Virtual traffic simulation with neural network learned mobility model

Virtual traffic simulation plays an important role in easing traffic congestion and reducing traffic pollution. As the transportation network expands, the former rule-based mobility models showed several limitations in producing convincing virtual vehicles. A more realistic model with example-based method is in demand. In this paper, a neural network is employed with carefully selected traffic trajectory data. The virtual vehicle production is driven by the proposed mobility model and organized by a specified structure. Then, the virtual traffic simulation could be given for an indicated scenario.