Traffic Flow Modeling

Traffic Flow Modeling uses mathematical and computational methods to simulate how vehicles move through transportation networks, predicting speeds, densities, and volumes on road segments under varying conditions. Models range from macroscopic approaches that treat traffic as a continuous fluid to microscopic simulations that track individual vehicle decisions including lane changes, gap acceptance, and car-following behavior. Fundamental relationships in traffic flow theory connect speed, density, and volume. As density increases, speed decreases until capacity is reached and congestion forms. Simulation platforms like VISSIM, SUMO, and TransModeler apply these principles within GISGISGeographic Information Systems (GIS) enable users to analyze and visualize spatial data to uncover patterns, relation...-based network representations to evaluate proposed road designs, signal timing plans, and demand management strategies before implementation. Applications include corridor planning, interchange design evaluation, work zone traffic management, and event traffic planning. Transportation agencies use flow models to forecast future congestion under projected growth scenarios. Researchers apply models to evaluate the impacts of connected and autonomous vehicles on network performance. Real-time traffic flow estimation from sensor and probe vehicle data supports adaptive traffic management systems.