The route choice system and the traffic control system (TCS) constitute two major approaches to mitigating congestion in urban road networks. The interaction between signal control and route choice is considered from a narrower route choice system perspective, with the focus on route choice models for operational purposes. The goal is to analyze the relative performance of alternative route choice models as different assumptions are made about the type of TCS in use. To this end, an agent-based framework for formulating different route choice models is defined, and this framework is integrated with a microscopic traffic simulation environment. Within the framework, each agent's memory is updated repeatedly (daily) to reflect available prior individual and social experience, and then a route is chosen by a probabilistic sequential decisionmaking process. Several previously developed route choice models from the literature are implemented with the framework, and their performance, along with some additional hybrid models that are suggested by the modeling framework, is evaluated on two simulated real-world systems: a 32-intersection road network in Pittsburgh, Pennsylvania, running with a SYNCHRO-gencrated coordinated timing plan and the same road network running with the scalable urban traffic control (SURTRAC) adaptive TCS. The results show that specific route choice models perform differentially when applied in conventional and adaptive traffic control settings and that better overall network performance for all route choice models is achieved in the adaptive control setting. This unified framework also makes it possible to analyze the performance impact of route choice model components and to formulate better-performing hybrid models.