Dynamic Learning and Human Interactions under the Extended Belief-Desire-Intention Framework for Transportation Systems

Abstract

In recent years, multi-agent traffic simulation has been widely used to accurately evaluate the performance of a road network considering individual and dynamic movements of vehicles under a virtual roadway environment. Given initial traffic demands and road conditions, the simulation is executed with multiple iterations and provides users with converged roadway conditions for the performance evaluation. For an accurate traffic simulation model, the driver's learning behavior is one of the major components to be concerned, as it affects road conditions (e.g., traffic flows) at each iteration as well as performance (e.g., accuracy and computational efficiency) of the traffic simulation. The goal of this study is to propose a realistic learning behavior model of drivers concerning their uncertain perception and interactions with other drivers. The proposed learning model is based on the Extended Belief-Desire-Intention (E-BDI) framework and two major decisions arising in the field of transportation (i.e., route planning and decision-making at an intersection). More specifically, the learning behavior is modeled via a dynamic evolution of a Bayesian network (BN) structure. The proposed dynamic learning approach considers three underlying assumptions: 1) the limited memory of a driver, 2) learning with incomplete observations on the road conditions, and 3) non-stationary road conditions. Thus, the dynamic learning approach allows driver agents to understand real-time road conditions and estimate future road conditions based on their past knowledge. In addition, interaction behaviors are also incorporated in the E-BDI framework to address influences of interactions on the driver's learning behavior. In this dissertation work, five major human interactions adopted from a social science literature are considered: 1) accommodation, 2) collaboration, 3) compromise, 4) avoidance, and 5) competition. The first three interaction types help to mimic information exchange behaviors between drivers (e.g., finding a route using a navigation system) while the last two interaction types are relevant with behaviors involving non-information exchange behaviors (e.g., finding a route based on a driver's own experiences). To calibrate the proposed learning behavior model and evaluate its performance in terms of inference accuracy and computational efficiency, drivers' decision data at intersections are collected via a human-in-the-loop experiment involving a driving simulator. Moreover, the proposed model is used to test and demonstrate the impact of five interactions on drivers' learning behavior under an en route planning scenario with real traffic data of Albany, New York, and Phoenix, Arizona. In this dissertation work, two major traffic simulation platforms, AnyLogic® and DynusT®, are used for the demonstration purposes. The experimental results reveal that the proposed model is effective in modeling realistic learning behaviors of drivers in conduction with interactions with other drivers.