Day-to-day dynamics and system reliability in urban traffic networks
This study investigates day-to-day dynamics in an urban traffic network induced by internal and external factors under real-time information. A robust cost network optimization algorithm to account for the randomness of trip time is proposed and its application is demonstrated for the static traffic assignment problem. In addition, a simulation-based day-to-day network analysis framework is developed by using an agent-based approach to modeling user behavior under information. Unique features of this framework includes its day-to-day simulation capability, quantification of system performance in non-equilibrium states, capability to model and study the influence of system shocks, richer representation of user behavior, and a wide array of system performance measures that enable assessment of reliability and trip time jointly. Computational experiments are used to investigate the effect of the following experimental factors: user behavior rules, users’ responsiveness, demand reduction control measures, and unplanned supply shocks (incidents). Insights and models along these lines will have important implications for congestion mitigation, improvement of travel time reliability, and assessment of different travel demand management strategies.