This study proposes a novel lattice hydrodynamic model to analyze the relationship between electronic throttle control (ETC) and psychological density behavior in traffic flow. The model integrates ETC with psychological headway to examine how these factors influence traffic dynamics. The study finds that the ETC coefficient enhances the stability of traffic flow for both low and high psychological headway values. At low density, drivers have better visibility and can adjust their speed and position more easily, leading to greater stability. A higher psychological density allows drivers to adjust their speed and position without disrupting traffic, reducing the likelihood of sudden braking or erratic maneuvers. The study also notes that psychological density helps reduce traffic congestion regardless of ETC rate. The findings suggest that optimizing ETC algorithms to account for both vehicle dynamics and driver psychology can improve traffic management and safety. The paper introduces a lattice hydrodynamic model that incorporates driver psychology and ETC to better understand traffic flow dynamics. The model is analyzed using linear and nonlinear methods, leading to the derivation of the modified Korteweg-de Vries equation. Numerical simulations confirm the model's effectiveness in capturing traffic flow behavior. The study highlights the importance of integrating psychological factors and ETC in traffic flow modeling to enhance traffic management and safety. The proposed model provides a framework for understanding how ETC and psychological density interact to influence traffic flow and congestion. The results suggest that optimizing ETC algorithms based on psychological factors can lead to more efficient and safer traffic systems.This study proposes a novel lattice hydrodynamic model to analyze the relationship between electronic throttle control (ETC) and psychological density behavior in traffic flow. The model integrates ETC with psychological headway to examine how these factors influence traffic dynamics. The study finds that the ETC coefficient enhances the stability of traffic flow for both low and high psychological headway values. At low density, drivers have better visibility and can adjust their speed and position more easily, leading to greater stability. A higher psychological density allows drivers to adjust their speed and position without disrupting traffic, reducing the likelihood of sudden braking or erratic maneuvers. The study also notes that psychological density helps reduce traffic congestion regardless of ETC rate. The findings suggest that optimizing ETC algorithms to account for both vehicle dynamics and driver psychology can improve traffic management and safety. The paper introduces a lattice hydrodynamic model that incorporates driver psychology and ETC to better understand traffic flow dynamics. The model is analyzed using linear and nonlinear methods, leading to the derivation of the modified Korteweg-de Vries equation. Numerical simulations confirm the model's effectiveness in capturing traffic flow behavior. The study highlights the importance of integrating psychological factors and ETC in traffic flow modeling to enhance traffic management and safety. The proposed model provides a framework for understanding how ETC and psychological density interact to influence traffic flow and congestion. The results suggest that optimizing ETC algorithms based on psychological factors can lead to more efficient and safer traffic systems.