised of three key parts, including a vehicle coordinate transformation model, a spatial interaction-based turning model, and a micro-traffic prediction model. First, the complex spatial topology of the urban regional road network is normalized in ROUTE using the coordinate transformation model. Second, the turning model calculates the next road that the vehicle chooses after leaving the current one. Third, a transformer and generative adversarial network-based model (FORMERGAN) predicts future micro-traffic states. Extensive experimental results demonstrate that ROUTE surpasses its competitors in accurately predicting fine-grained long-term micro-traffic and macro-traffic states.