Simulation of Advanced Vehicle Car-Following Models

In Internet of Vehicles (IoV) research, advanced vehicle car-following model simulation and spatiotemporal diagram visualization are critically important. For simulation, researchers typically implement algorithms like the Intelligent Driver Model (IDM) or Optimal Velocity Model (OVM) through programming languages such as Python or MATLAB. These models mathematically represent vehicle dynamics by calculating acceleration based on relative speed, distance to preceding vehicles, and driver reaction parameters. The simulation code generates trajectory data that captures following relationships, velocity fluctuations, and inter-vehicle spacing patterns. Concurrently, spatiotemporal diagrams are programmatically created using visualization libraries (e.g., Matplotlib or Plotly) to plot vehicle positions against time axes. These diagrams enable analytical insights into movement patterns across road segments, helping researchers identify congestion causes through metrics like traffic wave propagation and bottleneck detection. Thus, car-following simulations coupled with spatiotemporal visualizations serve as indispensable computational tools for developing data-driven traffic management solutions in IoV systems.