Development and Evaluation of a Knowledge-Based System for Traffic Congestion Management and Control
This study investigated the use of a real-time knowledge-based decision support tool (Traffic Control Manager) to assist traffic operations center (TOC) personnel with selection of alternative traffic control plans after the occurrence of nonrecurring congestion. The ability of Traffic Control Manager to reduce delay during nonrecurring congestion was tested using the Dynasmart simulation model (Dynamic Network Assignment Simulation Model for Advanced Road Telematics) to measure average and total network travel time and stop time for scenarios "with" and "without" Traffic Control Manager.
The transportation sub-network input into the Dynasmart simulation model represented the Disney Land area of Anaheim, California. Actual traffic count data was input into the model to represent baseline saturation levels corresponding to high, medium, and low attendance levels at Disney Land. Each simulation had a two hour time horizon, and a total of 10 scenarios were run "with" and "without" the decision support tool. Scenarios included variations in attendance level, incident location (no incident, freeway, arterial), incident duration (10, 30, 60 minutes), capacity reduction (30 to 80 percent), and the changeable message sign (CMS) compliance rate (50 to 100 percent).
Simulations indicated the traffic diversion strategies and intersection signal control timing plan combinations chosen by the decision support tool reduced average travel time 1.9 to 29 percent, and reduced stop time 14.8 to 55.9 percent compared to scenarios without the tool.
Traffic Control Manager was less effective during high and low levels of saturation. Demand/capacity was harder to balance when the network was closer to saturation, and had limited effects when network demand was very small. Traffic Control Manager was most effective during medium levels of saturation.