Drivers’ Learning and Network Behavior: Dynamic Analysis of the Driver-Network System as a Complex System

Abstract

A model system of drivers’ cognition, learning, and route choice is formulated, taking into account the limitations in drivers’ cognitive capabilities, and is applied to examine the dynamic nature of a driver-network system through microsimulation. Network equilibrium is not assumed a priori; rather, finding how an equilibrium is reached, or not reached, is the objective. Although equilibrium analysis, in general, focuses on unique and static equilibrium by treating drivers’ behavior as simply as possible, drivers’ behavior is treated more realistically to enhance understanding of the day-to-day dynamics of the driver-network system. Results of microsimulation analyses indicate that the network flow does not necessarily converge to user equilibrium; instead, it may reach “deluded equilibrium,” which is caused by drivers’ false perceptions of travel times, and have “path dependence.” Results, especially the complex behavior such as path dependence shown in the simulation, indicate that the driver-network system is a complex system.