Improved FVD Model for Signalized Intersection Vehicle Lane-Changing

To objectively describe the lane-changing behavior of the mixed flow of cars and buses in the inlet lane of urban signal intersections, an improved full velocity difference model (FVD) model was constructed by introducing a FVD car-following model in the specific process of vehicle lane-changing, considering the effects of vehicle type and driver attributes, defining the vehicle safety spacing as a function of velocity, and classifying the lane-changing behavior into four specific forms. Afterward, the lane-changing video clips taken at the approach road at the intersection were analyzed in tracker software to obtain data, and a matlab elite genetic algorithm was applied to calibrate the model parameters. The results show that, compared with the traditional FVD lane change model, the improved model is significantly improved and more consistent with the lane-changing behavior of heterogeneous traffic in the intersection inlet lane, while it provides a theoretical basis for modeling microscopic traffic flow.