Traffic Adaptive Control for Oversaturated Isolated Intersections: Model Development and Simulation Testing

Abstract

Traffic adaptive control for oversaturated intersections (TACOS) is a hybrid optimization and rule-based strategy that addresses weaknesses of existing adaptive control strategies by having attributes such as: (1) the information used for decision-making is not dependent on forecasts; (2) intersection utilization is used explicitly in the objective function; (3) phase sequencing is optimized; and (4) operational anomalies are detected and responded to. A new intersection simulator that emulates NETSIM, INTEGRATION, and TACOS was developed to compare TACOS with pretimed and actuated control strategies. Demand scenarios consisted of light to heavy flow, cyclical arrivals, and arrivals with an upstream incident. Throughput-to-demand ratio, speed, delay, queue time, and percent stops were examined. Traffic adaptive control for oversaturated intersections showed significant improvements over pretimed and actuated control in terms of all examined measures of effectiveness under all flow scenarios. Demanding parameter specifications required in other adaptive control strategies are unnecessary in TACOS.