Online Traffic Condition Evaluation Method for Connected Vehicles Based on Multisource Data Fusion

With the development of connected vehicle (cv) and vehicle to x (v2x) communication,more traffic data is being collected from the road network. in order to predict future traffic condition from connected vehicles' data in real-time,we present an online traffic condition evaluation model utilizing v2x communication. this model employs the analytic hierarchy process (ahp) and the multilevel fuzzy set theory to fuse multiple sources of information for prediction. first,the contemporary vehicle data from the on board diagnostic (obd) is fused with the static road data in the road side unit (rsu). then,the real-time traffic evaluation scores are calculated using the variable membership model. the real data collected by obu in field test demonstrates the feasibility of the evaluation model. compared with traditional evaluation systems,the proposed model can handle more types of data but demands less data transfer. © 2017 pang-wei wang et al.