A multilevel information fusion approach for road congestion detection in VANETs

As city road congestion problems become more serious, many researchers have started to use the technique of vehicle ad hoc networks (VANETs) for road congestion detection. However, various on-board sensors equipped in vehicles may generate lots of atomic messages, which usually cause serious channel competition problems. In this paper, we propose a multilevel information fusion approach by combining the fuzzy clustering-based feature level information fusion (FCMA) and the modified Dempster–Shafer evidence reasoning-based decision level information fusion (D-SEMA). The FCMA can extract the key features from atomic messages, thereby greatly reducing the network traffic load. Furthermore, the D-SEMA mechanism is used to judge whether the road congestion event occurs. Performance analysis and simulation results under ONE simulator show that the proposed multilevel information fusion approach can detect road congestion efficiently with low bandwidth consumption.