Abstract: In view of the increasingly complex structure of the road network and the continuous increase in traffic flow in the modern urban traffic environment,the trajectory of vehicles in a large transportation network has become extremely complex.The existing traffic data collection and analysis methods face challenges in deducing the complete driving trajectory of vehicles.In order to highly restore the real driving trajectory of vehicles and improve the degree of coordination of various traffic data,this study proposes a target vehicle trajectory deduction method based on weighted Bayesian network.Specifically,the topology of the road traffic network is directly mapped to the Bayesian network architecture,the influencing factors affecting driving decisions are extracted and quantified,and the weight analysis method is used to determine the weight coefficients of the influencing factors,so as to construct a weighted Bayesian network model for vehicle trajectory deduction.Taking the campus road network as the sample area for experiments,the proposed method can more accurately deduce and reproduce the complete driving trajectory of the target vehicle in a wide range of traffic network,which provides strong theoretical support for solving the problems of intelligent transportation construction,route planning and vehicle tracking.

Key words: Bayesian network, quantification of impact factors, multifactor weighting, transportation network, trajectory derivation