Traffic Diversion Effect of Ramp Metering at Individual and System Levels

Abstract

Understanding traffic diversion due to control actions is essential to the success of advanced traffic management systems such as ramp metering, arterial signal control, and traveler information services. This paper explores the systemwide traffic diversion effect of a well-documented control algorithm on a large real-world network. By using an unconventional positive approach that models how travelers actually learn the road network experientially and make heuristic route search and choice decisions in the presence of information and decision costs, this research is able to explore traffic diversion as an emergent process, with travelers’ decision-making processes individually traced. The supply-side effect of ramp metering is modeled as capacity changes at freeway bottlenecks and metered entrance ramps. Results show that only 1% of all peak-hour travelers (6,200) on the Twin Cities (Minneapolis–St. Paul), Minnesota, network change routes after meters are deployed, even though a much larger number of them (14%) are significantly hurt by the algorithm. It is also evident that ramp meters can benefit and hurt all types of roads in a large network, including freeways, local streets, and ramps. An important conclusion with practical significance is that different behavioral assumptions could produce opposing policy recommendations in traffic operations. For instance, models with normative assumptions (e.g., DUE) lead to systematic benefit overestimation because of a particular microbehavioral mechanism.

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