To address the challenges of the single variable time headway (VTH) model in simultaneously meeting the requirements of constant-speed cruising and variable-speed car-following over long-distance, this study proposes a continuous and synthesized variable time headway (CSVTH) model based on an improved particle swarm optimization (PSO) algorithm. Firstlg, a CSVTH model is designed to integrate the advantages of three single VTH models for calculating safe time headway for car-following. By employing transition functions to enable smooth switching between different VTH models, the proposed approach achieves multi-scenario VTH computation and derives reasonable following distances. Second, the particle swarm optimization algorithm is enhanced through adaptive inertia weight and learning weight adjustments, which are then applied to optimize the slope parameters of the CSVTH model. Finally, a Simulink-Carsim co-simulation environment is constructed for car-following scenarios. Simulation results demonstrate that, compared to the non-optimized CSVTH model, the optimized CSVTH model reduces fuel consumption by 4.4%, improves ride comfort by 6.4%, and decreases following error by 5.3% across three car-following scenarios.