Incorporating stochastic road capacity into day-to-day traffic simulation and traveler learning framework: Model development and case study

A key foundation for developing strategies aimed at improving the efficiency and reliability of an urban transportation network is identifying the locations and impact of system bottlenecks. Although free-flow capacity and queue discharge rates at system bottlenecks have traditionally been modeled as fixed values, they are in fact random variables. Therefore, assessing the operational impact of network bottlenecks requires reliable and realistic tools that account for stochasticity in prebreakdown flow rates and queue discharge rates. Focusing on methodological and analytic enhancements to existing dynamic traffic assignment models, this paper presents a method to seamlessly incorporate stochastic capacity models at freeway bottlenecks and signalized intersections and develops adaptive day-to-day traveler learning and route choice behavioral models under the travel time variability introduced by random capacity variations. To account for different levels of information availability and cognitive limitations of individual travelers, a set of bounded rationality rules are adapted to describe route choice rules for a traffic system with inherent process noise and different information provision strategies. A case study based on a real-world Portland, Oregon, subarea network is presented to illustrate the capabilities of the enhanced simulator and highlight the advantage of modeling stochastic capacity in a dynamic mesoscopic traffic simulator as compared with conventional tools that assume deterministic road capacity.