Analysis of driver memory with V2X-enabled weighted speed in car-following traffic flow model

In connected vehicle environments, drivers no longer rely solely on their own perception of the vehicle ahead, Vehicle-to-Everything (V2X) communication now allows them to access real-time weighted speed information from surrounding traffic, while their car-following decisions are still influenced by recollection of past driving states, referred to here as driver memory. This study explores how these two factors, cognitive memory and V2X-based weighted speed awareness, jointly govern car-following dynamics by extending the classical Full Velocity Difference (FVD) model to include both effects explicitly. Through linear stability analysis, the stability criterion of the proposed model is derived and the results show that the combined inclusion of memory and weighted speed information enlarges the stable traffic flow region well beyond what is achieved by the conventional FVD model. Near the neutral stability boundary, a nonlinear analysis based on the reductive perturbation approach is used to derive the modified Korteweg–de Vries (mKdV) equation, whose kink–antikink soliton solution captures the density wave behavior that develops close to this critical threshold. The energy consumption is calculated to check how much memory and V2X information affect traffic disturbances. The simulation results agree with the analytical results, confirming that the proposed model exhibits markedly reduced stop-and-go oscillations and lower energy dissipation than earlier models.