Unified route choice framework and empirical study in urban traffic control environment

Abstract

Route choice systems (RCS) and traffic control systems (TCS) constitute two major approaches to mitigating congestion in urban road networks. The interaction between signal control and route choice is often captured in the combined traffic assignment and control problem, whose solution has many practical applications. In this paper, the authors consider this interaction from a narrower RCS perspective, and focus on distributed route choice models for operational rather than planning purposes. The authors' goal is to analyze the relative performance of alternative route choice models as different assumptions are made about the type of TCS in use in the urban road network. To this end, the authors define a unified agent-based framework for formulating different route choice models, and integrate this framework with a microscopic traffic simulation environment. Within this 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 decision-making process that is a function of the agent’s updated current memory. Several previously developed route choice models from the literature are implemented using 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 TCSs: (1) a 32-intersection road network in Pittsburgh, PA running a fixed, SYNCHRO-generated coordinated signal control plan, and (2) the same road network running with the SURTRAC adaptive TCS. The results show that specific route choice models perform differentially when applied in conventional and adaptive traffic signal control settings, and that better overall network performance for all route choice models is achieved in the adaptive signal control setting. The authors' unified framework also makes it possible to analyze the performance impact of route choice model components, and to formulate better performing hybrid models.