RITA | ITS | Benefits: Simulation study predicts use of automated decision support tools can decrease travel times by up to 29%.

Simulation study predicts use of automated decision support tools can decrease travel times by up to 29%.

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 non-recurring 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 Disneyland area of Anaheim, California. Actual traffic count data were entered into the model to represent baseline saturation levels corresponding to high, medium, and low attendance levels at the amusement park. 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 (high, medium, and low), incident location (no incident, freeway, arterial), incident duration (10, 30, 60 minutes), capacity reduction (30-80%), and the changeable message sign compliance rate (50-100%).

The simulation results indicated the traffic diversion strategies and intersection signal control timing plan combinations chosen by the decision support tool reduced average travel time 2% to 29%, and reduced the average stop time 15% to 56%, compared to scenarios without the tool. Traffic Control Manager was most effective during medium levels of traffic demand where improvements in average travel time ranged from 17% to 29%, and improvements in average stop time ranged from 41% to 56%. The smallest improvement was seen during the highest and lowest levels of demand. The closer the network was to saturation, the smaller the spare capacity available, and the harder it was to find measures to balance the demand and capacity. During scenarios with the lowest levels of demand, the adverse effects of congestion were limited and the improvements offered by the decision support tool were marginal.

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