Traffic Flow Simulation: Dual-Lane Model

This article introduces a dual-lane application for traffic flow simulation, designed to enhance understanding of this technology through learning and continuous development. In traffic flow simulation, the dual-lane model can simulate vehicle movement on roadways, revealing the causes of traffic congestion and potential solutions. The simulation allows parameter adjustments including vehicle density, speed limits, and vehicle types to observe corresponding traffic flow variations. The implementation typically involves object-oriented programming where Vehicle classes contain attributes like speed, position, and lane-changing behavior, while Road classes manage lane configurations and traffic rules. In dual-lane simulations, different traffic signal configurations can be implemented using state machines or timer-based controllers to simulate traffic flow under various conditions. The core algorithm often employs cellular automata or car-following models (e.g., Nagel-Schreckenberg model) to calculate vehicle movement and lane-changing decisions based on gap acceptance theory. The simulation framework may include visualization components using graphical libraries like matplotlib or pygame to display real-time traffic dynamics. Through these applications, we can better understand traffic flow simulation concepts and implementations, providing valuable references for future traffic planning and design. The code structure typically separates core simulation logic from visualization modules, allowing easy extension with new vehicle behaviors or traffic management strategies. Key functions include update_vehicle_positions(), handle_lane_changes(), and manage_traffic_signals(), which form the backbone of the simulation cycle.