Construction of a Driving Route Inference Model Integrating Road Network Topology and Traffic Dynamics

The deployment volume of urban surveillance cameras has reached hundreds of thousands or even millions with the advancement of intelligent transportation systems (ITSs), indicating an enormous scale. However, the number of small-field-of-view surveillance cameras in large-scale traffic areas is insufficient to achieve full coverage of urban traffic zones. In the fields of ITSs, this study proposes a traffic information-based driving route inference method to clarify target vehicles’ paths in zones with monitoring blind spots and enhance the collaborative capability between surveillance cameras and traffic networks. First, this study maps traffic roads containing monitoring blind spots and their topologies into Bayesian network (BN) structures. The influencing factors of the target vehicle path can be analyzed, extracted, and quantified by the known data in a traffic network. A weight analysis method is utilized to estimate the weight coefficients of the influencing factors on the basis of the traditional BN model, thereby realizing the driving routes based on traffic networks. This study conducted experiments in Xinbei District, Changzhou City, and Jiangsu Province, China. Experimental results verify that the proposed method can accurately infer and reconstruct driving routes with monitoring blind zones. This method can provide theoretical support for analyzing driving directions at complex traffic intersections and enabling driving route inference in traffic network areas with monitoring blind spots.